The seasonality of foreign exchange and

interest rates in later medieval Europe,

c.1383-1411

Adrian R. Bell, Chris Brooks and Tony K. Moore

1

According to the mid-fifteenth-century merchant Giovanni di Antonio da Uzzano:

The good rule in making exchange should be as follows: beware not to find yourself

in debt in a certain territory at a time when money can be expected to be high, either

because of fairs, or because of ships sailing out, or because of large sales of

merchandise, or because of payments which may be due to soldiers, rulers,

communes, or the like, or because of anything of an ordinary or extraordinary nature

as a result of which you may hear that money is going to be withdrawn from banks

and changers, but you ought to find yourself well-supplied with money (translated in

Lopez and Raymond 2001, 420-1).

The first three factors mentioned by Uzzano all involved a seasonal element: fairs were held

at fixed times each year; the great Venetian galley fleets and Genoese carricks tended to set

sail according to a pre-determined schedule; while sales of merchandise often coincided with

the previous two or reflected the timing of domestic production (for example, the wool-

shearing season in England or Catalonia). Uzzano’s distinction between things of an ‘ordinary

or extraordinary nature’ suggests that there were some predictable or expected influences on

foreign exchange (FX) rates – but also that these were not the sole determining factors, and

there was the potential for unexpected developments to affect FX rates.

This paper will attempt to use a new dataset of medieval FX rates to test the extent of

seasonality in the medieval FX market between c.1383 and 1411. We will first explain the

key features of the medieval FX market and, in particular, how it was linked not just to the

financing of international trade but also to domestic credit markets, since a bill of exchange

included both a currency conversion and the extension of credit. The second section will set

out the sources of our data on medieval FX rates and our methodology for calculating interest

rates from these exchange rates. We will then test for whether there are identifiable seasonal

patterns in the FX and interest rate series. Any such patterns will be compared against the

descriptions by three near-contemporary practitioners and related to wider trade flows.

The FX market in medieval Europe

The FX market today is by far the largest financial market, with the Bank for International

Settlements estimating a daily turnover of $5.3 trillion in its most recent triennial survey (BIS

April 2013). This reflects the globalisation of trade and (overwhelmingly) international capital

flows, which make it necessary to conduct frequent conversions between the different

currencies of sovereign nation-states or currency areas. Although its size may be

unprecedented, the origins of the modern FX market can, in many ways, be traced back to

medieval Europe. The rise of kingdoms and principalities (many of which would form the

basis for today’s nation-states), each of which asserted a monopoly on minting coins within

its territory, combined with the ‘Commercial Revolution’ of the thirteenth century that led to

an increase in cross-border trade, created a similar need for FX transactions (Einzig 1970).

These were carried out on a local scale by money-changers and on an international level by

(mainly Italian) merchant societies.

The operation of the medieval FX system was set out by Raymond de Roover in a series of

seminal monographs and articles (de Roover 1944, 1957, 1966, 1968). Subsequently, Guilio

Mandich (1970) made some important adjustments to the de Roover thesis (the most

accessible discussion of these in English can be found in Mueller 1997). The classic FX

instrument was the bill of exchange, an informal holograph ‘order to pay’. In its fullest form,

the bill stated that the seller (usually known as the taker or drawer) of the bill had received a

2

sum of money in the local currency from the buyer (the giver or remitter) in place A. The

seller ordered his correspondent (the payer) in place B to pay the equivalent value in the

foreign currency, at a set exchange rate, to the buyer’s correspondent (the payee) at a

specified time.

The use of bills of exchange allowed merchants to minimise the cost and trouble of

transporting specie across Europe by netting off. As Peter Spufford (1988, 254-5) explains,

‘No longer did every prospective purchaser or returning vendor need to carry with him large

and stealable quantities of precious metals... instead, the static managers could send and

receive remittances from his factors and agents by bills of exchange’. Further, ‘the bill of

exchange enormously multiplied the supply of money available for international transfers

between these cities… a very large proportion of normal payments within this network of

cities was made by bill of exchange by the early fourteenth century’. It also allowed for the

development of sedentary merchants and merchant societies, which directed cross-European

trade and financial flows from their home cities, and also facilitated greater specialisation, as

in the case of the English staplers and mercers described below.

The vital role played by FX within the economy was recognised in medieval literature, with

Chaucer writing of his archetypal merchant, ‘wel coude he in eschaunge sheeldes selle’

(Chaucer, General prologue, line 278). It is most likely that the Merchant was a stapler whose

trade was based on exporting wool, the sale of which would have left him with funds in

Flanders (the sheelde or écu was a Flemish unit of account) that he needed to repatriate to

England (Goddard 2014, 182-4). He could have done this by buying goods for re-import to

England himself or he could buy a bill of exchange (‘selling shields’) from an English mercer

who specialised in the import trade, purchasing goods at the Flemish and Brabantine marts.

The symbiotic relationship between the English staplers and mercers (importers of cloth) in

the later fifteenth century was summarised by Eileen Power (1933, 67):

The Staplers [grocers] had Flemish money in Calais, where they sold, and in the

marts, where they collected their debts; they wanted English money in the Cotswolds

and London, where they bought. The mercers had English money in London, where

they sold, and needed Flemish money at the marts, where they bought. So the Stapler

[grocer] on the continent delivered his money to a mercer and received a bill of

exchange payable at a future date in London in English money.

In the above case, the FX market was matching English exporters with importers and thereby

reducing the need to transfer specie back and forth between England and the Low Countries.

Such netting-off of claims, however, could not directly resolve any underlying trade

imbalances between two countries. But bills of exchange could be they could be used in more

complicated ways to settle multi-lateral balances of payments. For example Bruges was at the

centre of the Florentines’ financial dealings in Northern Europe. The Italians generally had

credits in Bruges as a result of their trade surplus from the sale of spices and luxury goods

but, for this very reason, found it difficult to remit those funds back to Italy as there were few

Flemish merchants with spare credits in Florence to draw on or Florentine merchants that

needed to remit funds to Bruges (de Roover 1959). At the same time, the Florentines had

trade deficits with England and Catalonia for the wool (and grain-dye and saffron from the

latter) that they purchased for export back to Italy. Instead, they used their credits in Bruges to

provide funds to Catalan merchants or English mercers who wished to buy Flemish cloth at

the Brabantine marts; in return they received payment in Barcelona or London which they

could use to fund their purchases of raw materials (Watson 1962, 322-6). The Bruges money

market thus helped to equalize the trade and financial flows between Italy, England, Catalonia

and the Low Countries.

3

Alfonso Leone (1983) would stress that the primary role of the medieval FX market was to

allow capital to be efficiently mobilised to support international trade in the ways described

above. This position is supported by Spufford (1988) and Bolton and Guidi Bruscoli (2008).

An alternative interpretation, however, emphasises the role of FX transactions as credit

instruments.

The largest component of the modern FX market, around 38% in 2013, involves spot

transactions that are settled ‘on the spot’.1 In the international FX market during the Middle

Ages, however, there were effectively no spot transactions. This was an inevitable

consequence of the contemporary communications technology that made it impossible to

conduct a (near) instantaneous transfer of funds between two cities. Bills of exchange

between different financial centres had different maturities (known as usance), mostly

increasing with geographical distance. The shortest was between Florence and Pisa, where

bills were payable three days after they were presented for payment (plus one or two days

postal time). The longest was between London and the Italian cities, where bills matured three

months after they were issued. As a result, every bill of exchange necessarily involved the

extension of credit, since the buyer paid the seller upfront in local currency but only received

the value in foreign currency later.2

This meant that the seller of a bill of exchange was effectively a borrower, and the buyer a

lender. To account for the time value of money, either a discount or a premium was

incorporated into the exchange rates quoted at different financial centres. The operation of

this system was demonstrated by de Roover (1944) and has been traced back to the later

thirteenth century by Thomas Blomquist (1990, 362-8). It also meant that FX transactions

could also be used as a way of synthetically-creating loans. The use of bills of exchange as

pure credit instruments required not one transaction but two. The first took place at the current

exchange rate at the first place and the second ‘rechange’ transaction was carried out at the

exchange rate prevailing at the second place. The buyer/lender’s profit was determined by the

difference, or spread, in the exchange rates between the originating financial centre and the

destination centre (lagged by the usance period); the wider the spread, the greater the profit.

This profit can then be converted into an annualized interest rate taking into account the

usance period between the two places, as will be discussed in more detail below.

Importantly, the implicit interest rate could vary from transaction to transaction as the

exchange rates reacted to the particular conditions of trade and the money market at each

place. As a result, neither the lender nor the borrower in any specific exchange and rechange

transaction could know the exact interest rate in advance. Although the existence of a spread

between the exchange rates at the two financial centres meant that, on average and over the

long run, the buyer/lender would receive a positive return, this could vary and, indeed, the

buyer/lender could sometimes lose money on the transaction. The fact that there was no fixed

or certain profit was, it has been argued, one of the main reasons why bills of exchange were

not held to be usurious (Noonan 1957; de Roover 1967). Indeed, an important strand in the

historical and economic literature has argued that the primary significance of bills of

exchange was that they facilitated financial innovation by helping merchants to circumvent

the usury prohibition (Koyama 2010; Rubin 2010).

The use of FX transactions to conceal loans in this way was called ‘dry exchange’ (cambium

secco) because it did not water the flows of trade - there was never any intention to engage in

an actual FX transaction or to transfer funds between two places; money was advanced and

1 Technically, the standard settlement date is usually two days after the agreement.

2 In this way, the medieval bill of exchange differs from a modern FX forward or futures contract, in

which both payment and delivery take place at the same time.

4

repaid in the local currency. It could be carried out by physically sending a bill of exchange

that was then ‘protested’ by the seller’s correspondent and returned back to the home city (de

Roover 1966). A more efficient method was exchange ‘without letters’ (senza lettera). Here

the FX transactions were purely book-keeping entries based on reported market rates at the

second financial centre (Mandich 1970; Mueller 1997). This avoided some of the fixed costs

of sending and then protesting bills of exchange. Rather than ‘real’ FX transactions, these can

be seen as derivatives contracts on future exchange rates. Finally, there were allegations of

fictitious exchanges where the exchange rates were not based on market rates at all but rather

agreed in advance by the parties. Of course, by removing the element of uncertainty, this

would certainly have been considered usurious.

The FX market and the domestic credit market were thus inextricably bound up together. As

Peter Spufford (1988, 395) put it '[the] use, or misuse, of the system of bills of exchange as an

instrument of credit, depended on the existence of the system itself for genuine commercial

transactions'. Indeed, some FX transactions contained elements of both. In his study of the

Covoni account books, Mandich (1970) found numerous examples of what he termed

‘speculative exchange’. Here the Covoni in Florence bought bills of exchange drawn on

Venice – not to fund their trading activities in Venice but also without any prior agreement

with the seller to rechange that sum back to Florence. Instead, once that bill came due in

Venice, they relied on their correspondent there to find a second counterparty, one who

wanted money in Venice and could draw on funds in Florence. In effect, this operated in the

same way as the ‘dry exchange’ discussed above, except there was no pre-agreement to

protest the bill. Of course, this meant that there was no guarantee that the Covoni would find a

matching counterparty that wished to take the other end of the trade in Venice. In more

complicated cases, the merchants may then have used the funds received from the bill in

Venice to remit to a third market, such as Barcelona or Bruges, if that was believed to offer a

more favourable rate. In this way, such speculators in exchange rates may actually have

played a vital economic role by acting as counterparties for merchants wishing to transfer

funds for trading purposes – they may be seen as early ‘market makers’.

FX rates were therefore very sensitive to the wider condition of the money market. The FX

rate (price) can be seen as the result of the balance between buyers (supply) and sellers

(demand) of bills of exchange. The sixteenth-century merchant Bernardo Davanzati explained

this relationship using the analogy of a hand tightening or loosening its grip on money

(Mueller 1997, 305). If there were more would-be sellers than buyers (demand exceeded

supply), then the local currency appreciated and merchants described money as being ‘tight’

(strettazza) or ‘dear’ (carestia). If there were more would-be buyers than sellers (i.e. the

supply of money exceeded the demand), then money was said to be ‘loose’ (larghezza) or

‘abundant’ (dovizia) and the local currency depreciated.

This could be driven by demand for international transfers – either remittances to fund trading

activities in other cities or to repatriate money by drawing on credits in foreign cities. For

example, if more merchants wished to buy bills of exchange in order to send money abroad,

the local currency would depreciate as buyers would have to offer sellers more local currency

per unit of foreign currency. Conversely, if there were more merchants that wanted to receive

money locally by selling bills of exchange drawing on credits in another centre, then the local

currency would appreciate as they had to offer buyers more units of foreign currency per unit

of local currency. The same dynamic also applied to the domestic money market - at times of

high demand for cash or credit locally, Davanzati’s ‘hand’ would tighten and not release any

money except at a higher price (in terms of foreign currency). As a result, borrowers (i.e.

sellers of bills of exchange) would have to promise more foreign currency to receive one unit

of local currency and the reverse held if there was more demand from would-be lenders

(buyers) than borrowers.

5

The rhythms of international trade resulted in more or less regular seasonal flows of money

from one centre to another – with obvious implications for FX rates. There were also times of

greater or lesser demand for cash and credit at each banking centre, linked both to times of

heavy trading such as at fairs or when fleets arrived/departed but also to domestic production,

for example wool in England or Catalonia. Since short-term credit could be extended using

bills of exchange, this should also feed through into the FX rates. But can it be demonstrated

empirically?

Sources and methodology

Exchange rates

A merchant banker’s FX profits depended on his ability to predict market movements and to

time his trades. The merchant who had better and more up-to-date information about FX rates

in other cities therefore enjoyed a competitive advantage over his uninformed peers. For this

reason, when writing to their correspondents in foreign cities, merchants often listed the

current market FX rates at the end of their business letters. These rates were probably

collected from the bill brokers that arranged deals in each city (de Roover 1968, 29). They can

be seen as forerunners of the exchange rate currents printed from the sixteenth century

onwards (McCuster and Gravesteijn 1991), which ultimately developed into the modern

financial press.

This paper employs a new dataset of medieval FX rates hand-collected from the commercial

correspondence in the archive of Francesco di Marco Datini, the ‘merchant of Prato’. Datini

was active as an international merchant and banker from c.1383 to his death in 1411 (Origo

1957; Melis 1962; Nigro 2010). His main business interests concentrated on the western

Mediterranean, with an arc of branches stretching from Tuscany, Southern France and Spain,

but he was in touch with correspondents in Northern Europe (Bruges, London and Paris),

Eastern Italy (Milan, Venice) and Southern Italy (Gaeta, Naples, Rome) among other places.

Datini is still known today because many of the survival of many of his business records,

including 600 account books and 150,000 items of correspondence.3 These provide an

unparalleled source for medieval economic and financial history.

The medieval FX project collected the FX rates quoted at Florence, Genoa, London, Naples

and Paris in the original letters sent to Datini’s branches from those places. It also compiled

FX rates at Barcelona and Bruges as collected by Raymond de Roover (1968), at Gaeta by

Elena Cecchi Aste (1997), at Milan by Luciana Frangioni (1994) and at Venice by Reinhold

Mueller (1997). Note that we have extended the dataset for Bruges and corrected some of the

rates given for Venice. The final dataset contains 93,000 individual data points for 88

combinations of currency pairs at ten major financial centres over a nearly thirty-year period

between 1383 and 1411.4 However, this paper excludes the more fragmentary or short-lived

series and instead concentrates on 54 currency pairs at seven centres (Barcelona, Bruges,

Florence, Genoa, London, Paris and Venice) that are more suitable for quantitative analysis.

Details of these currency pairs are shown in the first four columns of Table 1.

These medieval business letters are not the easiest sources to use. Just like FX traders today,

the medieval merchants developed their own conventions and terminology to describe their

business operations. There were two main methods of quoting FX rates. In the first, rates were

quoted as ‘uncertain for certain’, that is as a variable number of units of currency A per fixed

unit of currency B. In the second, rates were quoted as a percentage better (meglio) or worse

3 Images of Datini’s correspondence can be viewed online at http://datini.archiviodistato.prato.it/

4 The dataset and further explanatory notes can be accessed at: http://apps.icmacentre.ac.uk/medievalfx/

6

(peggio) than par, reflecting the fact that most gold coins were based on a common model,

either the florin of Florence or the ducat of Venice, and so had a similar intrinsic metallic

content.

In our dataset, we first recorded all data as originally quoted and then converted it to a

standardised form for each currency pair, reflecting the most common method by which that

pair was quoted in the letters. As explained below, here we will work with the growth rates of

the FX rates so it was not necessarily to convert all currency pairs to a single format.

However, we do report all monthly changes in FX rates in terms of the local currency – for

example, Bruges gave ‘certain’ to Barcelona and London but ‘uncertain’ to Genoa, Paris and

Venice. This means that an increase in the latter set of rates reflects a weakening of the local

currency while an increase in the former means that it is appreciating. This may make it

slightly more difficult to compare different rates and so, to make interpretation of our results

as intuitive as possible, we have converted the latter rates to be expressed in the same way as

the former. In all of the tables and figures below, therefore, an increase in the FX rate is

equivalent to an appreciation of the local currency.

We have also cleaned the data in a small number of cases, notably where the FX rates

changed suddenly as a result of a devaluation or enhancement of one of the two currencies.

The monetary policies of the dukes of Burgundy, rulers of Bruges, are particularly notorious

(Munro 2012). However, for the period covered by most of our series, between the

devaluation of 1389 and the enhancement of 1409, the pond groot remained stable. The

Barcelona-Bruges and Genoa-Bruges series do include a sharp drop in March and November

1390 respectively as the devaluation of December 1389 took effect – we have excluded these

months from our analysis. Similarly, the Visconti dukes of Milan gradually devalued the

silver lira imperiali during the 1390s before restoring the quality of the coinage in February

1400, causing a fall in March of 22.9% and 22.2% in the Genoa-Milan and Venice-Milan

rates respectively (Bell et al. 2013, 118). These outliers have also been excluded from the

calculations.

Interest rates

In addition to the FX rates themselves, we also reconstruct the implicit interest rates from the

spreads between the rates quoted in different places. Several previous studies have sought to

calculate the profits received by merchants from particular exchange and rechange

transactions recorded as protested bills of exchange (de Roover 1957 and 1966) or in account

books (Mandich 1970; see others cited in Mueller 1997). Although these provide an

indication of the range of possible interest rates, the data is too scattered and infrequent to

permit any conclusions about possible seasonal patterns. However, as explained above, the

profits from FX trading were a product of differential exchange rates at each end of the

exchange and rechange transaction. Therefore, it is possible to reconstruct implicit or

potential interest rates even where we do not have actual transaction data, so long as we know

the market exchange rates prevailing at both places. Booth (2009) used this method to

calculate interest rates between Barcelona and Bruges. Applying a similar approach to our

expanded dataset allows us to expand the sample size dramatically to the extent that it is

feasible to investigate seasonal variations.

Our methodology involves a three stage process (described in more detail in Bell et al. 2015).

First, we identify all cases where we have a FX rate observation at place A and a

corresponding observation at place B lagged by the usance period from A to B. To enlarge the

dataset, where there were no exact matching observations, we extended the search to include

the closest match within one day before or after the exact target date. If we find a matching

entry one day before and one day after the target date, we take the average of the two

7

observations. We aim to minimise the extension to this parameters while ensuring that we

have a sufficient volume of material. For some of the more distant centres with longer usance

periods or less surviving data, however, we search for matches within up to seven days of the

target date.

Second, we calculate the spread between these two rates, Sp. When exchange rates are quoted

using the ‘certain’/’uncertain’ method, as in this case, the spread is calculated by deducting

the rate at the place that gave uncertain, ERUC

, from that at the place that gave certain, ERC.

As de Roover pointed out (repeatedly), the logic of the time value of money meant that the

exchange rate at the place that gave certain should generally be higher than the rate at the

place that gave uncertain. This nominal spread can then be converted into a percentage value

by dividing it by the rate at the place that gave uncertain. Note that this basic principle applies

regardless of the direction of the transaction. This is shown in equation (1).

(1)

Where the exchange rates are quoted as a percentage better or worse than par, the method is

slightly different. As explained above, our dataset converted all of these rates into the amount

of foreign currency received per 100 units of local currency. In effect, both places give

certain. The exchange rate at A thus represents the value received at place B in return for 100

units of currency A. This is then multiplied by the rechange rate (the exchange rate at B) to

calculate the sum received back at place A. Deducting the original 100 units that we started

with at A produces the nominal spread, and this can be converted into percentage format by

dividing by 100. This is shown in equation (2).

(2)

To facilitate comparison between different currency pairs, we convert the percentage spread

between the two rates – which represents the return from any one exchange and rechange

transaction – into an annualized rate, taking into account the usance periods. We calculate

both a simple annualized interest rate using equation (3) and a compounded annualized

interest rate using equation (4). In the following analysis, we use the non-compounded figures

- since compounding will tend to increase the differences between monthly interest rates, this

provides a stricter test for seasonality.

(3)

(4)

Although we do not have all the requisite data for all of the exchange rate series described

above, we are able to reconstruct a dataset of implicit interest rates for 26 currency pairs in

seven European financial centres, as shown in table 2. The features of the interest rates

themselves are discussed in more detail in Bell et al. (2015) but here we will focus on the

information they contain about the seasonality of the money markets.

Testing for seasonality

When analysing our exchange and interest rates, we work mainly with the growth rates of the

exchange rates, since we know that this analysis will be econometrically valid even if the raw

rates contain unit roots – which a number of the FX series do (Bell et al. 2013).

8

For both interest rates and exchange rates, we first take monthly averages over all data points

available for each month and then calculate the change between each month. The interest

rates are already expressed as a percentage figure and we convert the FX series into

percentage changes from the previous month. Unfortunately, it is in the nature of medieval

data sources that there are inevitably missing values for some months. In order not to lose too

many data points, we interpolate in such cases by rolling forward the value that was available

for the previous month. We ensure that we never roll forward a data point for more than three

months in a row – if there are more than three months’ of missing observations, we truncate

the sample at that point. As a result, some series with a reasonable number of observations

overall, for example some of the FX rates involving Pisa or interest rates for Genoa-London

or Venice-Paris had to be dropped since there were significant gaps in the series.

The final four columns in Tables 1 and 2 show the date range covered, the number of

observations and the number of interpolated values for each FX rate and interest rate series

respectively. As a result, for most of the FX rate series we have 150-300 observations, or

between twelve and nearly thirty years of data. As can be seen, the number of such missing

values for the FX series is modest, and rarely more than 10 per cent of the total sample. The

interest rate series are slightly shorter, since they require overlapping data from two places but

still substantial and, in some cases, also required more interpolation.

There are various ways to test for seasonality in time-series data. It is possible to employ

trigonometric functions or to work in the frequency domain. However, in such cases the

quantitative sophistication arises at the expense of interpretability. A much simpler approach,

which we apply here, is to use a linear regression including monthly dummy variables, as

shown in equation (5).

ttttt

ttttttttt

uDDDD

DDDDDDDDy

1211109

87654321

1211109

87654321

(5)

where yt is the exchange rate percentage change series under consideration, and D1t, D2t …

D12t are monthly dummy variables for January, February, …, December, and ut is an error

term, assumed to be normally distributed with zero mean and constant variance. These

dummy variables take the value one for the month to which they correspond and zero

otherwise – so, for example, D1t takes the value one every January and zero for every other

month. This way, the dummies effectively “pull out” the observations for their corresponding

months and set everything else to zero. Thus each parameter attached to the dummies can be

interpreted as the average change in the foreign exchange rate for that month (in percentage

terms).

Note that, given the way that it has been specified to contain a full set of 12 monthly dummy

variables, this regression must not contain an intercept term to avoid the “dummy variable

trap.” This would have arisen if all possible seasonal dummies given the frequency of the data

employed (i.e. 12 for monthly, four for quarterly, etc.) were included in the model together

with an intercept. The result would be that the regression could not be run due to

multicollinearity.

The empirical results for each of our seven financial centres are shown in Tables 3-16 (the FX

rates and interest rates for each centre are placed together to make it easier to follow the

descriptive analysis in the following section). As noted above, we work with growth rates

rather than with the raw levels, so the coefficients represent the average change in the FX rate

or the interest rate compared to the previous month in percentage terms. In addition, we can

9

also assess the statistical significance of the monthly changes. This allows us to identify

periods of significant tightening and easing in the money market at each centre.

However, analysing the growth rates in this way may obscure some trends over the course of

the year. For instance, if the FX or interest rate increased by 1% in March and then decreased

by 0.5% in April, although the money market did ease during April compared to March, the

average FX/interest rate would still have been higher than it was in February. For this reason,

we also construct an index of the cumulative movement in FX and interest rates over the

course of the year, shown in Figures 1-14 (for FX rates and interest rates at each of the seven

centres). The following reconstruction of the seasonality of the money market at each centre

(both in terms of the FX rates and the interest rates) should be read in conjunction with both

the tables and the figures.

Results

The only previous statistical study of the seasonality of medieval FX rates was conducted by

Hyman Sardy, based on the data collected by de Roover for Barcelona and Bruges. Sardy

found that ‘a seasonal pattern was found to exist in most of the series. This pattern, however,

does not conform perfectly to the description given in various merchant manuals. In general,

about ten per cent of the fluctuations in the series could be attributed to seasonality’ (de

Roover 1968, 104).

Our analysis draws on a larger sample and also uses techniques that were not easily available

to Sardy in the 1960s. Moreover, we also apply the same analysis to interest rates as well as

exchange rates. The following consider the same factors as above. First, do the series show

seasonal patterns and how much of the variation in each series can be explained by

seasonality? Second, do the trends observed in the data match the descriptions of the money

markets found in three near-contemporary merchant manuals and can they be explained in

terms of economic factors?

Our analysis provides strong support for the seasonality of the medieval FX market. The F-

tests show that the majority of the FX rates series exhibit a seasonal component that is

significant at the 1% level. Partial exceptions are Barcelona-Florence, Barcelona-Majorca and

Genoa-Valencia at the 5% significance level and Florence-Genoa at the 10% level. Perhaps

more noteworthy are the pairs that do not exhibit significant seasonality, namely Barcelona-

Venice, Florence-Avignon, Florence-Pisa and Florence-Rome, Genoa-Florence, Genoa-Pisa

and Genoa-Rome, and Venice-Milan. The lack of seasonality in the triangle between

Florence, Genoa and Pisa probably reflects the closely-integrated nature of these markets, as

might Venice-Milan. The lack of seasonality in the rates for Rome is also telling –

contemporary observers linked the demand for money at Rome to the presence of the papal

court (‘where the pope goes, money is expensive’) rather than to any consistent seasonal

pattern.

Just over half of the interest rate series show seasonality at the 1% level, a further five at the

5% level and three at the 10% level (Bruges-London, London-Bruges and Venice-Barcelona).

Only three do not exhibit this level of significance (Florence-Genoa, Genoa-Bruges and

London-Genoa). With the exception of Florence-Genoa, it is notable that most of these

involve London, Bruges and Barcelona, which lay at the periphery of the European financial

markets.

Our results suggest that seasonality accounted for a significant portion (generally the value of

R2 is 10-20%) of the total variation in FX rates, although it was clearly not the only or even

possibly the major determining factor. The extent of seasonality also varied significantly

10

between currency pairs. Figures 15 and 16 show the R2 values for the currency pairs in

descending order. Some currency pairs exhibited little seasonality while others were more

strongly affected. For example, the FX rates involving Venice demonstrate strong seasonality

that is clearly linked to the departure of the great Venetian galley fleets to the East in the

summer. On the other hand, FX rates between Florence, Genoa and Pisa did not exhibit strong

seasonal fluctuations, which again probably reflects the integration of these markets.

These variations in implied interest rates also had a major seasonal component and can shed

light on economic activities at the different financial centres and wider patterns of trade across

Europe. There is a much greater degree of correlation between interest rates at the same centre

across currency pairs. This suggests that the seasonal influences on the local money market

were highly significant.

The remainder of this section will set out the seasonal patterns at each financial centre based

on our quantitative analysis of the FX rates and the implicit interest rates. In each case, the

picture obtained from our quantitative analysis will be compared against the more qualitative

accounts presented in the merchant manuals attributed to Saminiato de’ Ricci (c.1396-1416),

Giovanni di Antonio da Uzzano (c.1422-40) and Giorgio di Lorenzo da Chiarini (c.1450).

Each of these has a section devoted to ‘the shortage/appreciation of money in many places’.

Ricci discusses Avignon, Bruges, Barcelona, Montpellier and Paris. Chiarini adds Genoa,

Naples, Rome, Valencia and Venice. Uzzano provides the most detailed discussion and also

covers Bologna, Florence and Pisa.

Barcelona

In Barcelona, the observed trend is for FX rates to reach their lowest point in late spring and

then to increase over the summer, peaking in autumn and starting to decline over the winter

(December or January in most cases but in November for Bruges). There are some differences

in timing - Florence, Genoa, Pisa and Venice all bottom out in April while Avignon, Bruges,

Majorca and Montpellier continue to weaken into June (May for Bruges). The former rates

start to appreciate strongly in May while the latter increase strongly in August, September and

October. Bruges is the first rate to fall in November, followed by Avignon, Genoa and

Montpellier in December, Florence and Pisa in January and Venice in February. We only

have interest rate data for Bruges, Florence, Genoa and Venice but they follow a similar

pattern to the above, with interest rates increasing from the spring to peak in October or

November, before falling from November/December and bottoming out in March/April.

According to Uzzano:

In Barcelona, money is dear from the first of June through all of August because of

the investments in wool from Aragon and the surrounding valleys and because of the

purchases of ‘grain’ [the dye] in Valencia; the money market tightens again in

October, after St Luke’s day, which is on the 18th, because of the investments in

saffron, when the dearness is even greater than in the wool season, and it will last

until January; and from then on, money eases every day and the exchange rates return

to their former level, and the easiness lasts until the wool season, unless something

unexpected happens (translated in de Roover 1968, 88).

Ricci and Chiarini give very similar accounts.

The FX and interest rates generally confirm the story presented by the practitioners. There is

indeed a general increase in FX and interest rates over the summer, from May or June and

lasting until October or November. It is also the case that FX and interest rates reached their

lowest point in the spring, when the merchant manuals state that conditions in the money

11

market were at their easiest. However, all three manuals describe monetary conditions as

tightening in October and remaining ‘dear’ until January. The FX and interest rate series do

indeed peak in October and remain high in November. Although they do begin to decline in

December, accelerating in January and February, the FX and interest rates in December-

January are still higher than during the summer, as Uzzano predicted.

Bruges

The situation in Bruges was less clear-cut and our results indicate a number of different

patterns. The strongest evidence for seasonality is for the FX rates with London and, albeit

with a different pattern, Genoa and Venice. For the former, the pond groot weakened

substantially against sterling in February through May, remained low during the summer

before strengthening strongly in September, October and November. For the latter two, the

pond groot strengthened against the Genoese florin and the ducat from April through to July,

before starting to weaken in the autumn, especially from September and during December to

February. The degree of variation in the Parisian rates is much smaller, possibly reflecting the

very close relationship between the two centres which made it relatively easy to ship specie

from one to the other. There were significant rises in the FX rate in July and November,

bracketing a fall in October. The Barcelona rates show even less seasonality – with the only

significant monthly changes being a sharp drop in September and an even steeper increase in

November.

This degree of variety probably reflects the position of Bruges as a nodal point between

several trading/financial systems. It was linked to Genoa and Venice directly but also via

Barcelona, to Barcelona itself, and also London and the North Sea world.

While the FX rates tell a number of different stories, interest rates were more consistent. In all

cases they began to rise in April or May (although Paris exhibits a sharp fall in June) and peak

in August-September. They then fell over the winter/spring, albeit interrupted by a secondary

peak in November, starting in October for Barcelona and Paris and December for Genoa and

Venice. The picture for London is hard to interpret, as there are a series of sharp increases and

reverses, especially between September and February.

It is difficult to reconcile this with any of the near-contemporary descriptions from the

merchant manuals. According to Uzzano, ‘money is dear in December and January because of

the many ships that are being loaded with commodities and dispatched at this time, and, in

August and September, money expands because of the fairs that are being held and that attract

merchants who come to purchase and bring in ready cash’ (de Roover 1968, 90). Ricci also

mentions shortage of money in December, January and July because of the loading of the

ships but Chiarini dates this to December and June [unless the latter is an error for January].

De Roover (1968, 49) prefers an anonymous author that 'money in Bruges was usually tight in

May and June and in December up to the first half of January, but it was easy at other times

when the Germans and country people repaired to Bruges to make their purchases and spent a

lot of specie'. However, Bolton and Guidi Bruscoli (2009, 368), based on the account books

of the Borromei of Bruges for 1438, found that ‘business seemed to be thriving, with peaks of

activity in late May and early June, and in October and early November, at the time of the

[Brabantine] fairs’.

There is no real indication in either the FX or interest rates for there being a tight money

market in December-January – if anything, the reverse was the case. The authors of the

merchant manuals concentrate on the need to raise funds to pay for goods to be loaded onto

the departing galleys and the ‘dearness’ that this may have caused but in fact the pond groot

depreciated rather than appreciated in December and January. It is possible that the arrival of

the galleys with their cargos stimulated trade and liquidity in the local market and left the

12

sellers of imported goods with money in hand and looking to buy bills of exchange. The data

does fit better with Bolton and Guidi Bruscoli’s observations about the Borromei’s business -

the increases in FX and interest rates over the early summer may be linked to the Brabantine

fairs and the anomalous increase in the sterling FX rate in September, October and November

could be linked to English merchants selling bills of exchange to fund their activities at the

Koudmarkt in Antwerp at the start of November.

Florence

Turning to FX and interest rates at Florence, the first point to make is that there was much

less seasonal variation than at the other centres. On the one hand, this may reflect Florence’s

prominence as a financial centre, which meant that there was always ample liquidity

available. On the other, Florence itself was not a major trading centre so there were no periods

of exceptional demand linked to fairs or the departure and arrival of trading fleets, as there

were at Venice. The main observable trend is the florin appreciated against nearly all of the

other currencies in August/September and then depreciated after October. Similarly, interest

rates increased over the summer, peaking in August/September, and then decreased over the

winter.

Only Uzzano discusses Florence - possibly because the authors of merchant manuals were

generally writing for a Florentine audience that might be expected to be familiar with local

conditions – and even he has relatively little to say. His main comment is that money in

Florence is always rising in value (tightening) but that September through to January were

good (=looser?) because annual payments were made in the countryside (presumably

connected to the harvest), which money was then brought into the city and to the banks.

Uzzano is often obscure and his precise meaning here is unclear. The graph of monthly

interest rates shows a clear increase in the interest rate at Florence over the summer, dropping

after September and into the spring. This corresponds closely to Uzzano’s description of

monetary conditions in Florence and would fit in well with an increase in the money supply in

Florence as farmers brought their harvest profits into the city.

There are some more particular movements in individual currency pairs – there is a clear

depreciation of the florin against the lira of Barcelona and the pound sterling in March and

April. The latter might be linked to the need for Florentine merchants to make remittances to

those places to fund their prospective trading activities – given the usance period of two

months to Barcelona and three months to London, if the merchants wished to purchase wool

in either place during June or July, they would need to buy bills of exchange in March and

April. The clearest pattern concerns Bologna and Venice – FX rates for these two places

tended to move together since the main route between Florence and Venice ran through

Bologna. There is a drop in the florin-ducat FX rate in June and July as merchants in Florence

sought to remit to Venice in time for the galley season (discussed in more detail below). Once

this buying pressure ceased towards the end of August, the FX rates returned to their previous

level. The renewed depreciation of the florin after October could be linked to the more

general seasonal trend at Florence described above, or the resumption of remittances to

Venice to fund the spring galley fleets.

Genoa

Like Florence, the Genoese money market was not the most seasonal. The impact of seasonal

changes on FX rates with Bruges, Florence and (especially) Pisa and Rome was minimal.

Seasonality was more significant for London, Bologna and Venice, and Avignon/Montpellier/

Paris. The general tendencies are for FX rates to decrease during the spring, especially in

April-May and then to increase until the late summer (July-August-September) and again over

the winter (December-January). Interest rates show a slightly different pattern – again the

13

lowest point is in February before increasing steadily to peak in July/August/September but,

unlike the FX rates, subsequently dropping sharply in October and November.

For Genoa, Chiarini states that money was dear in September, January and April for the

loading of the galleys. According to Uzzano, money was expensive at Easter but the greatest

natural increase was in July, as cash was withdrawn from the banks and moneychangers to be

loaded onto the galleys headed to the Levant.

Again, part of this description is supported by the empirical evidence – FX and interest rates

did peak around July-September, when the galleys were leaving for the East. However, both

sets of rates were at their lowest around Easter – contrary to both authors’ description of

money being tight or expensive at this time. It is possible that the supply of local money from

buyers of bills of exchange wishing to remit to Spain or Northern Europe in the spring (to

fund purchases in the summer season) counter-balanced any demand from sellers to borrow

money in Genoa.

London

For London, we only have FX rates for Bruges and Genoa and interest rates based on

exchange and rechange transactions with the same places, but they both tell a very consistent

story. FX and interest rates were low in the spring, began to rise in May-June and peaked in

August-September, before falling away again over the winter. Unfortunately, Uzzano does not

report on the London money market in his handbook, but it is likely that the demand for

money (and therefore FX rates) was tied to the wool-shearing and shipping season, as that

was still England’s major export at this time. Mid-June was the best time to buy as the new

wool was coming onto the market after the summer shearing at the Cotswold fairs – one

month later fairs on the Welsh marches (Bradley 1994, 59-60). If merchants were raising

money to fund their purchases by selling bills of exchange drawing on Bruges and Genoa,

then we would expect to sell sterling appreciate between May and July.

Paris

The Parisian money market exhibits some of the strongest seasonal behaviour – with the

monthly dummies accounting for around 30% of the observed variation. There were two

distinct peaks in both FX and interest rates: the first in May-June and the second in

September-October. The first was followed by a small but distinct fall in July and the second

by a more pronounced and prolonged decline between November and January. With the

partial exception of Bruges, which operated on a slightly different frequency, the FX rates

with Southern France and Italy were also highly integrated – the correlation of the changes in

the rates with Avignon, Genoa, Montpellier and Venice is between 0.89 and 0.99.

The situation at Paris partly matches the accounts given in the merchant manuals. All three

authors’ identify two periods of tightness in the money market, both linked to fairs. The first

was the Lendit fair which ran from 12-24 June, which agrees with our data, but the second

was the fair of St Andrew in December, when our data shows a marked depreciation in the

livre tournois, suggesting an easing of the money market, while none of the authors mention

any tightness in September/October.

Venice

Venice is another strongly seasonal market – with the partial exceptions of the satellite cities

of Bologna and Milan. The FX rates show a marked drop for most places in February

followed by a sharp increase in June and July and an equally dramatic fall in September (with

a slight recovery in some places after October). The pattern for Bruges and Paris differs

slightly – the fall in September is less marked but instead there is a slightly later drop in

November. We are only able to calculate interest rates for four currency pairs (Barcelona,

14

Burges, Florence and Genoa) but these show a similar pattern to the FX rates, with a peak in

the summer, a rapid fall in September and more gradual decline over the autumn/winter.

This can be compared with Uzzano’s very detailed reconstruction of the Venetian money

market:

In Venice money is expensive from May to 8th September, because of the

outward bound galleys which leave in July, August and September. The reason why it

gets more expensive is because everyone starts to make arrangements and they want

to remit more there; and this higher cost is due to the amount of cash the galleys

carry, because a great deal of merchandise is sold there at the time of the galleys,

which must be paid for just when you have many demands on your purse - and a lot

of money goes out of the banks in cash, so cash is always dear there by 1% more than

usual. And money is highly priced for all places, and is offered there at various

maturities. From 8th July money is highly priced, then there are no more maturities

until 1st August, and in this month [July] there is an expansion by ½ to 1%. From 1st

August money starts to fluctuate, and is expensive continually until 8th September;

and after the 8th all [payment terms] have become due, and all the galleys have gone,

so there is no more demand - and the banks are quick to supply and money goes

through the floor. (translation by Dr Helen Bradley).

This agrees with the empirical data. Uzzano’s figure of a ½% to 1% increase in exchange

rates in July is matched in the FX data, which then fluctuate around this high level in August,

before crashing by between 0.5 and 1.5% in September. It can also be seen very clearly from

the shortest term interest rate with Florence which increased by 27% (annualised) in August

before dropping 19% in September – money indeed went through the floor.

Conclusion

It is clear that there was a seasonal element in most of the FX and interest rate series, although

the timing and strength of this effect varied between currency pairs. Furthermore, there were

distinct seasonal rhythms at each of the seven financial centres – although these do not always

match up precisely with the predictions of near-contemporary observers. A more detailed

reconstruction of productive, trading and financial activities at each centre might help to

account for these differences.

In addition to analyzing the seasonal patterns within each centre, it is also possible to look for

wider connections between them – were there wider macro-economic trends that affected all

the centres? The average FX and interest rates at each centre are shown on figures 17 and 18

respectively. A simple visual inspection shows a clear difference between the two, which can

be confirmed by looking at correlation matrices in Tables 18 and 19. In short, there is no

obvious pan-European pattern in terms of FX rates, which reflected the particular conditions

at each centre, but there is a clear and consistent seasonal pattern in interest rates across all the

cities.

Interest rates tended to rise over the summer, peak in the autumn, decline over the winter and

reach their lowest point in spring. There are two plausible explanations for this. First, it could

reflect pan-European economic flows. In effect, Europe ran a substantial trade deficit with

Africa and the East. To balance this, it was necessary to export specie, especially silver (the

gold:silver ratio was generally more favourable to the latter in the East than in Europe). Thus,

silver tended to be sucked towards Venice as the galley fleets prepared to leave in the late

summer, and this demand for cash caused interest rates to rise. Subsequently, when the

galleys fleets returned with their cargos around Christmas (arriving in Northern Europe in the

15

spring), the influx of goods spurred liquidity and economic activity. Second, it may reflect the

importance of the agricultural year in a still largely agrarian society.

Much recent research has concentrated on assembling economic data on an annual basis in

order to explore long-run trends – but the initial analysis above suggests that there is an

equally intriguing story to be told concerning intra-year patterns.

16

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19

Table 1: Exchange Rates Sample

Centre Currency Code Method of Quotation Start End N INT

Barcelona

Avignon BarAvi Pence of Barcelona per papal franc 1383/2 1410/4 327 28

Bruges BarBru Pence of Barcelona per écu of 22 groats 1383/2 1411/1 336 29

Florence BarFlo Pence of Barcelona per florin di suggello 1397/6 1411/9 172 8

Genoa BarGen Pence of Barcelona per Genoese florin 1383/2 1411/2 337 26

Majorca BarMaj Pence of Barcelona per Majorcan real 1392/3 1409/3 205 50

Montpellier BarMon Pence of Barcelona per franc 1383/2 1411/3 338 23

Pisa BarPis Pence of Barcelona per Pisan florin 1392/3 1405/12 166 8

Venice BarVen Pence of Barcelona per Venetian ducat 1399/1 1411/9 153 5

Bruges

Barcelona BruBar Pence of Barcelona per écu of 22 groats 1392/1 1410/11 227 9

Genoa BruGen Groats per Genoese florin 1392/1 1410/11 227 9

London BruLon Pence sterling per écu of 24 groats 1392/1 1410/11 227 11

Paris BruPar Groats per livre tournois 1392/1 1410/11 227 9

Venice BruVen Groats per Venetian ducat 1392/1 1410/11 227 8

Florence

Avignon FloAvi Papal florins per 100 florins di suggello 1385/9 1411/7 311 30

Barcelona FloBar Pence of Barcelona per florin di suggello 1388/12 1411/9 274 14

Bologna FloBol Bolognese ducats per 100 florins di suggello 1383/2 1402/7 234 21

Bruges FloBru Groats per florin di suggello 1391/12 1411/3 232 2

Genoa FloGen Genoese florins per 100 florins di suggello 1383/2 1410/10 333 5

London FloLon Pence sterling per florin di suggello 1396/1 1411/7 187 6

Lucca FloLuc Lucchese florins per 100 florins di suggello 1397/10 1411/7 166 1

Montpellier FloMon Francs per 100 florins di suggello 1395/12 1411/7 188 1

Paris FloPar Livre tournois per 100 florins di suggello 1392/3 1411/7 233 1

Pisa FloPis Pisan florins per 100 florins di suggello 1383/2 1397/10 177 3

Rome FloRom Cameral florins per 100 florins di suggello 1391/3 1407/9 199 13

Venice FloVen Pence affiorino per lira di grossi of 10 ducats 1383/2 1411/7 342 3

Genoa

Avignon GenAvi Papal florins per 100 Genoese florins 1383/2 1410/6 329 3

Barcelona GenBar Pence of Barcelona per Genoese florin 1383/2 1410/9 332 4

Bologna GenBol Bolognese ducats per 100 Genoese florins 1383/3 1402/5 231 12

Bruges GenBru Groats per Genoese florin 1383/1 1410/9 333 9

Florence GenFlo Florins di suggello per 100 Genoese florins 1383/1 1410/9 333 4

London GenLon Pence sterling per Genoese florin 1392/8 1410/9 218 6

Montpellier GenMon Florins per 100 Genoese florins 1383/2 1410/9 332 4

Paris GenPar Livre tournois per 100 Genoese florins 1383/3 1410/9 331 12

Pisa GenPis Pisan florins per 100 Genoese florins 1383/2 1406/2 277 2

Rome GenRom Cameral florins per 100 Genoese florins 1391/7 1407/9 195 53

Valencia GenVal Pence of Valencia per Genoese florin 1398/6 1410/9 148 5

Venice GenVen Venetian ducats per 100 Genoese florins 1383/2 1410/9 332 6

London Bruges LonBru Pence sterling per écu of 24 groats 1394/2 1405/6 137 38

Genoa LonGen Pence sterling per Genoese florin 1394/2 1405/6 137 39

Paris

Avignon ParAvi Papal francs per 100 livre tournois 1394/4 1409/4 181 18

Bruges ParBru Groats per livre tournois 1394/4 1409/5 182 14

Genoa ParGen Genoese florins per 100 livre tournois 1394/4 1409/5 182 14

Montpellier ParMon Francs per 100 livre tournois 1394/4 1409/5 182 14

Venice ParVen Venetian ducats per 100 livre tournois 1394/12 1409/5 174 38

Venice

Barcelona VenBar Pence of Barcelona per Venetian ducat 1399/1 1410/9 141 1

Bologna VenBol Bolognese ducats per 100 Venetian ducats 1386/8 1410/9 290 29

Bruges VenBru Groats per Venetian ducat 1399/3 1410/9 139 2

Florence VenFlo Pence affiorino per lira di grossi of 10 ducats 1384/3 1410/9 319 10

Genoa VenGen Genoese florins per 100 Venetian ducats 1384/3 1410/9 319 16

Lucca VenLuc Lucchese florins per 100 Venetian ducats 1399/4 1410/8 137 11

Milan VenMil Milanese ducats per 100 Venetian ducats 1394/4 1404/4 121 7

Paris VenPar Grossi of Venice per livre tournois 1399/3 1411/3 145 6

Pisa VenPis Pisan florins per 100 Venetian ducats 1384/3 1405/9 259 14

20

Table 2: Implicit interest rates from the Datini archive, 1380-1411

Centre Currency Term Match N Spread Simple APR Comp APR Start End Number INT

Barcelona

Bruges 120 4 337 4.40% 13.39% 14.42% 1395/2 1410/7 185 30

Florence 120 3 278 3.93% 11.97% 12.73% 1397/4 1406/9 113 15

Genoa 80 2 560 3.33% 15.18% 16.79% 1391/5 1409/8 219 18

Venice 120 3 245 4.43% 13.47% 14.31% 1399/3 1409/9 126 20

Bruges

Barcelona 120 4 459 4.50% 13.70% 14.67% 1394/12 1410/10 190 32

Genoa 94 1 466 3.73% 14.49% 15.93% 1392/1 1410/6 221 14

London 60 4 179 2.24% 13.66% 15.20% 1399/4 1405/5 73 10

Paris 46 1 194 1.33% 11.55% 11.53% 1394/3 1409/4 181 29

Venice 120 3 500 3.68% 11.18% 11.82% 1398/11 1410/6 139 18

Florence

Barcelona 120 3 393 3.85% 11.70% 12.37% 1394/3 1409/4 181 12

Genoa 28 0 880 0.83% 10.83% 12.10% 1398/11 1410/6 139 5

Venice 30 1 832 0.82% 10.03% 10.91% 1397/2 1410/11 165 13

Genoa

Barcelona 80 2 543 3.48% 15.89% 17.59% 1383/7 1409/9 314 26

Bruges 94 1 558 3.52% 13.67% 14.67% 1383/11 1410/9 322 13

Florence 28 0 880 0.84% 10.92% 12.17% 1383/6 1410/6 324 10

London 180 4 356 6.85% 13.89% 14.54%

Paris 88 2 510 3.81% 15.82% 17.05% 1394/11 1409/4 173 16

Venice 50 0 269 1.45% 10.56% 11.44% 1391/2 1410/6 232 15

London Bruges 60 4 127 2.11% 12.82% 14.30% 1396/3 1405/6 111 39

Genoa 180 4 166 6.67% 13.52% 14.20% 1394/2 1405/6 136 43

Paris

Bruges 46 1 190 1.15% 9.09% 9.72% 1394/4 1409/1 177 38

Genoa 88 2 446 3.39% 14.07% 15.37% 1394/4 1409/4 180 21

Venice 120 3 288 4.26% 12.96% 13.77% 1398/11 1409/4 125 32

Venice

Barcelona 120 3 243 4.28% 13.01% 13.81% 1399/3 1408/11 116 18

Bruges 120 3 375 3.38% 10.27% 10.85% 1399/1 1410/8 139 16

Florence 30 1 769 0.79% 9.62% 10.89% 1390/6 1410/9 243 21

Genoa 50 0 258 1.32% 9.62% 10.78% 1391/2 1410/8 234 26

Paris 120 3 248 4.14% 12.58% 13.34%

21

Table 3: Barcelona Exchange Rate Monthly Average Percentage Changes

Avignon Bruges Florence Genoa Majorca Montpellier Pisa Venice

Jan -0.47* -0.80

** -0.85

* -1.23

*** -0.54

*** -0.43

* -2.29

*** -0.23

Feb -0.44* -0.75

** -0.75 -0.71

** -0.28 -0.50

** -0.56 -0.97

**

Mar -0.07 -0.25 0.79* -0.04 -0.14 -0.06 -0.42 0.37

Apr -0.21 -0.13 -0.41 -0.08 -0.08 -0.14 -0.26 -0.68

May -0.12 -0.10 1.42***

0.81**

-0.22 -0.06 0.63 0.93**

Jun -0.30 0.13 -0.08 0.47 -0.02 -0.34 0.38 -0.36

Jul 0.46* 0.60

* -0.04 0.46 0.00 0.31 0.66 0.27

Aug 0.39 0.76**

0.34 0.44 0.14 0.72***

0.49 0.71

Sep 0.61**

1.14***

0.54 0.28 0.27 0.54**

0.95**

0.46

Oct 0.72***

0.97***

-0.27 0.69**

0.64***

0.70***

0.40 -0.04

Nov 0.06 0.00 -0.22 0.20 0.19 0.13 0.12 -0.27

Dec -0.53**

-0.26 -0.44 -1.11***

0.12 -0.66***

-0.44 -0.41

STDERR 0.0024 0.0032 0.0085 0.012 0.0054 0.0043 0.023 0.0023

R2 0.10 0.12 0.12 0.13 0.12 0.11 0.20 0.11

F-Test 2.95***

3.63***

1.84**

4.10***

2.13**

3.21***

3.18***

1.46

Table 4: Barcelona Interest Rate Monthly Average Percentage Changes

Bruges Florence Genoa Venice

Jan -4.34**

-2.92 -6.70***

-1.50

Feb -2.20 -5.49**

-6.55***

-2.41

Mar 1.68 1.46 -6.52***

-3.21*

Apr 0.75 -1.68 -1.33 0.30

May 0.48 4.10**

5.61***

5.03***

Jun -1.61 2.29 2.48 1.40

Jul -1.34 1.09 5.13**

-0.25

Aug 2.44 -1.56 0.90 3.36**

Sep 7.39***

4.87**

0.82 1.57

Oct 4.97***

0.49 5.66**

1.68

Nov -2.75 -1.22 3.99* -0.36

Dec -5.60***

-1.78 -1.78 -3.22*

STDERR 0.018 0.021 0.022 0.017

R2 0.21 0.18 0.20 0.19

F-Test 3.8324***

1.9291**

4.4325***

2.2323**

22

Table 5: Bruges Exchange Rate Monthly Average Percentage Changes

Barcelona Genoa London Paris Venice

Jan -0.41 -0.84**

-0.32 -0.25 -0.67**

Feb -0.44 -0.88***

-0.70***

-0.19 -0.63**

Mar -0.48 -0.15 -0.59**

-0.19 -0.46

Apr 0.19 0.87***

0.15 0.04 0.63**

May 0.44 1.20***

-1.01***

0.08 0.47

Jun -0.02 0.61**

-0.11 0.08 0.28

Jul 0.49 0.95***

0.03 0.66***

0.95***

Aug -0.45 -0.72**

0.12 -0.12 -0.42

Sep -0.68**

-0.24 0.64**

-0.26 0.13

Oct -0.27 -0.70**

0.82***

-0.53***

-0.67**

Nov 1.10***

0.16 0.96***

0.50***

-0.21

Dec 0.09 -0.84**

-0.38 -0.26 -0.91***

STDERR 0.0032 0.0033 0.0025 0.0018 0.0031

R2 0.12 0.23 0.23 0.15 0.17

F-Test 2.37***

5.19***

5.27***

3.15***

3.60***

Table 6: Bruges Interest Rate Monthly Average Percentage Changes

Barcelona Genoa London Paris Venice

Jan -1.88 -2.89 7.77 -1.67 -1.23

Feb -1.95 -6.98***

-9.57 0.42 -3.48**

Mar -0.17 -3.87* 1.42 -2.54 -1.03

Apr -2.49 1.50 3.68 1.06 3.00*

May 3.92**

6.61***

4.19 3.44 5.39***

Jun 1.19 4.65**

0.69 -6.71**

1.87

Jul 4.54**

8.06***

-0.05 4.82* 0.83

Aug 3.85**

0.68 0.97 6.26**

1.42

Sep -2.35 -3.49 -14.61**

5.10* -0.17

Oct -4.86**

0.64 -0.51 -9.32***

-0.13

Nov 0.27 2.18 14.34**

2.71 1.04

Dec -1.53 -6.02***

-11.01* -3.09 -7.32

***

STDERR 0.019 0.021 0.059 0.026 0.0175

R2 0.13 0.22 0.26 0.18 0.22

F-Test 2.1478**

4.9963***

1.7724* 3.2127

*** 3.0818

***

23

Table 7: Florence Exchange Rate Monthly Average Percentage Changes

Avignon

Barc-

elona Bologna Bruges Genoa London Lucca

Mont-

pellier Paris Pisa Rome Venice

Jan 0.25 0.42 0.23* 0.04 -0.18 0.14 0.11 0.26 0.25 -0.08 -0.15 0.10

Feb 0.24 0.26 0.09 0.16 0.25 -0.10 0.19 0.18 0.08 0.13 0.18 0.35**

Mar -0.13 -0.60**

0.11 -0.53**

-0.02 -0.63**

0.17 -0.22 -0.30 -0.22 -0.09 -0.10

Apr -0.14 -0.47* -0.11 -0.25 0.23 -0.76

** 0.18 -0.27 -0.47

** 0.15 -0.31 0.04

May -0.04 -0.18 -0.15 -0.19 0.42**

0.08 0.16 -0.12 -0.08 0.12 0.17 0.02

Jun 0.15 -0.02 -0.52***

0.47* 0.18 0.41 0.10 0.42 0.51

** 0.09 -0.02 -0.43

***

Jul -0.10 0.91***

-0.39***

0.49**

0.18 0.52 -0.15 0.48* 0.70

*** -0.29

** -0.24 -0.72

***

Aug 0.60***

0.69**

0.25* 0.80

*** 0.34

* 1.08

*** 0.34

** 1.05

*** 1.04

*** -0.11 0.19 0.57

***

Sep 0.24 0.09 0.71***

0.65***

-0.17 0.71**

0.11 0.50* 0.53

** 0.09 0.40 1.33

***

Oct -0.08 -0.43 -0.25**

0.49**

-0.27 -0.27 -0.45***

0.00 -0.03 0.24 0.47 -0.45***

Nov -0.33 -0.22 -0.10 -0.53**

-0.13 -0.31 0.01 -0.85***

-0.96***

-0.25* -0.40 -0.46

***

Dec -0.03 0.05 -0.01 -0.38 -0.42***

-0.16 -0.60***

-0.63**

-0.32 0.06 -0.25 -0.35**

STDERR 0.0021 0.0029 0.0013 0.0024 0.002 0.0032 0.0016 0.0028 0.0024 0.0015 0.003 0.0014

R2 0.05 0.10 0.25 0.17 0.06 0.15 0.18 0.18 0.23 0.09 0.05 0.35

F-Test 1.41 2.44***

6.26***

3.66***

1.65* 2.63

*** 2.80

*** 3.13

*** 5.36

*** 1.39 0.76 14.59

***

Table 8: Florence Interest Rate Monthly Average Percentage Changes

Barcelona Genoa Venice

Jan 0.89 -6.46***

-3.99**

Feb -0.74 1.04 0.89

Mar 1.68 -0.32 1.14

Apr -2.11 -2.19 2.14

May 0.38 3.06 -0.53

Jun 1.90 1.65 4.07**

Jul 3.81**

1.86 -1.88

Aug 1.63 3.17 -3.11*

Sep 1.30 -0.93 5.58***

Oct -6.11***

-2.72 0.89

Nov -0.81 -0.13 -4.34**

Dec -2.71 1.80 -0.54

STDERR 0.018 0.023 0.018

R2 0.13 0.05 0.09

F-Test 1.9858**

1.3361 2.661***

24

Table 9: Genoa Exchange Rate Monthly Average Percentage Changes

Avignon

Barc-

elona Bologna Bruges Florence London

Mont-

pellier Paris Pisa Rome Valencia Venice

Jan 0.66***

0.88***

0.46**

-0.11 0.38* 0.45 0.62

*** 0.37

* 0.36

* 0.67

* 0.56 0.32

Feb -0.19 0.12 0.01 -0.64 -0.42**

-0.90***

-0.27 -0.20 -0.23 -0.73* -0.59 -0.08

Mar -0.04 -0.19 -0.11 -1.01**

0.10 -0.72**

0.10 0.13 0.03 -0.03 -0.13 0.13

Apr -0.66***

-0.76***

-0.24 -0.78* -0.29 -1.27

*** -0.86

*** -1.02

*** -0.26 -0.32 -0.96

** -0.42

**

May -0.68***

-0.67**

-0.61***

-0.22 -0.46**

-0.56* -0.77

*** -0.83

*** -0.27 -0.55 -0.49 -0.49

**

Jun -0.36* -0.04 -0.44

** 0.52 -0.08 0.36 0.06 0.49

** -0.13 -0.40 0.05 -0.45

**

Jul 0.16 0.49* -0.29 0.43 0.16 0.31 0.49

** 0.44

** -0.03 0.09 0.00 -0.67

***

Aug 0.39* 0.27 0.17 0.33 -0.06 0.37 0.29 0.60

*** 0.19 0.04 0.98

** 0.46

**

Sep 0.21 -0.25 1.12***

0.70* -0.06 0.94

*** 0.38

* 0.30 0.19 0.14 -0.73

* 1.10

***

Oct 0.03 -0.42 -0.15 0.16 0.08 -0.03 -0.09 -0.05 -0.09 0.56 -0.26 0.11

Nov -0.04 0.07 -0.21 -0.07 -0.10 0.13 -0.21 -0.43**

-0.18 -0.08 0.23 -0.60***

Dec 0.53***

0.60**

0.08 1.03**

0.34 0.78**

0.37* 0.46

** 0.23 0.18 1.11

** 0.04

STDERR 0.002 0.0027 0.0021 0.0042 0.0022 0.0030 0.0021 0.0022 0.0021 0.0039 0.0043 0.0020

R2 0.14 0.11 0.19 0.07 0.05 0.22 0.16 0.18 0.04 0.07 0.16 0.20

F-Test 4.25***

3.13***

4.25***

3.64***

1.46 4.79***

4.91***

5.80***

0.99 1.07 2.15**

6.54***

Table 10: Genoa Interest Rate Monthly Average Percentage Changes

Barcelona Bruges Florence Paris Venice

Jan 1.85 -2.78* 5.97

** -1.10 -0.83

Feb -0.75 -3.87**

-4.08* -2.77

* -2.55

Mar 0.84 2.82* 0.09 1.91 2.66

Apr -0.29 1.57 4.10* 0.68 1.05

May -1.50 0.89 -4.49* 1.58 -0.21

Jun 2.86 2.27 0.95 -1.79 3.23

Jul 4.09* 0.19 5.66

** 1.99 -3.37

Aug 3.40 0.73 2.12 5.56***

-4.46*

Sep 1.13 0.60 -3.89 2.40 9.67***

Oct -4.98**

0.26 -1.46 -3.59**

-0.45

Nov -8.32***

-2.22 -5.51**

-2.45 -4.88**

Dec 0.83 -0.53 -0.41 -1.29 0.00

STDERR 0.025 0.017 0.024 0.016 0.023

R2 0.10 0.07 0.09 0.16 0.13

F-Test 1.9453**

1.3542 2.4939***

2.4914***

2.7401***

25

Table 11: London Exchange Rate Monthly Average Percentage Changes

Bruges Genoa

Jan -0.81 -0.83

Feb 0.50 -0.05

Mar 0.07 -0.48

Apr 0.31 -0.16

May 0.67 1.36**

Jun 1.30**

1.72***

Jul 0.78 0.87

Aug 0.04 0.99

Sep 0.13 -0.17

Oct -1.60***

-2.10***

Nov -1.20**

-1.30**

Dec 0.15 -0.10

STDERR 0.0051 0.0064

R2 0.20 0.21

F-Test 2.53***

2.79***

Table 12: London Interest Rate Monthly Average Percentage Changes

Bruges Genoa

Jan -7.39**

-3.46

Feb 2.91 2.43

Mar -2.15 -1.44

Apr 0.00 -1.54

May 2.62 2.18

Jun 2.16 2.67

Jul 2.55 1.55

Aug 6.31* 5.57

*

Sep 7.98**

6.61**

Oct -3.61 -2.95

Nov -8.46**

-7.73**

Dec -2.87 -2.35

STDERR 0.037 0.030

R2 0.17 0.13

F-Test 1.7123* 1.5453

26

Table 13: Paris Exchange Rate Monthly Average Percentage Changes

Avignon Bruges Genoa Montpellier Venice

Jan -0.60**

-0.11 -1.52***

-0.66***

-0.93**

Feb -0.22 0.04 -0.35 -0.13 -0.45

Mar 0.21 0.22 -0.20 0.02 -0.32

Apr 0.31 -0.04 0.48 0.18 0.50

May 0.81***

0.07 1.54***

0.94***

0.87**

Jun 0.32 0.23 0.90***

0.36 0.26

Jul -0.46* -0.67

*** -0.43 -0.55

** -0.05

Aug -0.09 -0.05 -0.01 -0.13 0.44

Sep 0.17 0.76***

0.29 0.12 0.26

Oct 1.18***

1.04***

1.16 1.18***

1.58***

Nov -1.09***

-0.87***

-1.27***

-1.10***

-1.59***

Dec -0.14 -0.12 0.10***

-0.18 -0.17

STDERR -0.0060 -0.0011 -0.015 -0.0066 -0.0093

R2 0.30 0.30 0.31 0.33 0.26

F-Test 5.89***

6.06***

6.42***

6.94***

4.75***

Table 14: Paris Interest Rate Monthly Average Percentage Changes

Bruges Genoa Venice

Jan -2.67 -5.82***

-2.73

Feb -1.94 -6.99***

-3.54

Mar 0.30 -2.95 -1.53

Apr 1.48 1.21 2.23

May -0.71 8.28***

5.87**

Jun 7.13***

8.47***

4.31*

Jul -1.34 -2.61 -3.10

Aug -1.96 -0.71 -0.39

Sep 1.12 -0.91 -0.29

Oct 6.83***

7.28***

5.88**

Nov -2.45 -3.23* -5.70

**

Dec -6.54***

-1.82 0.54

STDERR 0.023 0.019 0.023

R2 0.16 0.34 0.20

F-Test 2.6904***

7.2731***

2.3932***

27

Table 15: Venice Exchange Rate Monthly Average Percentage Changes

Barc-

elona Bologna Bruges Florence Genoa Lucca Milan Paris Pisa

Jan -0.16 -0.09 0.43 -0.28 -0.33 0.05 0.98 0.11 -0.18

Feb -0.40 -0.17 -1.04***

-0.51***

-0.37* -0.59

** -0.86 -1.05

*** -0.48

**

Mar -0.52 0.09 -0.25 0.19 0.07 0.20 -1.19 -0.33 0.08

Apr -0.68* -0.02 -0.09 0.09 0.10 0.24 0.56 0.33 0.02

May -0.01 -0.21 0.15 -0.03 0.61***

0.09 0.22 -0.12 -0.04

Jun 0.60 -0.03 1.03***

0.55***

0.66***

0.47* 1.15 0.65

* 0.71

***

Jul 1.27***

0.44***

0.79**

0.73***

0.85***

0.88***

0.36 1.18***

0.91***

Aug 0.14 0.32**

0.09 -0.14 -0.04 -0.34 -0.03 0.27 0.01

Sep -0.81**

-0.53***

-0.41 -1.45***

-1.02***

-1.21***

-0.84 -0.36 -1.35***

Oct 0.33 -0.04 1.04***

0.50***

0.30 0.27 0.39 0.20 0.39**

Nov -0.18 0.11 -0.80**

0.15 -0.11 -0.27 -0.04 -0.90**

0.01

Dec 0.79**

0.22 -0.05 0.35**

0.00 0.18 0.74 -0.05 0.00

STDERR 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

R2 0.19 0.11 0.22 0.29 0.17 0.29 0.08 0.21 0.29

F-Test 2.40***

2.95***

3.02***

10.37***

5.06***

4.18***

0.79 2.88***

8.43***

Table 16: Venice Interest Rate Monthly Average Percentage Changes

Barcelona Bruges Florence Genoa

Jan -3.40 -1.43 2.57 -4.00

Feb -0.11 0.43 -7.53***

0.99

Mar 3.23 0.48 3.90 -2.83

Apr -4.18**

1.51 0.16 -3.25

May 1.00 1.67 -2.93 1.57

Jun 3.49* 2.47 -3.73 -1.25

Jul 2.79 1.62 14.04***

8.56***

Aug 2.92 2.06 12.58***

8.16***

Sep -2.71 -3.40**

-18.99***

-5.69**

Oct 0.30 1.09 0.65 0.55

Nov -2.53 -2.91* -5.35

** -2.79

Dec 1.34 -4.29***

4.89***

0.51

STDERR 0.021 0.016 0.027 0.024

R2 0.16 0.16 0.35 0.15

F-Test 1.7223* 1.9956

** 10.122

*** 3.1821

***

28

Figures 1 and 2: Barcelona

93

94

95

96

97

98

99

100

101

102

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Barcelona Exchange Rates

Avignon

Bruges

Florence

Genoa

Majorca

Montpellier

Pisa

Venice

0%

5%

10%

15%

20%

25%

30%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Barcelona Interest Rates

Bruges

Florence

Genoa

Venice

29

Figures 3 and 4: Bruges

95

96

97

98

99

100

101

102

103

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Bruges Exchange Rates

Barcelona

Genoa

London

Paris

Venice

0%

5%

10%

15%

20%

25%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Bruges Interest Rates

Barcelona

Genoa

London

Paris

Venice

30

Figures 5 and 6: Florence

96

97

98

99

100

101

102

103

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Florence Exchange Rates Avignon

Barcelona

Bologna

Bruges

Genoa

London

Lucca

Montpellier

Paris

Pisa

Rome

Venice

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Florence Interest Rates

Barcelona

Genoa

Venice

31

Figures 7 and 8: Genoa

94

95

96

97

98

99

100

101

102

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Genoa Exchange Rates Avignon

Barcelona

Bologna

Bruges

Florence

London

Montpellier

Paris

Pisa

Rome

Valencia

Venice

0%

5%

10%

15%

20%

25%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Genoa Interest Rates

Barcelona

Bruges

Florence

Paris

Venice

32

Figures 9 and 10: London

96

97

98

99

100

101

102

103

104

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

London Exchange Rates

Bruges

Genoa

0%

5%

10%

15%

20%

25%

30%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

London Interest Rates

Bruges

Genoa

33

Figures 11 and 12: Paris

95

96

97

98

99

100

101

102

103

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Paris Exchange Rates

Avignon

Bruges

Genoa

Montpellier

Venice

0%

5%

10%

15%

20%

25%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Paris Interest Rates

Bruges

Genoa

Venice

34

Figures 13 and 14: Venice

96

97

98

99

100

101

102

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Venice Exchange Rates

Barcelona

Bologna

Bruges

Florence

Genoa

Lucca

Milan

Paris

Pisa

0%

5%

10%

15%

20%

25%

30%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Venice Interest Rates

Barcelona

Bruges

Florence

Genoa

Moore, Tony Section name

Figure 15: R2 values for FX rate currency pairs

Figure 16: R2 values for interest rate currency pairs

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

FLO

VEN

PA

RG

EN

PA

RA

VI

VEN

FLO

PA

RV

EN

BR

ULO

N

BR

UG

EN

GEN

LON

VEN

PA

R

GEN

VEN

GEN

BO

L

FLO

LUC

FLO

MO

N

FLO

BR

U

GEN

VA

L

FLO

LON

GEN

AV

I

BA

RFL

O

BA

RM

AJ

BA

RV

EN

GEN

BA

R

FLO

BA

R

VEN

MIL

GEN

RO

M

FLO

GEN

GEN

FLO

GEN

PIS

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

VEN

FLO

PA

RG

EN

BR

ULO

N

BR

UV

EN

BR

UG

EN

BA

RB

RU

BA

RG

EN

PA

RV

EN

BA

RV

EN

BA

RFL

O

BR

UP

AR

LON

BR

U

VEN

BA

R

PA

RB

RU

VEN

BR

U

GEN

PA

R

VEN

GEN

FLO

BA

R

GEN

VEN

LON

GEN

BR

UB

AR

GEN

BA

R

FLO

VEN

GEN

FLO

GEN

BR

U

FLO

GEN

Moore, Tony Section name

Figures 15 and 16: Average exchange and interest rates

95

96

97

98

99

100

101

102

103

104

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AverageER

Barcelona

Bruges

Florence

Genoa

London

Paris

Venice

0

0.05

0.1

0.15

0.2

0.25

0.3

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

AverageIR

Venice

Paris

London

Genoa

Florence

Bruges

Barcelona

Moore, Tony Section name

Table 19: Correlation Matrix of Average Monthly FX Rate Percentage Changes at Each City

Barcelona Bruges Florence Genoa London Paris Venice

Barcelona 1

Bruges 0.569892 1

Florence 0.896715 0.239726 1

Genoa -0.31308 -0.59639 0.085335 1

London 0.753716 0.808566 0.526228 -0.57176 1

Paris 0.795836 0.473279 0.634902 -0.51255 0.582918 1

Venice 0.050706 0.618025 -0.3485 -0.86301 0.470435 0.303704 1

Table 20: Correlation Matrix of Average Interest Rate Monthly Average Percentage Changes at

Each City

Barcelona Bruges Florence Genoa London Paris Venice

Barcelona 1

Bruges 0.779502 1

Florence 0.609398 0.872481 1

Genoa 0.437292 0.757293 0.875618 1

London 0.752911 0.901564 0.946008 0.848315 1

Paris 0.820399 0.773659 0.699456 0.560041 0.731513 1

Venice 0.453342 0.793303 0.757084 0.740599 0.743268 0.479491 1