Empirical performance of alternative pricing models of currency options

Sarwar, Ghulam; Krehbiel, Timothy L.

doi:10.1002/(sici)1096-9934(200003)20:3<265::aid-fut4>3.0.co;2-4

Cited by 30 publications

(26 citation statements)

References 33 publications

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“…However adding the stochastic volatility feature to the Black-Scholes model improves out-ofsample pricing and hedging performance of the model. In a later paper Sarwar and Krehbiel (Sarwar and Krehbiel, 2000) report that the Black-Scholes model calculated with daily revised implied volatilities performs as well as the stochastic volatility model for European currency call options. Derman and Kani (Derman and Kani, 1994a,b), Dupire (Dupire, 1994) and Rubinstein (Rubinstein, 1994) develop a deterministic volatility function (DVF) option valuation model in an attempt to exactly explain the observed cross-section of option prices.…”

Section: Introductionmentioning

confidence: 95%

A Nonparametric Approach to Pricing Options Learning Networks

Can

Fadda

2014

scjournal

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For practitioners of equity markets, option pricing is a major challenge during high volatility periods and Black-Scholes formula for option pricing is not the proper tool for very deep out-of-the-money options. The Black-Scholes pricing errors are larger in the deeper out-of-the money options relative to the near the-money options, and it's mispricing worsens with increased volatility. Experts opinion is that the Black-Scholes model is not the proper pricing tool in high volatility situations especially for very deep out-of-the-money options. They also argue that prior to the 1987 crash, volatilities were symmetric around zero moneyness, with inthe-money and out-of-the money having higher implied volatilities than at-the-money options. However, after the crash, the call option implied volatilities were decreasing monotonically as the call went deeper into out-of-the-money, while the put option implied volatilities were decreasing monotonically as the put went deeper into in-the-money. Since these findings cannot be explained by the Black-Scholes model and its variations, researchers searched for improved option pricing models. Feedforward networks provide more accurate pricing estimates for the deeper out-of-the money options and handles pricing during high volatility with considerably lower errors for out-of-the-money call and put options. This could be invaluable information for practitioners as option pricing is a major challenge during high volatility periods. In this article a nonparametric method for estimating S&P 100 index option prices using artificial neural networks is presented. To show the value of artificial neural network pricing formulas, Black-Scholes option prices are compared with the network prices against market prices. To illustrate the practical relevance of the network pricing approach, it is applied to the pricing of S&P 100 index options from

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Section: Introductionmentioning

confidence: 95%

A Nonparametric Approach to Pricing Options Learning Networks

Can

Fadda

2014

scjournal

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“…Hull and White (1987), Heston (1993), Danielsson (1994), Kim et al (1998) and many others have studied the stochastic volatility models. Among the few empirical studies that adopted stochastic models are Bakshi et al (1997), Sarwar and Krehbiel (2000), Kim and Kim (2004) and Sharp et al (2010).…”

Section: Realised Volatilitymentioning

confidence: 99%

“…In this study, daily index options data that consist of trading date, expiration date, closing price, strike price and trading volume for each trading option are collected from the Securities Industry Research Centre of Asia-Pacific. We refer to a few Australian empirical studies that used daily data in their analysis, such as Do (2002), Do and Faff (2004), Li and Yang (2009) and Sharp et al (2010), as well as to other studiesconducted in markets other than Australia, such as Sarwar and Krehbiel (2000) and Li and Pearson (2007).…”

Section: Data Descriptionmentioning

confidence: 99%

“…Furthermore, the implied volatilities of options with short time to maturity behave erratically (Sarwar and Krehbiel, 2000).…”

Section: Data Sampling Proceduresmentioning

confidence: 99%

“…This suggests that the implied adjusted volatility of short-term options behaves erractically (Sarwar and Krehbiel, 2000). Table 9 reports the various transaction costs rate estimates implied from various option moneyness and time to maturity groupings for calls.…”

Section: Implied Transaction Costs Rate Estimates Kmentioning

confidence: 99%

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Implied transaction costs by Leland option pricing model: A new approach and empirical evidence

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The Valuation of Options with Restrictions on Preferences and Distributions

Camara¹

2001

Journal of Futures Markets

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This article develops a discrete-time, risk-neutral valuation relation (RNVR) for the pricing of contingent claims when preferences in the economy are characterized by decreasing absolute risk aversion and the marginal distribution of the underlying is an inverse coshnormal. The RNVR is applied to obtain closed-form expressions for calls and puts written on nondividendpaying stocks, futures contracts, foreign currencies, and dividend-paying stocks. Such pricing equations contain two parameters, the threshold and rescale parameters, not contained in the Black-Scholes valuation equation. Inverse-coshnormal option values make the approach look interesting.

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Empirical performance of alternative pricing models of currency options

Cited by 30 publications

References 33 publications

A Nonparametric Approach to Pricing Options Learning Networks

A Nonparametric Approach to Pricing Options Learning Networks

Implied transaction costs by Leland option pricing model: A new approach and empirical evidence

The Valuation of Options with Restrictions on Preferences and Distributions

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