Post on 02-Oct-2020
International Finance 15:2, 2012: pp. 225–250
DOI: 10.1111/j.1468-2362.2012.01303.x
Monetary Policy Announcementsand Short-Term Interest Rate
Futures Volatility: Evidence fromthe Mexican Market∗
Renata Herrerıas† and Pedro Gurrola‡†School of Business Administration, ITAM, Mexico, D.F., Mexico and
‡Regent’s College, European Business School, London
Abstract
The relationship between monetary policy and the behaviour of financialmarkets is commonly examined to assess the effectiveness of the actions ofcentral banks. Our study explores, for the case of Mexico, the reaction ofshort-term interest rate futures to monetary policy announcements, andto what extent the change to the operational targeting of interest ratesimplemented by the central bank in 2004 led to changes in this reaction.The results show that in a market dominated by institutional investorstrading solely for hedging purposes, the actions of the central bank are notfully incorporated into prices in advance. As a result, interest rate futuresprices are adjusted on announcement dates. Furthermore, the change to an
∗We thank the valuable suggestions made by two anonymous referees and the editor BennSteil. We are also grateful to Manuel Ramos Francia, Guillermo Benavides and the seminarparticipants at Banco de Mexico on 17 July 2009; Ai Jun Hou for her discussion at the 2010Eastern Finance Association Annual Conference; and Janko Hernandez for helpful discussionsand comments. Renata Herrerıas gratefully acknowledges the financial support of AsociacionMexicana de Cultura A.C.
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226 Renata Herrerıas and Pedro Gurrola
interest rate operational target modified the trading behaviour so that thechanges in the volatility, volume and prices of the futures contracts on theannouncement dates are now more evident.
I. Introduction
A common view among researchers is that the quality and the effectivenessof monetary policy can be assessed by studying the way in which the financialmarkets respond to the central bank’s actions. If the objective of the monetary au-thority is to maintain economic stability by conducting an effective and smoothmonetary policy, then their actions should be in line with the general marketexpectations (Poole and Rasche 2000). The reaction to central bank actions hasbeen assessed for the United States and other economies and for several instru-ments. Examples of this line of research include the following studies: Bernankeand Kuttner (2005), which explained the US stock market reaction to FederalReserve policy; Kuttner (2001), which evaluated the reaction of market interestrates to the expected and unexpected components of the target rate changes;Armesto and Gavin (2005), which studied the response of commodity futuresto expected inflation and the real interest rates; and Bernoth and von Hagen(2004), which assessed the effects of the European Central Bank (ECB) policyannouncements on the volatility of Euribor futures rates. Studies that evaluatethe response of market interest rates to monetary policy actions show that marketexpectations are set using interest rate futures prices when available. In general,interest rate futures have proved to be unbiased predictors for future spot in-terest rates in the United States and the Eurozone (Krueger and Kuttner 1996;Bernoth and von Hagen 2004); therefore, it has been possible to independentlyassess the market reaction to the expected and unexpected actions of centralbanks.
The aim of this study is to contribute to the literature by presenting evidence ontwo different aspects of the relationship between monetary policy and financialmarkets in Mexico. The first aspect is related to the reaction of short-term interestrate futures to monetary policy announcements in a market that is dominated byinstitutional investors with passive investment strategies. We analyse this reac-tion by looking for changes in futures price volatility during the announcementdates. The second aspect addresses whether changes in the transmission mech-anism for monetary policy intentions modified the behaviour of interest ratefutures.
To explore both questions, we use the volatility and the trading volume ofthe 28-day interbank-rate futures contracts (TIIE futures), which are traded onthe Mexican Derivatives Exchange (MexDer). The contract has been tradingfor over a decade, and is the most liquid contract on the exchange and the
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most commonly used exchange-listed derivative in Mexico. The sole traders areinstitutional investors, primarily banks and pension funds, both national andinternational, and because of the heavy regulation they face and the limitationson their risk-bearing beyond their core businesses, the trading is primarily forhedging purposes. We select this instrument because the TIIE futures could playa similar role to that of the Fed funds futures in the US market, which are used asa proxy for market expectations about future interest rates. If interest rate futuresare a good proxy for market expectations in mature markets where investors cananticipate central bank policies, no change in volatility should occur during thepolicy releases when the central bank actions are correctly predicted. However, inthose mature markets, interest rate futures are widely used for both speculationand hedging purposes, which is not the case in the Mexican futures market. Thisdifference appears to define the particular response of TIIE futures to centralbank actions.
Regarding the second aspect, since the federal authorities granted fullindependence to the Mexican central bank (Banco de Mexico) in the mid-1990s, the operating target and the transmission mechanisms have been evolv-ing to better suit the economic environment, moving towards more transparentand more consistent mechanisms. Our findings show that when the opera-tional target is the balance of the central bank’s overdrafts, interest rate futuresbarely react to monetary announcements, as opposed to the reaction whenthe operational target is the overnight funds interest rate. For the latter, wefind that futures volatility tends to increase with announcements. The previ-ous studies do not address the implication of using a particular transmissionmechanism and are not able to review how the financial markets utilize thenew signals provided by the central bank because all of the studies apply toeconomies where the sole transmission mechanism is the definition of the targetrate.
The choice of the Mexican case is dictated by several factors. In addition to itsrelevance to global institutional investors, Mexico serves as a good example ofthe differences that arise from the use of distinct operating targets. In 1995, thecentral bank introduced an operational framework to send qualitative signalsto the market without determining interest rate levels. The bank transmittedits policy intentions by establishing a target level for banks’ current accountbalances at the central bank and penalizing banks with negative balances inthose accounts. As economic conditions became much more favourable, thecentral bank gradually began the transition towards achieving an interest rateoperational target and a more transparent monetary policy signalling mech-anism. Since April 2004, the central bank has announced its target for theovernight interbank rate together with the target level of banks’ current ac-count balances at the central bank at market rates. In January 2008, the levelof banks’ current account balances was definitively replaced by the overnight
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interbank interest rate as the operating target. Another important change was thedecision in 2003 to define pre-established dates to announce the monetary policystance.
Mexico is not the only case of an emerging market that is shifting mone-tary policy regimes towards inflation targeting and market-oriented operatingmechanisms. In the years between 1997 and 2004, countries including Peru,Brazil, Korea and the Czech Republic adopted an inflation-targeting mone-tary framework. By 2005, some form of inflation targeting had become themost common monetary policy regime in emerging markets. Similar to Mexico,some of these countries in addition to changing their monetary policy regimesalso changed their operating mechanisms, shifting from conducting opera-tions that were aimed at controlling the money supply to interest rate-targetingmechanisms.
We use a FIGARCH-type model to estimate the futures price volatility inTIIE futures contracts with monthly maturities of up to six months forwardand include a dummy variable signalling the policy announcement dates. Ingeneral, our results show that the announcements increase futures price volatil-ity and induce adjustments in interest rate levels even when the policy an-nouncement maintains unchanged current conditions. Regardless of the typeof announcement, there is an interest rate adjustment on the day of the an-nouncement. Furthermore, the reaction to policy announcements depends onthe mechanism that is used to transmit the intentions of the central bank.During the time period when interest rate volatility was high and the cen-tral bank did not provide information about target interest rates, the volatil-ity of futures prices did not increase with the announcements. However, oncethe central bank started to communicate its target for the overnight rate andchanged the operational target the volatility began to increase on policy an-nouncement dates, and there was significant futures price movement. Althoughthis behaviour appears to be counterintuitive, we have to consider the par-ticular characteristics of the derivatives market in Mexico. When the centralbank did not set the target level for interest rates, institutional investors werenot compelled to change their positions in futures contracts; this is reason-able if one considers the transaction costs of rolling over contracts. With thecentral bank providing timely information about interest rates, institutional in-vestors are forced to adjust their positions to reflect the new level of interestrates.
The remainder of this article is organized as follows. The next section contains areview of previous studies. Section III provides the background that is necessaryto understand the transmission of Mexican monetary policy over the past 15years. In Section IV, the main characteristics of the Mexican interest rate futuresmarkets are reviewed. The data are presented in Section V. Section VI presents the
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analysis, and the obtained results are discussed. Some final remarks are providedin the last section.
II. Previous Studies
There is a consensus in the literature about the relevance of understanding therelationship between financial markets and monetary policy given that cen-tral banks achieve monetary policy aims by affecting asset prices and returns(Bernanke and Kuttner 2005). The most widely studied relationship has beenthat of monetary policy announcements and the subsequent reaction of mar-ket interest rates. Since the study by Cook and Hahn (1989), who examinedhow yields on Treasury securities reacted to changes in the target Fed fund ratebetween 1974 and 1979, several studies have followed the same direction, forexample, Radecki and Reinhart (1994), Roley and Sellon (1995), Reinhart andSimin (1997), Thornton (1998) and Poole and Rasche (2000). These studiesdeveloped further analyses that either focused on more recent periods, othermarkets, or introduced improved specifications or techniques. A consistent re-sult emerged from this literature: interest rates systematically respond to policyactions or policy-related information, implying that these actions are not beingfully anticipated.
Roley and Sellon (1998a) and Kuttner (2001) pointed to the need to distin-guish between the expected and the unexpected elements of monetary policyannouncements. Kuttner argued that bond yields in forward-looking marketsshould respond to anticipated elements of monetary policy differently than tounanticipated elements. If the market anticipates much of the target changesoccurring on day d, then those expectations would have been incorporated intothe long-term rates on day d − 1. Therefore, little change should be observed onthe day of the target change. However, a surprise in the target rate will lead to achange in the long-term rates.
After their introduction in the Chicago Board of Trade (CBOT) in 1989, 30-day Federal funds futures contracts became popular as predictors of the Fed’schanges in target rates. The question of the ability of the Federal funds futuresrates to forecast the funds target rate and, by extension, the short-run movementsin monetary policy was initially considered by Carlson et al. (1995). Krueger andKuttner (1996) presented a more elaborate analysis and concluded that the Fedfunds futures market is very good at anticipating changes in the target fund rate.Robertson and Thornton (1997) noted the difficulties arising from the fact thatthe Fed funds futures rate forecasts the fund’s effective rate and not the target rate,but the authors still agreed as to the usefulness of the Federal funds futures rate asa predictor of whether the Fed will change its target. Based on these conclusions,Kuttner (2001) used the spot-month 30-day Federal funds futures contracts as a
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230 Renata Herrerıas and Pedro Gurrola
measure of the expected Fed policy to isolate the unanticipated component of thetarget change. Under this perspective, the changes in the futures rate on day d areused as a measure of the unexpected change in the target rate on day d. Regressingthe change in the interest rate on the unexpected and expected componentsof the target rate change, Kuttner found a small and statistically insignificantresponse to the anticipated component, while the response to the unanticipatedcomponent was large and highly significant. In fact, for the surprise component,the coefficients obtained were larger than those reported by Cook and Hahn(1989).
The research has expanded to include other central banks and markets, andit has also tended to support the view that as transparency and the markets’understanding of policy have increased over the years, the accuracy of the mar-ket’s forecasts of the central bank’s actions has improved. For example, Haldaneand Read (2000) found that the introduction of inflation targeting in the UnitedKingdom in 1992 appears to have had a dampening effect on the yield curveresponses in the short term, which is consistent with the idea that inflation tar-geting brings greater transparency to the monetary regime. Gaspar et al. (2002)examined the impact of the ECB policy decisions on the level and volatility of thedaily overnight interbank rate (EONIA) using a GARCH model. They concludedthat market participants were able to predict the ECB’s interest rate decisionsquite accurately.
An example for an emerging economy is Larraın (2005), which studied theeffects of monetary policy surprises on long-term inflation-linked interest rates inChile. He used the forward curve data to separate changes in the anticipated andunanticipated components of the monetary policy rate, and controlled for thefeedback relationships between monetary actions and market interest rates. Hisresults indicated that the unanticipated monetary policy rate had a significantpositive impact on market interest rates, with the largest effect on rates withshorter maturities. Finally, Bernoth and von Hagen (2004) analysed three aspectsof the predictability of interest rates in the European Monetary Union (EMU):the efficiency of the Euribor interest rate futures market, the impact of monetarypolicy announcements on the volatility of Euribor futures rates and the effectof ECB policy announcements on the prediction error contained in Euriborfutures rates. They found that Euribor futures rates with a forecast horizon of upto four months are unbiased and efficient predictors of future spot rates and thatthe patterns in volatility indicated that market participants correctly anticipatedthe direction of the interest rate changes intended by the ECB, but that therewas uncertainty about the timing. During the first five years of the EMU, theaverage volatility of Euribor futures rates on council days was significantly largerthan on non-council days. Finally, they found that the information released bythe governing council meetings did not improve the market’s ability to forecastinterest rates.
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III. The Mexican Central Bank’s Monetary Policy TransmissionMechanism
A. The ‘Corto’
Since the early 1990s, the monetary transmission mechanism in Mexico hasundergone important changes. After the 1995 financial crisis that brought astrong depreciation of the currency, high inflation, and a severe deterioration inthe real economy, the central bank considered it inadvisable to use target interestrates as an operational mechanism. The bank argued that it was not convenientto determine a specific level of interest rates when short-term interest rates werethe only reference rates in the money market; too low a rate would encouragelending and higher inflation, while a high rate would exacerbate the problemsfaced by borrowers and commercial banks.
In 1995, the central bank introduced an operational framework to send qual-itative signals to the market without determining interest rate levels. The frame-work included a reserve requirement with an average of approximately zeroreserves over a 28-calendar-day maintenance period. Under this scheme, thecentral bank did not remunerate positive settlement balances nor did it chargefor overdrafts posted at the end of each day in the commercial banks’ currentaccounts balances at the central bank. Instead, it charged a penalty rate at theend of the maintenance period if the cumulative balance of the daily positive andnegative balances was negative. The high level of the penalty, which was twicethe overnight interbank rate, was intended to be an incentive for the banks toend the maintenance period with a zero cumulative balance, making the net costof the end-of-period negative cumulative balances similar to the cost of holdingend-of-period positive cumulative balances. The transmission of monetary pol-icy under this framework involved providing or withdrawing liquidity at marketrates so that the banks’ current accounts at the central bank equalled zero atthe end of the measurement period. To maintain a restrictive policy, the centralbank announced a negative balance target and, for an accommodative monetarypolicy, a positive balance target.
By 1998, once the major difficulties posed by the crisis had been overcome, thecentral bank started to signal a bias towards a restrictive monetary policy stancethrough a negative overdraft target on the cumulative balance of the commercialbanks’ current accounts (the monetary policy instrument known as ‘el corto’).When the negative balance target is used, the central bank provides all of theliquidity needed by the financial system. However, part of this liquidity, withrespect to the size of the ‘corto’, was provided at the penalty rate. This actionpressured interest rates upwards as banks attempted to obtain funds through theinterbank market to avoid paying the penalty rate. An increase in the ‘corto’ wasthus interpreted as a signal of a tighter monetary policy, while its reduction was
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232 Renata Herrerıas and Pedro Gurrola
observed as a more neutral stance, even though the level of the ‘corto’ was notzero.
B. Transition to an Interest Rate Operating Target
Since 2001, the Mexican central bank’s monetary policy has been conductedexclusively under an inflation-targeting framework. In 2002, an annual inflationrate of 3% was defined as the long-term inflation target with a variability intervalof plus/minus one percentage point. The introduction of inflation targeting bythe central bank has been associated with a major break in the transmissionmechanism; since then, the level and volatility of inflation have not only declined,but the degree of inflation persistence has fallen (Capistran and Ramos-Francia2006), and inflation has switched from a non-stationary to a stationary process(Chiquiar et al. 2007).
Once stability in the financial markets and a low level of inflation were attained,the exclusive use of the ‘corto’ to signal the monetary policy stance became lessappropriate. In an environment of stable inflation, the desired level of interestrates needs to be specified more clearly. For this reason, and to strengthenmonetary policy implementation, in 2003 the central bank began the gradualprocess of adopting an operating interest rate target and decided to announceits monetary policy stance on pre-established days. In April 2004, in addition tothe level of the ‘corto’, the bank also started to communicate specific levels ofinterest rates (euphemistically called ‘monetary conditions’). Through its pressreleases, the central bank’s signalling of an adjustment in monetary conditionsled to a precise and stable adjustment of the overnight interbank rate. Since 2004,the market has functioned according to the interest rate signalled by the centralbank. In fact, the last change in the overnight interbank rate associated withthe ‘corto’ took place in February 2005. This smooth transition to an operatinginterest rate target concluded in January 2008 when the ‘corto’ was definitivelyreplaced by an operating target for the interbank overnight rate.
IV. The TIIE Rate and Its Futures Contracts
A. Structural Breaks in the TIIE Spot Rate
Since March 1996, the Mexican central bank has determined and published theshort-term interest rate benchmark known as Tasa de Interes Interbancario deEquilibrio, or TIIE. This rate is the measure of the average cost of funds in theMexican interbank money market and is based on quotations submitted dailyby full-service banks. The participating institutions submit their quotes by noon(Mexico City time), and the central bank determines the TIIE as the weighted
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Figure 1: Twenty-eight day TIIE spot rate (1 January 2001 to 31 December 2009)
Note: The graph presents the daily TIIE spot rate in percentage.
average between the bid and ask quotes. The rates quoted by the institutions thatparticipate in the survey are the actual bids and offers by which these institutionsare committed to borrow from or lend to the central bank.
Figures 1 and 2 show the evolution of the TIIE spot rate and its volatility(measured as the absolute value of daily changes) from January 2001 to the endof 2009, respectively, where the different phases of the TIIE spot rate behaviourcan be identified. The most striking observation is the contrast between theperiod of high volatility that prevailed throughout the first half of 2004, duringwhich movements of almost 150 bps within very short periods (two weeks)occurred, and throughout the later period, during which the TIIE spot ratestabilized. In fact, after June 2006 and until the end of 2008, when the globalfinancial crisis intensified, the volatility of the TIIE changes remained close tozero. The most likely explanation for the abrupt decrease in volatility is related tothe changes in the policy transmission mechanism explained above. Since April2004, the central bank has communicated the overnight funds target rate, whichhas resulted in a progressive reduction in short-term interest rate volatility.1 Toconfirm the regime change in short-term interest rates, we perform a Bai-Perron
1It is worth mentioning that the last time the central bank modified the target interest rate wason 17 July 2009, decreasing it by 25 basis points to 4.5%. The rate was gradually reduced startingin January 2009.
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234 Renata Herrerıas and Pedro Gurrola
Figure 2: Twenty-eight day TIIE spot rate volatility (1 January 2001 to 31 December 2009)
Note: The graph presents the volatility of the TIIE spot rate measured as the absolute valueof daily log rate changes.
(1998) test for structural breaks for the spot TIIE and for each of the futures rateseries to establish the precise date of the expected structural break in the dataused for this research. The test shows that a structural break is present towards thesecond half of May 2004 for all data series. The argument of a regime switch wasalso supported by the work of Benavides and Capistran (2009). They consideredthe daily risk-free interest rate in the Mexican secondary market calculated fromMexican government bonds (CETES) between 4 November 1998 and 29 August2008. Their results show one structural break in the squared returns on 12 May2004.
B. TIIE Futures Contracts
The TIIE futures contract, which is listed on the MexDer, is the most tradedfutures contract in Mexico. The contract settles to 28-day deposits that produceyields at the 28-day TIIE. Each (28-day) TIIE futures contract covers a face valueof 100,000 Mexican pesos (approximately US$8,000). MexDer lists and makesavailable for trading series of the TIIE futures contracts on a monthly basisfor up to ten years. It is important to observe that, in contrast with analogousinstruments such as Chicago Mercantile Exchange’s eurodollar, TIIE futuresquotes are in terms of future yields, not in terms of prices. The relationship
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between the quoted future yield on day t and the corresponding futures price Ft
is determined by the formula
Ft = 100, 000
1 + Yt (28/360), (1)
where Yt is the quoted yield.
V. Sample Data
The analysed period begins in May 2001 and ends in December 2009. Thisperiod has several characteristics that make it unique for the purposes of thisstudy. First, the TIIE futures contract was initially listed in 1999, and it tooka couple of years to reach reasonable trading volume levels. As of May 2001,the TIIE futures contract began to be the most traded futures contract on theMexDer, and eventually it became one of the three most traded futures contractsin the world (Holz 2007). Furthermore, 2001 is the year when the Mexican centralbank started to conduct its monetary policy exclusively under an inflation targetframework. Second, the proposed period covers the transition between the twomonetary policy transmission mechanisms; that is, it includes the time whenthe central bank commenced releasing its target rate for overnight funds and thetime when it completely eliminated the target balance procedure. As explainedpreviously, over those years, it is possible to observe the regime switch in thevolatility of the short-term interest rates market that is attributed to the twodifferent transmission mechanisms. Finally, together with the fact that the targetrate has remained constant since June 2009, the trading volume of the TIIEfutures contract declined again in 2009 when the MexDer introduced interestrate swap futures contracts.
A. Monetary Policy Announcements
Between May 2001 and December 2009, the Mexican central bank released 92policy announcements, of which 51 left monetary conditions unchanged, 23announced the contraction of monetary conditions and 18 announced theirexpansion. Before 2003, there were no specific release dates for monetary policyannouncements; for example, there were only three policy announcements in2001 and four in 2002. Beginning in 2003, the third Friday of each month was setas the predetermined date to communicate monetary intentions, but the centralbank maintained the right to release further announcements if necessary. In 2003and 2004, there were 14 announcements, two more than the expected number foreach year and, in every case, the unexpected announcement served to correct theprevious decision. For example, in 7 February 2003, the central bank contracted
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236 Renata Herrerıas and Pedro Gurrola
the monetary policy stance only 14 days after the previous release, which had leftthe monetary conditions unchanged. The same occurred on 27 April 2004, justone trading day after the previous announcement, when the central bank wasforced to contract monetary conditions due to an undesirable reduction in theinterbank rates within those dates.
Despite the fact that the Mexican central bank started to set the target for theovernight interest rates in April 2004, the policy press releases in 2004 did notcontain explicit information about target interest rates. During 2005, the fourreleases that mentioned interest rates announced the desired change in the targetrate, but the actual target level was not mentioned. Since January 2006, everypress release contained the desired change in the interest rate target, and startingin January 2008, all releases explicitly mention the target level of interest rates.
B. Futures Rates Data
The futures rates data used in this study are those provided by the MexDer, andthey include a total of 2,188 trading days from 2 May 2001 to 31 December2009. For each trading day, we consider six observations corresponding to thedaily settlement rates of each of the 28-day TIIE futures contracts expiring everymonth, from the next-to-expiration contract to the contract expiring five monthsahead. All of the rates are transformed into continuous compounding form towork with a smoother time series and to facilitate comparisons with centralbank policies. Once the rates are in continuous compounding form, we obtainthe daily rate changes
YCCt = 360
28× ln
(1 + Yt
360× 28
), (2)
rt = (YCCt − YCCt−1) × 10,000, (3)
where YCCt is the equivalent continuous compounding rate of the settlementrate Yt and rt is the rate change expressed in basis points.
To avoid the problem of the limited lifespan of individual futures contracts, apanel is created by rolling over the contracts; once the most immediate contractreaches maturity, we rollover each of the rate change series to the contract that isnext according to maturity. The result of this procedure is a panel consisting ofsix rollover series defined according to time to maturity. The first series (Series 1)contains the rate changes for the most immediate contract, the second containsthe rate changes for the contract that will expire between two and one monthsahead, the third contains the rate changes for the contract with an expirationdate between three and two months ahead, and so on.
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According to the structural break detected in the series, which approximatelycoincides with the time when the central bank introduced the overnight fundsrate target as its main instrument for monetary policy, the analysis also considerstwo separate sub-periods. The first sub-period begins on 2 May 2001, and endson 14 May 2004 (766 observations), and the second one runs from 17 May 2004to 31 December 2009 (1,422 observations), for a total of 2,188 observations.
Panels A–C in Table 1 provide the summary statistics of each series of ratechanges for the whole sample and for the first and second periods, respectively.For the whole sample, all of the means are negative and significantly differentfrom zero, and the standard deviations are higher for the contracts that are closerto expiration (Panel A). In the first sub-period (Panel B), all of the means aresignificantly different from zero and higher than the whole-sample mean returns;the standard deviation is higher as well and also tends to increase when thecontracts are closer to expiration. However, in the second sub-period (Panel C),only the first contract mean is significantly different from zero, the volatility hasmuch smaller values, and it decreases as the contracts approach expiration. Theoverturn of the volatility patterns confirms the change in the market conditions;as the monetary policy implemented in April 2004 removes the uncertainty inshort-term interest rates and as inflation progressively stabilizes, the long-termcontracts tend to show higher volatility with respect to the short-term contracts.
All of the series are leptokurtic; some of them have negative skewness and,according to the Bera–Jarque statistic, are far from being normally distributed.The non-normal behaviour tends to be stronger for the nearby contracts andover the second sub-period of time. The Engle (1982) LM-test for ARCH ef-fects clearly rejects the null hypothesis of no-ARCH effects, and the Ljung–Box(1978) portmanteau test shows 20th-order serial correlation in the standardizedresiduals.
VI. Results and Discussion
A. The Effect of Monetary Policy Announcements on TIIE FuturesVolatility
Considering the properties of the data, we use a FIGARCH (p, d, q) specificationthat is proposed by Baillie et al. (1996) to model the volatility of the TIIE futuresrates. The FIGARCH process, which nests both GARCH and IGARCH models,has proved to be more suitable for modelling the propagation of volatility shocksin financial series because it allows for a slow hyperbolic rate of decay for theautocorrelations of squared errors. There is plenty of evidence that shows that thevariance of returns for risky securities such as stocks, long-term options or futurescontracts follow long-memory processes and therefore are better modelled with
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238 Renata Herrerıas and Pedro Gurrola
Table 1: Statistics for the TIIE Futures Returns for the Whole Sample andSub-Periods
StandardSeries Mean deviation Skewness Kurtosis JB Q(20) LM(20)
Panel A: Whole period May 2001 to December 2009 (2,188 observations per series)1 −1.23 ∗∗ 253.3 −1.14 120.64 1,327,290.5 ∗∗ 154.4 ∗∗ 903.6 ∗∗
2 −0.89 ∗∗ 196.4 −1.15 49.55 224,294.5 ∗∗ 190.2 ∗∗ 725.6 ∗∗
3 −0.92 ∗∗ 163.2 0.34 23.94 52,277.5 ∗∗ 133.8 ∗∗ 497.4 ∗∗
4 −0.84 ∗∗ 170.2 0.22 18.95 32,761.6 ∗∗ 91.9 ∗∗ 297.1 ∗∗
5 −0.76 ∗∗ 181.2 0.14 24.16 53,237.4 ∗∗ 97.9 ∗∗ 274.5 ∗∗
6 −0.68 ∗∗ 160.6 0.22 21.84 43,512.0 ∗∗ 119.7 ∗∗ 146.4 ∗∗
Panel B: Sub-period May 2001 to May 2004 (766 observations per series)1 −3.01 ∗∗ 692.4 −0.52 44.34 62,789.1 ∗∗ 56.8 ∗∗ 296.6 ∗∗
2 −2.10 ∗∗ 513.6 −0.58 18.74 11,252.0 ∗∗ 79.5 ∗∗ 242.0 ∗∗
3 −2.18 ∗∗ 405.3 0.45 9.28 2,773.6 ∗∗ 54.9 ∗∗ 150.1 ∗∗
4 −1.85 ∗∗ 414.4 0.33 7.37 1,746.2 ∗∗ 43.5 ∗∗ 70.7 ∗∗
5 −1.67 ∗ 428.1 0.21 10.47 3,504.8 ∗∗ 54.1 ∗∗ 70.2 ∗∗
6 −1.41 ∗ 364.4 0.23 10.14 3,288.6 ∗∗ 69.6 ∗∗ 29.9Panel C: Sub-period May 2004 to December 2009 (1,422 observations per series)1 −0.27 ∗∗ 14.4 −0.47 26.26 40,923.9 ∗∗ 54.7 ∗∗ 200.6 ∗∗
2 −0.23 24.6 −2.12 42.73 109,239.8 ∗∗ 84.2 ∗∗ 118.3 ∗∗
3 −0.24 31.6 −0.62 27.75 45,726.3 ∗∗ 90.0 ∗∗ 209.1 ∗∗
4 −0.29 38.0 −0.52 17.23 17,650.5 ∗∗ 80.4 ∗∗ 182.6 ∗∗
5 −0.27 47.7 0.27 19.55 22,658.5 ∗∗ 85.1 ∗∗ 170.9 ∗∗
6 −0.28 50.6 0.60 17.54 18,308.3 ∗∗ 91.1 ∗∗ 184.9 ∗∗
Note: Series n consists of continuous compounding rate changes expressed in basis points for thecontract with an expiration date between n and n − 1 months ahead. JB is the Jarque–Bera statisticto test the null hypothesis of the normal distribution, LM(20) is the LM-statistic for the ARCH effectswith 20 lags, and Q(20) refers to the Ljung–Box portmanteau test for up to 20th-order serial correlationin the standardized residuals.∗Significance at 5% level.∗ ∗Significance at 1% level.
models such as the FIGARCH specification (see, for instance, Beine et al. 2002,or Kilic 2004).
Our volatility model includes a dummy variable, both in the mean and thevariance equations, which takes the value of 1 whenever there is an announce-ment. The dummy variable in the mean equation controls for any adjustment inthe interest rate levels induced by the policy announcement, while the dummyin the variance equation is the variable of interest. Positive and significant co-efficients for the dummy in the variance equation will indicate that volatilitytends to increase with monetary policy announcements. The mean equationalso includes a variable to capture the expected interest rate change in the policyannouncement, which is based on the daily average rate changes during thenon-announcement dates between monetary policy announcements. That is,
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Monetary Policy Announcements 239
the expected movement on the announcement day will be the average of thedaily rate changes using every trading day between the day after the previousannouncement and the day before the considered announcement day.2
Given that different policy actions move interest rates in opposite directions,we separately evaluate the responses to each category of release: restrictive,expansive or no change. Finally, to take into consideration the regime switch inthe data and to respond to whether the change in the operational target affectedthe way in which the investors reacted to central bank actions, we divide thesample into the first and second sub-periods. Specifically, the FIGARCH (1,d,0)model will take the following form:
rt = μ + γ1rt−1 + γ2rnt + γ3Dnt + ut (4)
and
σ 2t = α0 + β1σ
2t−1 + [1 − β1L − (1 − ϕL)(1L)d ]u2
t + γ4Dnt , (5)
where μ is a constant, rt is the logarithmic change of settlement prices on day t, rnt
is the expected change in futures interest rates on the date of the monetary policyannouncements and ut are the residuals with a mean of zero and a conditionalvariance σ 2
t . The parameter d in equation (5) is the hyperbolic rate of decayin residuals autocorrelation; the process is strictly stationary and ergodic for 0< d < 1, and shocks in volatility will have no permanent effect. When d = 0,the model reduces to a standard GARCH (1,1), and when d = 1 then the modelbecomes an IGARCH (1,1). The dummy Dn takes the value of 1 whenever there isa monetary policy announcement, with n standing for a restrictive, an expansiveor a no-change policy action. Equations (4) and (5) are jointly estimated bymaximum likelihood (ML) using the Berndt–Hall–Hall–Hausman algorithm.
A. 1. Expansion Announcements
Table 2 reports the ML parameter estimates for the FIGARCH (1,1) specificationdefined by equations (4) and (5), and the dummy variable takes the value of 1when the announcement relaxes the monetary conditions. Table 2 also presentsthe results divided by the first and the second sub-periods. When the wholeperiod is considered (Panel A), the announcement dummy coefficient in themean equation is negative and significant for all contracts, and it is positive and
2Following Kuttner (2001), most papers use a weighted average of futures prices during thecalendar month as a proxy for the expected component of central bank actions when analysingthe reaction of market spot rates. However, in our analysis, it is not possible to use the exactsame procedure because the dependent variable is precisely the change in TIIE futures rate andbecause the monetary policy announcements were not always released on pre-established dates.
C© 2012 Blackwell Publishing Ltd
240 Renata Herrerıas and Pedro Gurrola
significant for all contracts in the variance equation. This result suggests thatthe futures price volatility increases when the central bank releases informationto the market. The coefficient for the expected change in the futures rates isalso negative and significant for almost all contracts; however, the magnitudeof the coefficient tends to be lower than that from the unexpected component.The analysis for the first sub-period in Panel B shows that the price adjustmentis negative, significant and of higher magnitude, the expected component isnot always significant and, although the increases in volatility appear to alsobe of higher magnitude during the announcement, the dummy coefficients arenot always significantly different from zero. Apparently, during this period ofhigh interest rate volatility, the monetary policy announcement had less effecton the futures prices. Finally, Panel C presents the results for the second sub-period where the dummy coefficients in the mean equation are negative andsignificant and the dummy coefficients in the variance equation are positiveand significant, although of lower magnitude than those from the first sub-period. The last contract in Panel C is one of the few cases where the ratechange on announcements days is entirely captured by the expected component.Comparing the results from the first and second sub-period, it can be arguedthat using target balances as transmission mechanisms has less of an effect onfutures market trading.
A. 2. Restrictive Announcements
We now analyse the market response when the releases indicate a contractionof monetary conditions. It can be observed in Table 3, Panel A, that the ex-pected component does not always capture the futures rate adjustments on theannouncement day, while all dummy coefficients in the mean equation are pos-itive and significant. Additionally, volatility appears to move up during policyannouncement dates, although the increase is not as pronounced as that fromthe expansive announcements. During the first sub-period (Panel B), the in-creases in volatility on the announcement dates are only present in the contractsthat are closest to maturity, but all of the contracts present significant dummycoefficients in the variance equation in the second sub-period (Panel C). Onceagain, there is evidence of higher volatility on announcement dates under thenew monetary policy transmission mechanism.
Turning to the mean equation, it is worth noting that all of the coefficientsfor the dummy variables are positive and significant in all three of the panels.When restrictive news is considered, the increase in interest rates implied by thistype of announcement becomes evident and, apparently, in most cases it is notcaptured by the expected component. The adjustment occurs regardless of theanalysed sample.
C© 2012 Blackwell Publishing Ltd
Monetary Policy Announcements 241Ta
ble
2:FI
GA
RC
HE
stim
ates
for
Exp
ansi
veM
onet
ary
Pol
icy
An
nou
nce
men
ts
Seri
esμ
γ1
γ2
γ3
α0
β1×
103
dγ
4Q
(20)
P-v
alu
e
Pan
elA
1−0
.099
∗∗0.
028
−0.8
69∗∗
−4.8
96∗∗
∗1.
965
∗∗∗
0.31
3∗∗
∗0.
599
∗∗∗
137.
39∗∗
∗36
.00.
022
−0.2
08∗∗
∗0.
119
∗∗∗
−0.6
73−4
.361
∗∗3.
761
∗∗∗
0.22
5∗∗
∗0.
514
∗∗∗
267.
12∗∗
∗23
.37
0.27
3−0
.271
∗∗∗
0.15
3∗∗
∗−1
.361
∗∗∗
−5.6
45∗∗
5.36
1∗∗
∗0.
137
∗∗∗
0.45
0∗∗
∗33
5.96
∗∗∗
19.8
440.
474
−0.3
48∗∗
∗0.
145
∗∗∗
−1.1
97∗∗
∗−5
.103
∗∗∗
9.19
6∗∗
∗0.
100
∗∗∗
0.38
8∗∗
∗37
4.73
∗∗∗
29.5
960.
085
−0.4
30∗∗
∗0.
159
∗∗∗
−1.5
70∗∗
∗−5
.260
∗∗∗
5.44
6∗∗
∗0.
135
∗∗∗
0.42
8∗∗
∗28
9.87
∗∗∗
18.6
750.
546
−0.4
61∗∗
∗0.
112
∗∗∗
−1.0
18∗∗
−3.6
84∗∗
6.19
5∗∗
∗0.
135
∗∗∗
0.39
4∗∗
∗26
0.52
∗∗∗
23.3
650.
27Pa
nel
B1
−1.7
45∗∗
∗0.
175
∗∗∗
−0.1
41−3
2.06
4∗∗
∗23
.213
∗∗∗
0.52
9∗∗
∗0.
683
∗∗∗
672.
15∗∗
13.7
400.
842
−1.4
38∗∗
∗0.
190
∗∗∗
−0.4
94−3
5.10
5∗∗
∗5.
537
0.03
20.
330
∗∗∗
477.
5527
.580
0.12
3−2
.157
∗∗∗
0.18
3∗∗
∗−1
.822
∗∗−2
5.52
1∗∗
∗7.
259
−0.0
430.
357
∗∗∗
186.
6623
.066
0.29
4−2
.221
∗∗∗
0.18
0∗∗
∗−2
.024
∗∗−2
0.34
1∗∗
∗13
.325
∗∗−0
.109
0.34
4∗∗
∗78
5.82
∗∗25
.423
0.19
5−1
.991
∗∗∗
0.18
4∗∗
∗−2
.201
∗∗∗
−20.
225
∗∗∗
15.2
62∗∗
∗0.
008
0.44
0∗∗
∗82
9.82
∗∗25
.206
0.19
6−2
.236
∗∗0.
054
−1.9
66−3
1.85
6∗∗
∗21
.081
0.72
0∗∗
∗28
.08
28.3
920.
10Pa
nel
C1
−0.0
61∗∗
−0.0
88∗∗
∗−2
.106
∗∗∗
−4.3
89∗∗
∗0.
117
∗∗∗
0.14
2∗∗
∗0.
654
∗∗∗
166.
53∗∗
∗26
.410
0.15
2−0
.225
∗∗∗
0.05
5∗∗
∗−1
.188
∗∗∗
−4.2
85∗∗
∗0.
458
∗∗∗
0.25
3∗∗
∗0.
610
∗∗∗
232.
17∗∗
∗20
.830
0.41
3−0
.228
∗∗∗
0.13
7∗∗
∗−1
.427
∗∗∗
−5.7
35∗∗
0.90
3∗∗
∗0.
221
∗∗∗
0.54
0∗∗
∗22
7.67
∗∗∗
21.9
410.
344
−0.2
82∗∗
∗0.
129
∗∗∗
−1.1
24∗∗
∗−4
.492
∗∗1.
523
∗∗∗
0.20
7∗∗
∗0.
492
∗∗∗
201.
23∗∗
∗37
.564
0.01
5−0
.313
∗∗∗
0.16
0∗∗
∗−1
.552
∗∗∗
−4.9
35∗∗
∗0.
102
0.31
1∗∗
∗0.
548
∗∗∗
183.
38∗∗
∗22
.280
0.33
6−0
.301
∗∗∗
0.19
3∗∗
∗−1
.001
∗∗−2
.816
0.59
2∗∗
∗0.
257
∗∗∗
0.51
9∗∗
∗16
6.15
∗∗∗
25.9
470.
17
Not
e:T
he
tabl
ere
port
sre
sult
sfr
omth
eFI
GA
RC
Hes
tim
atio
n:
r t=
μ+
γ1r t−
1+
γ2r nt
+γ
3D
nt+
u tan
dσ
2 t=
α0+
β1σ
2 t−1+
[1−
β1L
−(1
−ϕ
L)(
1L)d
]u2 t+
γ4D
nt,
wh
ereμ
isa
con
stan
t,r t
isth
elo
gari
thm
icch
ange
ofse
ttle
men
trat
eson
day
tan
dex
pres
sed
inba
sis
poin
ts,a
nd
r ntis
the
expe
cted
chan
gein
futu
res
inte
rest
rate
sca
lcu
late
dov
ern
on-a
nn
oun
cem
ents
days
.T
he
resi
dual
su t
are
assu
med
toh
ave
am
ean
ofze
roan
dco
ndi
tion
alva
rian
ceσ
2t.
Dnt
isth
edu
mm
yva
riab
leth
atta
kes
the
valu
eof
1w
hen
ther
eis
anex
pan
sive
mon
etar
ypo
licy
ann
oun
cem
ent.
∗ ,∗∗
and
∗∗∗
indi
cate
sign
ifica
nce
atth
e10
%,5
%an
d1%
leve
ls,r
espe
ctiv
ely.
Q(2
0)re
fers
toth
eLj
un
g–B
oxpo
rtm
ante
aute
stfo
ru
pto
20th
-ord
erse
rial
corr
elat
ion
inth
est
anda
rdiz
edre
sidu
als.
Seri
esn
stan
dsfo
rth
eco
ntr
actw
ith
anex
pira
tion
date
betw
een
nan
dn
−1
mon
ths
ahea
d.Pa
nel
Are
port
sre
sult
sfo
rth
ew
hol
esa
mpl
e,fr
omM
ay20
01to
Dec
embe
r20
09(2
,187
obse
rvat
ion
s),P
anel
Bre
port
sre
sult
sfo
rth
efi
rst
sub-
per
iod,
from
2M
ay20
01to
14M
ay20
04(7
66ob
serv
atio
ns)
and
Pan
elC
repo
rts
resu
lts
for
the
seco
nd
sub-
per
iod,
from
17M
ay20
04to
31D
ecem
ber
2009
(1,4
22ob
serv
atio
ns)
.
C© 2012 Blackwell Publishing Ltd
242 Renata Herrerıas and Pedro Gurrola
Tab
le3:
FIG
AR
CH
Est
imat
esfo
rR
estr
icti
veM
onet
ary
Pol
icy
An
nou
nce
men
ts
Seri
esμ
γ1
γ2
γ3
α0
β1×
103
dγ
4Q
(20)
P-v
alu
e
Pan
elA
1−0
.231
∗∗∗
0.04
7∗∗
1.24
6∗∗
∗10
.919
∗∗∗
2.13
6∗∗
∗0.
311
∗∗∗
0.60
2∗∗
∗42
.38
∗28
.457
0.10
2−0
.269
∗∗∗
0.14
3∗∗
∗0.
216
10.2
12∗∗
∗2.
772
∗∗∗
0.27
5∗∗
∗0.
545
∗∗∗
65.4
4∗∗
25.9
740.
173
−0.3
18∗∗
∗0.
149
∗∗∗
−0.1
30∗∗
∗10
.844
∗∗∗
3.94
4∗∗
∗0.
214
∗∗∗
0.48
5∗∗
∗78
.49
∗∗∗
20.7
010.
424
−0.3
93∗∗
∗0.
143
∗∗∗
0.03
910
.225
∗∗∗
7.54
1∗∗
∗0.
159
∗∗∗
0.41
3∗∗
∗79
.32
∗∗∗
27.3
980.
125
−0.4
77∗∗
∗0.
155
∗∗∗
−0.6
78∗∗
∗9.
464
∗∗∗
4.17
7∗∗
∗0.
195
∗∗∗
0.45
5∗∗
∗89
.42
∗∗15
.145
0.77
6−0
.478
∗∗∗
0.11
9∗∗
∗−0
.124
7.72
2∗∗
∗5.
340
∗∗∗
0.20
4∗∗
∗0.
419
∗∗∗
85.0
4∗∗
∗18
.031
0.59
Pan
elB
1−1
.856
∗∗∗
0.19
2∗∗
∗1.
593
∗∗26
.915
∗∗∗
10.4
05∗
0.50
7∗∗
∗0.
649
∗∗∗
97.5
8∗∗
∗14
.178
0.82
2−1
.569
∗∗∗
0.21
4∗∗
∗1.
831
∗∗∗
27.3
01∗∗
∗18
.774
∗∗∗
0.05
9∗∗
∗0.
347
∗∗∗
89.0
8∗∗
27.3
330.
133
−1.9
62∗∗
∗0.
180
∗∗∗
−0.0
44∗∗
∗23
.040
∗∗∗
0.48
4∗∗
∗−0
.035
∗∗∗
0.34
9∗∗
∗4.
74∗∗
26.1
770.
164
−2.0
46∗∗
∗0.
190
∗∗∗
1.11
922
.239
∗∗∗
6.18
8−0
.096
∗∗∗
0.34
1∗∗
∗−0
.71
26.3
730.
155
−1.9
30∗∗
∗0.
195
∗∗∗
−0.6
70∗
16.6
68∗∗
∗7.
760
∗∗∗
−0.0
010.
437
∗∗∗
29.2
623
.925
0.25
6−2
.256
∗∗∗
0.01
00.
111
10.3
08∗∗
∗13
.727
∗∗∗
0.07
5∗∗
∗0.
401
∗∗∗
−1.7
629
.158
0.09
Pan
elC
1−0
.226
∗∗∗
0.00
21.
162
∗∗∗
10.6
25∗∗
∗1.
306
∗∗∗
−0.0
49∗∗
0.56
6∗∗
∗54
.48
∗∗18
.103
0.58
2−0
.270
∗∗∗
0.09
3∗∗
∗−0
.451
∗∗8.
654
∗∗∗
0.29
4∗∗
∗0.
312
∗∗∗
0.63
3∗∗
∗30
.95
∗∗24
.090
0.24
3−0
.256
0.14
6∗∗
∗−0
.193
8.78
7∗∗
∗0.
525
∗∗∗
0.35
5∗∗
∗0.
588
∗∗∗
51.3
5∗
21.4
940.
374
−0.3
29∗∗
∗0.
134
∗∗∗
−0.3
448.
324
∗∗∗
1.33
5∗∗
∗0.
313
∗∗∗
0.52
0∗∗
∗54
.87
∗∗34
.575
0.02
5−0
.330
∗∗∗
0.16
0∗∗
∗−0
.898
∗∗7.
359
∗∗∗
0.38
60.
412
∗∗∗
0.56
1∗∗
∗54
.63
∗∗20
.295
0.44
6−0
.328
∗∗0.
193
∗∗∗
−0.4
297.
467
∗∗∗
0.06
00.
338
∗∗∗
0.53
8∗∗
∗67
.00
∗∗21
.930
0.34
Not
e:T
he
tabl
ere
port
sre
sult
sfr
omth
eFI
GA
RC
Hes
tim
atio
n:
r t=
μ+
γ1r t−
1+
γ2r nt
+γ
3D
nt+
u tan
dσ
2 t=
α0+
β1σ
2 t−1+
[1−
β1L
−(1
−ϕ
L)(
1L)d
]u2 t+
γ4D
nt
wh
ereμ
isa
con
stan
t,r t
isth
elo
gari
thm
icch
ange
ofse
ttle
men
trat
eson
day
tan
dex
pres
sed
inba
sis
poin
ts,a
nd
r ntis
the
expe
cted
chan
gein
futu
res
inte
rest
rate
sca
lcu
late
dov
ern
on-a
nn
oun
cem
ents
days
.T
he
resi
dual
su t
are
assu
med
toh
ave
am
ean
ofze
roan
dco
ndi
tion
alva
rian
ceσ
2t.
Dnt
isth
edu
mm
yva
riab
leth
atta
kes
the
valu
eof
1w
hen
ther
eis
are
stri
ctiv
em
onet
ary
pol
icy
ann
oun
cem
ent.
∗ ,∗∗
and
∗∗∗
indi
cate
sign
ifica
nce
atth
e10
%,5
%an
d1%
leve
ls,r
espe
ctiv
ely.
Q(2
0)re
fers
toth
eLj
un
g–B
oxpo
rtm
ante
aute
stfo
ru
pto
20th
-ord
erse
rial
corr
elat
ion
inth
est
anda
rdiz
edre
sidu
als.
Seri
esn
stan
dsfo
rth
eco
ntr
actw
ith
anex
pira
tion
date
betw
een
nan
dn
−1
mon
ths
ahea
d.Pa
nel
Are
port
sre
sult
sfo
rth
ew
hol
esa
mpl
e,fr
omM
ay20
01to
Dec
embe
r20
09(2
,187
obse
rvat
ion
s),P
anel
Bre
port
sre
sult
sfo
rth
efi
rst
sub-
per
iod,
from
2M
ay20
01to
14M
ay20
04(7
66ob
serv
atio
ns)
and
Pan
elC
repo
rts
resu
lts
for
the
seco
nd
sub-
per
iod,
from
17M
ay20
04to
31D
ecem
ber
2009
(1,4
22ob
serv
atio
ns)
.
C© 2012 Blackwell Publishing Ltd
Monetary Policy Announcements 243
A. 3. No-Change Announcements
Table 4 shows that even when the central bank leaves monetary conditionsunchanged, there are further adjustments in futures interest rates that are notcaptured by the expected component. All of the adjustments decrease the futuresinterest rate level, and they occur mostly during the second sub-period. Volatilityon announcement dates is also higher over this second sub-period. This result issimilar to that of Roley and Sellon (1998b), or Poole et al. (2002), who explainthat market participants can also revise their expectations when the central banktakes no action. That is, the market can also be surprised by the no-changeannouncement, and therefore, it is necessary to adjust the futures positions.
A. 4. Results Analysis
All of the results suggest that sending information about the interest rate targetand the change in the transmission mechanism actually induced futures marketparticipants to trade according to the new market conditions, while the currentaccount balance mechanism affects the market less. This result is reasonableconsidering that the Mexican futures market is mainly used to hedge the positionsof institutional investors willing to fix futures interest rates. During the analysedperiod, the institutional investors that we refer to are only banks and pensionfunds because insurance companies were not allowed by law to use derivatives,mutual funds were very unsophisticated and hedge funds were non-existent. Inparticular, the banks and pension funds are passive investors that are not allowedto take risks beyond their core business. The only use for short-term interest ratefutures then, is to receive or pay a monthly fixed rate to protect the interest ratemargin. Without any guidance on interest rates, these institutions did not needto adjust futures prices, contrary to the case when the central bank defines theinterest rate target.
Comparing our findings to those from the studies on more mature markets, itcan be considered that the Mexican central bank is not successfully smoothing theeffects of its monetary policy. However, it is important to take into account thatsince 2001, when the central bank adopted the inflation-targeting framework,the level and volatility of inflation have been declining, and from the secondhalf of 2004, the volatility and level of interest rates have also decreased. For thisreason, it is possible that the increase in futures rates volatility during monetaryannouncement days has more to do with the characteristics of the market andless with the quality of central bank’s communications. The key aspect is thatduring the period under consideration, the Mexican derivatives markets was stilla relatively young market providing hedging instruments for large banks andpension funds. For instance, a very common use of TIIE futures contracts was toconstruct synthetic swaps where the investor entered into long or short positions
C© 2012 Blackwell Publishing Ltd
244 Renata Herrerıas and Pedro Gurrola
Tab
le4:
FIG
AR
CH
Est
imat
esfo
rN
o-C
han
geM
onet
ary
Pol
icy
An
nou
nce
men
ts
Seri
esμ
γ1
γ2
γ3
α0
β1×
103
dγ
4Q
(20)
P-v
alu
e
Pan
elA
10.
015
0.02
2−0
.492
−2.8
87∗∗
∗3.
004
∗∗∗
0.28
7∗∗
∗0.
570
∗∗∗
6.09
∗∗∗
32.3
590.
042
−0.1
21∗∗
∗0.
132
∗∗∗
−0.4
98∗∗
−2.0
48∗∗
∗2.
529
∗∗∗
0.31
4∗∗
∗0.
561
∗∗∗
5.67
∗∗∗
26.1
790.
163
−0.1
99∗∗
0.15
3∗∗
∗−0
.882
∗∗−1
.099
∗3.
223
∗∗∗
0.26
1∗∗
∗0.
512
∗∗∗
8.86
∗∗20
.527
0.43
4−0
.278
∗∗∗
0.15
1∗∗
∗−0
.864
∗∗∗
−1.4
20∗∗
7.37
3∗∗
∗0.
193
∗∗∗
0.42
2∗∗
∗13
.78
∗∗29
.328
0.08
5−0
.387
∗∗∗
0.15
3∗∗
∗−1
.193
∗∗∗
−0.3
17∗∗
∗4.
286
∗∗∗
0.20
6∗∗
∗0.
454
∗∗∗
10.3
420
.508
0.43
6−0
.386
∗∗∗
0.12
4∗∗
∗−0
.840
∗∗∗
−0.4
424.
531
∗∗∗
0.23
5∗∗
∗0.
434
∗∗∗
16.0
9∗∗
∗21
.103
0.39
Pan
elB
1−1
.622
∗∗0.
182
∗∗∗
−0.0
48−5
.316
23.7
78∗∗
∗0.
546
∗∗∗
0.69
9∗∗
∗2.
3614
.879
0.78
2−1
.367
∗∗∗
0.19
5∗∗
∗−0
.803
∗∗−6
.363
∗∗∗
13.4
36∗
0.00
00.
302
∗∗∗
−24.
16∗∗
30.0
310.
073
−1.9
28∗∗
∗0.
174
∗∗∗
−2.6
67∗∗
∗−8
.728
∗∗∗
6.37
8∗∗
∗−0
.059
∗0.
337
∗∗∗
−25.
9926
.764
0.14
4−2
.017
∗∗∗
0.17
0∗∗
∗−1
.122
−3.0
5212
.996
−0.1
11∗
0.32
2∗∗
∗−3
1.88
25.3
750.
195
−1.8
15∗∗
∗0.
186
∗∗∗
−3.1
65∗∗
∗−6
.907
∗∗11
.600
−0.0
050.
430
∗∗∗
−20.
4024
.065
0.24
6−2
.360
∗∗∗
−0.0
06−1
.204
∗∗∗
−1.8
9616
.814
∗∗∗
0.06
8∗∗
∗∗∗
−43.
0330
.178
0.07
Pan
elC
1−0
.011
−0.1
16∗∗
∗−0
.723
∗−2
.794
∗∗∗
1.17
6∗∗
∗0.
049
0.58
6∗∗
∗8.
45∗∗
∗23
.678
0.26
2−0
.134
∗∗∗
0.07
6∗∗
∗−0
.851
∗∗−1
.930
∗∗∗
0.33
60.
361
∗∗∗
0.65
1∗∗
∗4.
95∗∗
23.0
160.
293
−0.1
56∗∗
∗0.
151
∗∗∗
−0.5
85∗∗
∗−0
.881
∗0.
435
∗∗∗
0.39
3∗∗
∗0.
617
∗∗∗
7.65
∗∗∗
21.3
470.
384
−0.2
19∗∗
0.14
9∗∗
∗−1
.031
∗∗−1
.253
∗1.
447
∗∗∗
0.35
0∗∗
∗0.
535
∗∗∗
13.1
6∗∗
∗37
.848
0.01
5−0
.248
∗∗0.
158
∗∗∗
−1.1
74∗∗
∗0.
556
∗∗∗
0.32
4∗∗
0.43
3∗∗
∗0.
576
∗∗∗
12.3
7∗∗
∗23
.368
0.27
6−0
.226
∗∗0.
202
∗∗∗
−1.0
83∗∗
−0.0
250.
454
∗∗∗
0.43
8∗∗
∗0.
600
∗∗∗
21.9
4∗∗
∗24
.606
0.22
Not
e:T
he
tabl
ere
port
sre
sult
sfr
omth
eFI
GA
RC
Hes
tim
atio
n:
r t=
μ+
γ1r t−
1+
γ2r nt
+γ
3D
nt+
u tan
dσ
2 t=
α0+
β1σ
2 t−1+
[1−
β1L
−(1
−ϕ
L)(
1L)d
]u2 t+
γ4D
nt,
wh
ereμ
isa
con
stan
t,r t
isth
elo
gari
thm
icch
ange
ofse
ttle
men
trat
eson
day
tan
dex
pres
sed
inba
sis
poin
ts,a
nd
r ntis
the
expe
cted
chan
gein
futu
res
inte
rest
rate
sca
lcu
late
dov
ern
on-a
nn
oun
cem
ents
days
.T
he
resi
dual
su t
are
assu
med
toh
ave
am
ean
ofze
roan
dco
ndi
tion
alva
rian
ceσ
2t.
Dnt
isth
edu
mm
yva
riab
leth
atta
kes
the
valu
eof
1w
hen
ther
eis
an
o-ch
ange
mon
etar
yp
olic
yan
nou
nce
men
t.∗ ,∗
∗an
d∗∗
∗in
dica
tesi
gnifi
can
ceat
the
10%
,5%
and
1%le
vels
,res
pect
ivel
y.Q
(20)
refe
rsto
the
Lju
ng–
Box
port
man
teau
test
for
up
to20
th-o
rder
seri
alco
rrel
atio
nin
the
stan
dard
ized
resi
dual
s.Se
ries
nst
ands
for
the
con
trac
twit
han
expi
rati
onda
tebe
twee
nn
and
n−
1m
onth
sah
ead.
Pan
elA
repo
rts
resu
lts
for
the
wh
ole
sam
ple,
from
May
2001
toD
ecem
ber
2009
(2,1
87ob
serv
atio
ns)
,Pan
elB
repo
rts
resu
lts
for
the
firs
tsu
b-p
erio
d,fr
om2
May
2001
to14
May
004
(766
obse
rvat
ion
s)an
dPa
nel
Cre
port
sre
sult
sfo
rth
ese
con
dsu
b-p
erio
d,fr
om17
May
2004
to31
Dec
embe
r20
09(1
,422
obse
rvat
ion
s).
C© 2012 Blackwell Publishing Ltd
Monetary Policy Announcements 245
in multiple futures contracts with consecutive expiration months and all withthe same settlement price. In this case, one could think that futures prices werenot utilized as predictors of future spot rates.
B. Robustness Check
In this section, we perform two robustness checks to explore whether our resultsare driven by day-of-the-week effects or by abnormal trading volumes.
B. 1. Day-of-the-Week Effects
Although differences in price volatility in futures contracts across days of the weekhave been mainly attributed to macroeconomic scheduled announcements, it isimportant to verify that abnormal behaviour during policy announcements is notthe result of day-of-the-week anomalies. These anomalies have been commonlyreported in the literature, for example, Harvey and Huang (1991), Ederingtonand Lee (1993) and Han et al. (1999).
To control for the potential effects of any recurrent pattern on a particular dayof the week the futures rate changes and variance, we included binary dummiesfor Monday, Tuesday, Thursday and Friday in both the mean and the varianceequations in the previous estimations. Given that a constant term is allowed inthe regression equation, the dummy trap is avoided by not including a dummyfor Wednesday.3 The results (not reported here, but available from the authors)show a significant Tuesday effect in volatility. The reasons for this effect remainunexplored, but a plausible argument stems from the fact that the central bankconducts Treasury Certificates (CETES) auctions on Tuesdays. These auctionsrelease information concerning the interest rates for the 28- and 91-day Treasurybills. Regardless of this Tuesday effect, the results presented in the previoussection remain unchanged, and are therefore still robust after controlling forday-of-the-week effects.
B. 2. Trading Volume
We also investigate whether the level of trading volume is different on monetarypolicy release days relative to the no-announcement days and whether changesin price volatility can be explained by the trading volume level. To answer thefirst question, we perform the following estimation with trading volume as thedependent variable and the announcement date dummy as the independent
3The choice was dictated by the fact that Wednesday is the usual expiration day for all contracts.
C© 2012 Blackwell Publishing Ltd
246 Renata Herrerıas and Pedro Gurrola
variable:
volt = μ + ϕDnt + ut , (6)
where μ is a constant, volt is the number of contracts traded on day t for eachcontract and Dnt is the dummy variable that takes the value of 1 when there is amonetary policy announcement. The results are reported in Table 5. In general,there is no statistical evidence for a higher trading volume on announcementdays except for the case of the contract closest to expiration. However, during thesecond sub-period, when the announcement relates to a restrictive policy stance,the trading volume is significantly higher than the average trading volume onnon-release days.
To analyse whether the increase in trading volume causes the increase involatility, we include the natural logarithm of trading volume as the explanatoryvariable in the variance equation of the FIGARCH estimations. As before, thesame set of regressions is performed by each type of announcement for the wholesample and for each sub-period. In general, the results (not reported here, butavailable from the authors) show that price volatility is occasionally related totrading volume, most of all in contracts with a longer expiration and always witha positive relationship. However, the effect of the announcement on contractvolatility remains unchanged.
VII. Conclusions
This paper investigates, in the case of Mexico, the effects of changes in the mon-etary policy operational target on the volatility of interest rate futures. Using aFIGARCH-type specification to model futures price volatility in contracts withmonthly maturities up to six months forward, and including policy announce-ment days as the exogenous variable on the mean and the conditional variance, wefind that, in most cases, the unexpected component of the announcements havea significant impact on the mean interest rate changes and on futures volatility.Similar to previous studies, we find that the market consistently adjusts interestrates when the announcement corresponds to restrictive and expansive mone-tary policy stances, and occasionally when the announcement leaves monetaryconditions unchanged. The increase in price volatility occurs regardless of thetype of announcement. The analysis of trading volume demonstrates that thereis also an increase in the level of trading related to restrictive announcements.
The results reveal that the change in the operational target modified the trad-ing behaviour of market participants, and therefore futures price volatility beganbehaving in a different way. While the negative balance target mechanism didnot induce a significant increase in volatility, the short-term interest rate target
C© 2012 Blackwell Publishing Ltd
Monetary Policy Announcements 247Ta
ble
5:Tr
adin
gV
olu
me
Reg
ress
ion
s
Wh
ole
sam
ple
Firs
tsu
b-pe
riod
Seco
nd
sub-
peri
od
Seri
esC
onst
ant
ϕSe
ries
Con
stan
tϕ
Seri
esC
onst
ant
ϕ
Pan
elA
:Exp
ansi
vem
onet
ary
polic
yan
nou
nce
men
ts1
37,8
37∗∗
∗−9
100
34,1
76∗∗
∗−2
1,91
00
39,7
80∗∗
∗2,
079
244
,223
∗∗∗
−11,
313
145
,175
∗∗∗
−29,
341
143
,718
∗∗∗
−7,3
933
41,7
23∗∗
∗−1
2,04
02
40,7
30∗∗
∗−1
8,73
02
42,2
49∗∗
∗−1
1,03
14
32,0
49∗∗
∗−6
,356
333
,274
∗∗∗
−4,1
073
31,3
99∗∗
∗−6
,400
526
,276
∗∗∗
−2,3
214
25,7
34∗∗
∗11
,766
426
,563
∗∗∗
−5,3
186
23,4
63∗∗
∗−8
,481
522
,646
∗∗∗
−4,3
135
23,8
97∗∗
∗−9
,585
Pan
elB
:Res
tric
tive
mon
etar
ypo
licy
ann
oun
cem
ents
136
,672
∗∗∗
110,
118
∗∗∗
033
,420
∗∗∗
56,1
02∗∗
∗0
38,3
82∗∗
∗14
5,22
2∗∗
∗
243
,321
∗∗∗
76,9
05∗∗
∗1
44,8
23∗∗
∗19
,671
142
,531
∗∗∗
113,
523
∗∗∗
340
,851
∗∗∗
73,4
69∗∗
∗2
40,4
36∗∗
∗18
,409
241
,069
∗∗∗
108,
913
∗∗∗
431
,608
∗∗∗
36,9
90∗∗
∗3
33,1
21∗∗
∗11
,491
330
,812
∗∗∗
53,2
06∗∗
∗
525
,959
∗∗∗
28,2
91∗∗
∗4
25,7
78∗∗
∗22
24
26,0
54∗∗
∗46
,356
∗∗∗
623
,094
∗∗∗
28,4
12∗∗
∗5
22,6
06∗∗
∗1,
950
523
,351
∗∗∗
45,4
81∗∗
∗
Pan
elC
:No-
chan
gem
onet
ary
polic
yan
nou
nce
men
ts1
36,9
18∗∗
∗39
,097
∗∗∗
133
,766
∗∗∗
17,4
07∗
138
,592
∗∗∗
46,8
22∗∗
∗
244
,363
∗∗∗
−9,9
712
45,0
63∗∗
∗−2
922
43,9
90∗∗
∗−1
3,52
73
41,8
83∗∗
∗−1
1,12
23
40,6
38∗∗
∗90
73
42,5
44∗∗
∗−1
5,86
44
32,1
77∗∗
∗−7
,737
433
,327
∗∗∗
−3,7
524
31,5
66∗∗
∗−9
,068
526
,418
∗∗∗
−6,9
095
25,8
44∗∗
∗−3
,376
526
,723
∗∗∗
−8,3
346
23,4
37∗∗
∗−1
,879
622
,510
∗∗∗
6,38
66
23,9
29∗∗
∗−5
,148
Not
e:T
he
tabl
ere
port
sre
sult
sfr
omth
est
anda
rdO
LSes
tim
atio
n:
vol t
=μ
+ϕ
Dnt
+u t,
wh
ere
μis
aco
nst
ant,
vol t
isth
en
um
ber
ofco
ntr
acts
trad
edon
day
tfo
rea
chco
ntr
act
and
Dnt
isth
edu
mm
yva
riab
leth
atta
kes
the
valu
eof
1w
hen
ther
eis
am
onet
ary
polic
yan
nou
nce
men
t.∗ ,∗
∗an
d∗∗
∗in
dica
tesi
gnifi
can
ceat
the
10%
,5%
and
1%le
vels
,res
pect
ivel
y.
C© 2012 Blackwell Publishing Ltd
248 Renata Herrerıas and Pedro Gurrola
mechanism does. This fact appears to be counterintuitive because the introduc-tion of an interest rate target is normally associated with improved transparencyand higher information quality that remove uncertainty and decrease volatility.However, given the particular characteristics of the Mexican market, these resultscan be feasible. For instance, with the negative balance target mechanism, bankscould simply pay the penalty without changing any of their positions, so thatfutures contract prices would not move as much as they would when interestrates were controlled by the central bank. Furthermore, it is possible that futuresprices are not meant to anticipate future spot interest rates in a non-maturederivatives market where the only investors are banks and pension funds tradingto hedge interest rate margins. When the central bank provides clear informationabout interest rates, market participants will efficiently adjust futures prices toincorporate new information, but only when the information is released, andnot before to avoid excessive rollover costs.
In sum, there are two key implications of our findings. First, the operationaltarget to transmit monetary policy is relevant in determining the subsequentreaction of market participants. Second, in markets that are dominated by in-stitutional investors trading only for hedging purposes futures contracts interestrates do not necessarily anticipate the central bank’s actions.
Renata HerrerıasSchool of Business AdministrationITAMCamino a Sta. Teresa 930,Mexico, D.F. 10700,Mexicorherreria@itam.mx
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