Empirical Tests of Canonical Nonparametric American Option Pricing Methods

Alcock, Jamie; Auerswald, Diana

doi:10.2139/ssrn.1405842

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Pricing error appears to be greatest for all methods when pricing long-dated options, particularly for at-the-money put options. This is consistent with the results of Alcock and Auerswald (2010) and Gray, Edwards, and Kalotay (2007), among others, who find that long-dated, at-the-money options are the most difficult to price and hedge.…”

Section: Pricing Resultssupporting

confidence: 92%

“…That is, we ensure that no generated prices suffer from "look-ahead" bias. Using the same bootstrap, we then estimate the riskneutral probability measures by the minimization problem (8) for each given model, and 3 This is consistent with previous empirical studies of nonparametric pricing methods (Alcock & Auerswald, 2010;Gray, Edwards, & Kalotay, 2007). calculate the option prices by the general pricing formula (4) for call options (and by the corresponding formula for put options).…”

Section: Pricing Methodologysupporting

confidence: 61%

“…The constrained models each use two additional pricing constraints that ensure that they accurately price the most recently traded European call and put options from the same moneyness/time-toexpiry subset as the given option being priced (the first option trade of a given sample is thus not used in the calculation of the pricing performance statistics). This simple technique of choosing the constraining options differs slightly from that used by Gray, Edwards, and Kalotay (2007) and by Alcock and Auerswald (2010), but results in a larger usable sample and in significant improvements in pricing performance.…”

Section: Pricing Methodologymentioning

confidence: 99%

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Testing Alternative Measure Changes in Nonparametric Pricing and Hedging of European Options

Alcock

Smith

2013

Journal of Futures Markets

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Haley and Walker [Haley, M.R., & Walker, T. (2010). Journal of Futures Markets, 30, 983–1006] present the Euclidean and Empirical Likelihood nonparametric option pricing models as alternative tilts to Stutzer's [Stutzer, M. (1996). Journal of Finance, 51, 1633–1652] Canonical pricing method. We empirically test the comparative strengths of each of these methods using a large sample of traded options on the S&P100 Index. Furthermore, we explore an additional tilt based on Pearson's chi‐square, and derive and empirically test nonparametric delta hedges for each of these approaches. Differences in the pricing performance of the various tilts are a function of differences between the sample distribution and the real distribution of the underlying. When the sample distribution displays fatter (thinner) tails and/or higher (lower) volatility than the true distribution, the Euclidean (Pearson's chi‐square) model outperforms. Significantly, when these nonparametric methods utilize information contained in a small number of observed option prices they often outperform the implied volatility Black and Scholes [Black, F., & Scholes, M. (1973). Journal of Political Economy, 81, 637–654] model. These pricing performance differences do not translate into static and dynamic hedging performance differences. However, each of the nonparametric models induce an implied volatility smile and term structure that generally agree in form with the smile and term structure embedded in market prices. © 2013 Wiley Periodicals, Inc. Jrl Fut Mark 34:320–345, 2014

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Section: Pricing Resultssupporting

confidence: 92%

Section: Pricing Methodologysupporting

confidence: 61%

Section: Pricing Methodologymentioning

confidence: 99%

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Testing Alternative Measure Changes in Nonparametric Pricing and Hedging of European Options

Alcock

Smith

2013

Journal of Futures Markets

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“…Next, define the moneyness of an option, that is m, as the ratio of its strike price to the underlying asset price. Following Alcock and Auerswald (2010), an S&P 100 put option is called in-the-money (ITM) if ∈ m [1, 025, 1.075), deeply ITM (DITM) if ≥ m 1.075, at-the-money (ATM) if ∈ m [0.975, 1.025), out-of-the-money (OTM) if ∈ m [0.925, 0.975), and deeply OTM (DOTM) if m < 0.925, respectively. In terms of maturity, the S&P 100 put options are classified into three categories: Short-term options (maturity ≤ 42 trading days), medium-term options (42 trading days < maturity ≤ 105 trading days), and long-term options (maturity > 105 trading days).…”

Section: Empirical Analysis Based On Sandp 100 Options Datamentioning

confidence: 99%

Robust upper bounds for American put options

Xue

Liu³

2018

Journal of Futures Markets

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In this paper, we develop robust and model‐free upper bounds for American put option prices. Our bounds have all of those appealing features of the upper bounds for European options provided in DeMarzo et al. (2016, Robust option pricing: Hannan and Blackwell meet Black and Scholes, Journal of Economic Theory, 410‐434) but cover more popular derivatives in practice. Numerical and empirical investigations illustrate the performance of our method.

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Canonical Least-Squares Monte Carlo: Empirical Evidences from S&P 100 Index and IBM Puts

Liu

2010

SSRN Journal

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Empirical Tests of Canonical Nonparametric American Option Pricing Methods

Cited by 7 publications

References 17 publications

Testing Alternative Measure Changes in Nonparametric Pricing and Hedging of European Options

Testing Alternative Measure Changes in Nonparametric Pricing and Hedging of European Options

Robust upper bounds for American put options

Canonical Least-Squares Monte Carlo: Empirical Evidences from S&P 100 Index and IBM Puts

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