Terry Cheuk - Academia.edu (original) (raw)
Papers by Terry Cheuk
Journal of Derivatives, Aug 31, 1996
This article develops a new trinomial tree model for barrier options. It is well-known that for b... more This article develops a new trinomial tree model for barrier options. It is well-known that for barrier options, the positions of nodes in the tree with respect to the barrier value are critical. We use a time-dependent shift to position the tree optimally with respect to the barrier. The model is very flexible and can be used to price options with time-varying barrier structures. It can be used to price knock-in and knock-out options based on either one or two underlying assets, including those with time-varying barriers - single or double. Traditional lattice models all have difficulties when the underlying asset price is very close to the barrier. This model does not suffer from that limitation. Also, in many applications, the barrier condition is based on the daily or weekly fixings. A simple solution for the discrete-time barrier observation is advanced, which enables us to uncover the price differences among barrier options with different observation frequencies.
Social Science Research Network, 2003
Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in... more Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in application. In order to use Black-Scholes to price any option, one needs to know the implied volatility surface. The existence of such surface is an evidence of misspecification of the model. In this case, the problem is with the assumption of a geometric Brownian motion for the stock price process. There is strong empirical evidence that stocks do not follow such process. However, no agreement has been reached on what is the best distribution to use. Neural Network approaches the problem very differently. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any distributional assumption regarding the underlying variable. Instead, it puts up a formula with a set of unknown parameters and let the optimization routine search for the parameters best fitted to the desired results. Hutchinson-Lo-Poggio (1994) showed that it is indeed possible to use a Neural Network to price S&P futures options. In this paper, we will continue with this line of research. Specifically, we will examine the best way to set up and train a Neural Network for option pricing and hedging. We will also investigate if a Neural Network could produce better hedging parameters than the standard option pricing model. We use S&P futures options data covering the period 1990-2000.
Social Science Research Network, 2002
ABSTRACT We construct portfolios of S&P500 futures and their associated options, which ar... more ABSTRACT We construct portfolios of S&P500 futures and their associated options, which are long out of (in) the money puts and short out of (in) the money calls, and which are delta (price) and vega (volatility) neutral, with respect to a GARCH type model for the underlying price. These systematically earn less (more) than the riskless return. We give evidence that this loss is not a payment for insurance against a market crash, by separately including a deep out of the money put in the portfolio, which makes it neutral to a market crash, but does not substantially alter the result. Our result is consistent with a market imperfection interpretation of the smirk: out of the money puts are "too dear", relative to out of the money calls, perhaps because these puts are valuable in providing portfolio insurance. Our strategies do not earn excessive profits, and so we argue that the smirk will not disappear in an efficient market.
Review of Derivatives Research, May 20, 2009
SSRN Electronic Journal, 2003
Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in... more Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in application. In order to use Black-Scholes to price any option, one needs to know the implied volatility surface. The existence of such surface is an evidence of misspecification of the model. In this case, the problem is with the assumption of a geometric Brownian motion for the stock price process. There is strong empirical evidence that stocks do not follow such process. However, no agreement has been reached on what is the best distribution to use. Neural Network approaches the problem very differently. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any distributional assumption regarding the underlying variable. Instead, it puts up a formula with a set of unknown parameters and let the optimization routine search for the parameters best fitted to the desired results. Hutchinson-Lo-Poggio (1994) showed that it is indeed possible to use a Neural Network to price S&P futures options. In this paper, we will continue with this line of research. Specifically, we will examine the best way to set up and train a Neural Network for option pricing and hedging. We will also investigate if a Neural Network could produce better hedging parameters than the standard option pricing model. We use S&P futures options data covering the period 1990-2000.
Computational Economics, 1999
There exist a number of approximation methods for the price of average rate options, when the und... more There exist a number of approximation methods for the price of average rate options, when the underlying asset is a currency or equity. Realistic pricing models for average interest rate caps based on interbank offered rates have not yet been published. In this paper we propose to adapt the methods of Levy (1992), Vorst (1992) and Rogers and Shi (1995)
SSRN Electronic Journal, 2002
ABSTRACT We construct portfolios of S&P500 futures and their associated options, which ar... more ABSTRACT We construct portfolios of S&P500 futures and their associated options, which are long out of (in) the money puts and short out of (in) the money calls, and which are delta (price) and vega (volatility) neutral, with respect to a GARCH type model for the underlying price. These systematically earn less (more) than the riskless return. We give evidence that this loss is not a payment for insurance against a market crash, by separately including a deep out of the money put in the portfolio, which makes it neutral to a market crash, but does not substantially alter the result. Our result is consistent with a market imperfection interpretation of the smirk: out of the money puts are "too dear", relative to out of the money calls, perhaps because these puts are valuable in providing portfolio insurance. Our strategies do not earn excessive profits, and so we argue that the smirk will not disappear in an efficient market.
Review of Derivatives Research, 2009
Journal of International Money and Finance, 1997
In the last decade, interest in exotic options has been growing, especially in the over-the-count... more In the last decade, interest in exotic options has been growing, especially in the over-the-counter currency market. In this paper we consider Iookback currency options, which are path-dependent. We show that a one-state variable binomial model for currency Iookback options can be constructed with the same computational complexity, or should we say simplicity, as the standard binomial model. Furthermore, the model allows us to investigate a second very important feature of real-life lookback option contracts-the observation frequency. (JEL F31).
The Journal of Derivatives, 1996
This article develops a new trinomial tree model for barrier options. It is well-known that for b... more This article develops a new trinomial tree model for barrier options. It is well-known that for barrier options, the positions of nodes in the tree with respect to the barrier value are critical. We use a time-dependent shift to position the tree optimally with respect to the barrier. The model is very flexible and can be used to price options with time-varying barrier structures. It can be used to price knock-in and knock-out options based on either one or two underlying assets, including those with time-varying barriers - single or double. Traditional lattice models all have difficulties when the underlying asset price is very close to the barrier. This model does not suffer from that limitation. Also, in many applications, the barrier condition is based on the daily or weekly fixings. A simple solution for the discrete-time barrier observation is advanced, which enables us to uncover the price differences among barrier options with different observation frequencies.
Journal of Derivatives, Aug 31, 1996
This article develops a new trinomial tree model for barrier options. It is well-known that for b... more This article develops a new trinomial tree model for barrier options. It is well-known that for barrier options, the positions of nodes in the tree with respect to the barrier value are critical. We use a time-dependent shift to position the tree optimally with respect to the barrier. The model is very flexible and can be used to price options with time-varying barrier structures. It can be used to price knock-in and knock-out options based on either one or two underlying assets, including those with time-varying barriers - single or double. Traditional lattice models all have difficulties when the underlying asset price is very close to the barrier. This model does not suffer from that limitation. Also, in many applications, the barrier condition is based on the daily or weekly fixings. A simple solution for the discrete-time barrier observation is advanced, which enables us to uncover the price differences among barrier options with different observation frequencies.
Social Science Research Network, 2003
Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in... more Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in application. In order to use Black-Scholes to price any option, one needs to know the implied volatility surface. The existence of such surface is an evidence of misspecification of the model. In this case, the problem is with the assumption of a geometric Brownian motion for the stock price process. There is strong empirical evidence that stocks do not follow such process. However, no agreement has been reached on what is the best distribution to use. Neural Network approaches the problem very differently. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any distributional assumption regarding the underlying variable. Instead, it puts up a formula with a set of unknown parameters and let the optimization routine search for the parameters best fitted to the desired results. Hutchinson-Lo-Poggio (1994) showed that it is indeed possible to use a Neural Network to price S&P futures options. In this paper, we will continue with this line of research. Specifically, we will examine the best way to set up and train a Neural Network for option pricing and hedging. We will also investigate if a Neural Network could produce better hedging parameters than the standard option pricing model. We use S&P futures options data covering the period 1990-2000.
Social Science Research Network, 2002
ABSTRACT We construct portfolios of S&P500 futures and their associated options, which ar... more ABSTRACT We construct portfolios of S&P500 futures and their associated options, which are long out of (in) the money puts and short out of (in) the money calls, and which are delta (price) and vega (volatility) neutral, with respect to a GARCH type model for the underlying price. These systematically earn less (more) than the riskless return. We give evidence that this loss is not a payment for insurance against a market crash, by separately including a deep out of the money put in the portfolio, which makes it neutral to a market crash, but does not substantially alter the result. Our result is consistent with a market imperfection interpretation of the smirk: out of the money puts are "too dear", relative to out of the money calls, perhaps because these puts are valuable in providing portfolio insurance. Our strategies do not earn excessive profits, and so we argue that the smirk will not disappear in an efficient market.
Review of Derivatives Research, May 20, 2009
SSRN Electronic Journal, 2003
Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in... more Since its introduction in 1973, the Black-Scholes model has found increasingly more resistance in application. In order to use Black-Scholes to price any option, one needs to know the implied volatility surface. The existence of such surface is an evidence of misspecification of the model. In this case, the problem is with the assumption of a geometric Brownian motion for the stock price process. There is strong empirical evidence that stocks do not follow such process. However, no agreement has been reached on what is the best distribution to use. Neural Network approaches the problem very differently. Essentially, a Neural Network is a non-parametric estimation technique. It does not make any distributional assumption regarding the underlying variable. Instead, it puts up a formula with a set of unknown parameters and let the optimization routine search for the parameters best fitted to the desired results. Hutchinson-Lo-Poggio (1994) showed that it is indeed possible to use a Neural Network to price S&P futures options. In this paper, we will continue with this line of research. Specifically, we will examine the best way to set up and train a Neural Network for option pricing and hedging. We will also investigate if a Neural Network could produce better hedging parameters than the standard option pricing model. We use S&P futures options data covering the period 1990-2000.
Computational Economics, 1999
There exist a number of approximation methods for the price of average rate options, when the und... more There exist a number of approximation methods for the price of average rate options, when the underlying asset is a currency or equity. Realistic pricing models for average interest rate caps based on interbank offered rates have not yet been published. In this paper we propose to adapt the methods of Levy (1992), Vorst (1992) and Rogers and Shi (1995)
SSRN Electronic Journal, 2002
ABSTRACT We construct portfolios of S&P500 futures and their associated options, which ar... more ABSTRACT We construct portfolios of S&P500 futures and their associated options, which are long out of (in) the money puts and short out of (in) the money calls, and which are delta (price) and vega (volatility) neutral, with respect to a GARCH type model for the underlying price. These systematically earn less (more) than the riskless return. We give evidence that this loss is not a payment for insurance against a market crash, by separately including a deep out of the money put in the portfolio, which makes it neutral to a market crash, but does not substantially alter the result. Our result is consistent with a market imperfection interpretation of the smirk: out of the money puts are "too dear", relative to out of the money calls, perhaps because these puts are valuable in providing portfolio insurance. Our strategies do not earn excessive profits, and so we argue that the smirk will not disappear in an efficient market.
Review of Derivatives Research, 2009
Journal of International Money and Finance, 1997
In the last decade, interest in exotic options has been growing, especially in the over-the-count... more In the last decade, interest in exotic options has been growing, especially in the over-the-counter currency market. In this paper we consider Iookback currency options, which are path-dependent. We show that a one-state variable binomial model for currency Iookback options can be constructed with the same computational complexity, or should we say simplicity, as the standard binomial model. Furthermore, the model allows us to investigate a second very important feature of real-life lookback option contracts-the observation frequency. (JEL F31).
The Journal of Derivatives, 1996
This article develops a new trinomial tree model for barrier options. It is well-known that for b... more This article develops a new trinomial tree model for barrier options. It is well-known that for barrier options, the positions of nodes in the tree with respect to the barrier value are critical. We use a time-dependent shift to position the tree optimally with respect to the barrier. The model is very flexible and can be used to price options with time-varying barrier structures. It can be used to price knock-in and knock-out options based on either one or two underlying assets, including those with time-varying barriers - single or double. Traditional lattice models all have difficulties when the underlying asset price is very close to the barrier. This model does not suffer from that limitation. Also, in many applications, the barrier condition is based on the daily or weekly fixings. A simple solution for the discrete-time barrier observation is advanced, which enables us to uncover the price differences among barrier options with different observation frequencies.