Competence Center Corporate Finance & Risk Management by Harald Weiß Stuttgart, Jan 27th 2006...
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![Page 1: Competence Center Corporate Finance & Risk Management by Harald Weiß Stuttgart, Jan 27th 2006 Pricing Liquidity Risk.](https://reader031.fdocuments.in/reader031/viewer/2022032800/56649d4d5503460f94a2b7b3/html5/thumbnails/1.jpg)
Competence Center Corporate Finance & Risk Management
by Harald WeißStuttgart, Jan 27th 2006
Pricing Liquidity Risk
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Agenda
1. Motivation
2. Liquidity Measures
3. Pricing Liquidity Risk with APT
4. Using OPT for Valuing the Time Dimension of Liquidity
5. Extending the Longstaff model
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Practitioner´s view
majority of market participants consider the liquidity of assets in portfolio
decisions
Until recently
liquidity research was focussed on individual assets
asset value is reduced by lower liquidity
investor demands for a liquidity premium
Current research about liquidity
liquidity is a market-wide phenomena
Is liquidity risk a non-diversifiable risk?
1. Motivation
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2. Liquidity Measures
Asset Liquidity
Time Dimension Price Dimension
Keynes (1930): „realisable at short notice without loss“
market liquidity is characterized by the trade-off between the selling-price and the time-till-sale for a given set of market conditions
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2. Liquidity Measures
A closer to look to the Xetra-Orderbook
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2. Liquidity Measures
Aitken and Comerton-Forde (Pacific-Basin Finance Journal, 2003 (1))
found 68 different measures used in the literature
Selected liquidity measures with summary statistics for NYSE (Roll, 2005)
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3. Pricing Liquidity Risk with APT
Pastor and Stambaugh (2003) model
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Pastor and Stambaugh (2003) model
3. Pricing Liquidity Risk with APT
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4. Using OPT for Valuing the Time Dimension of Liquidity
Perfectly liquid asset:
t=0 t=1
S
t=0 t=1
S
Il liquid asset:
Restrictedfrom trading
more flexibility for investors holding the liquid asset standard no-arbitrage argument:
value of marketability is equal to the price difference between the two assets
(positive) value of marketability can be viewed as a trading option
tt
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Idea: a put option provides a protection against illiquidity
a position hedged by a put self-liquidates as money is lost and markets become illiquid
put price can be viewed as the value of liquidity concept of portfolio insurance
In general: put option gives the holder the right to sell the underlying asset by a certain date T for a certain price X
Short Put
X
Payoff
ST (Terminal stock price)0
4. Using OPT for Valuing the Time Dimension of Liquidity
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Longstaff, Journal of Finance (1995)
investor has perfect market timing ability, knows the optimal selling point in t=0
additional profit:
= payoff of a European lookback put option upper bound for the value of marketability
Lookback option
underlying stochastic process is normally a geometric Brownian motion
modification: Brownian bridge process
5. Extending the Longstaff model
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5. Extending the Longstaff model
Definition of the underlying stochastic procees
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Simulation of the underlying stochastic process70
8090
100
110
120
0 .2 .4 .6 .8 1t
S1 S2S3 S4S5
Lookback
5. Extending the Longstaff model
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Pricing Lookback options using Monte Carlo Simulation
(STATA) Program:
(1) Select the starting parametershere: S0 , ST , σ , T , dt
(2) Simulate the underlying stochastic process from t=0 to t=T(3) Keep in mind the maximum stock price during t=0 and t=T(4) Calculate the option price as
(5) Repeat steps (1) to (4) (here: 1000 times)(6) Option price is calculated as the mean of (5)(7) Vary the starting parameters σ and T
and repeat the simulation steps (1) to (6)
5. Extending the Longstaff model
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5. Extending the Longstaff model
Simulation results
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Back-up: Further Research
Change the stochastic process of the underlying geometric Brownian motion Borwnian bridge process Ornstein-Uhlenbeck process Poisson process
Vary the option type American vs. European Lookback, Russian
Model the option parameters volatility model (GARCH) stochastic interest rate
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-> add additional risk factors (stochastic interest rates)integrate multiple non-trading periods-> investigate time-dependence of liquidityasset remains illiquid-> no continuous trading (trading is only possible at certrain points in time)-> distribution of trading dates (not uniform?)-> dividend payments
Back-up: Further Research
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Kempf
investor has no perfect foresight investor has an incentive to sell the asset when the reservations price Yt differs
from the market price value of marketability depends from investor preferences Xt follows a stochastic process with the restriction XT = 0
Koziol and Sauerbier, Working-Paper (2003) Lookback Option Stochastic interest rate, multiple non-trading periods, time varying liquidity of
bonds
Back-up: Option Based Models
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Back-up: Limitations of the Model
Discussing the Black-Scholes assumptions (here: selection)
(1) underlying asset price follows a geometric Brownian Motion
check empirical return distributions
(2) assets are divisible
(3) trading of the underlying asset is continuously
same problem as for real options
see assumption: complete capital market is needed
Modelling Liquidity Risk
exercies of the option which corresponds to the sale of the property is stochastic
use Russian options which have no exipry date
No general equilibrium model
add further assumptions about investor preferences
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Back-up: XETRA-Orderbook
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Result
both approaches should deliver the same risk-return profile-> Compare the results!
=5%
=6%
we start here
Back-up: Motivation
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3. Option Based Model for Valuing the Time Dimension
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3. Option Based Model for Valuing the Time Dimension
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Back-up
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Back-up