Nonparametric Methods and Option Pricing PDF Download

Author:
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ISBN:
Category :
Languages : en
Pages : 24

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Author: Eric Ghysels
Publisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 24

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Author: Jianqing Fan
Publisher:
ISBN:
Category :
Languages : en
Pages : 55

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Parametric option pricing models are largely used in Finance. These models capture several features of asset price dynamics. However, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this paper, we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method estimates the state price distribution nonparametrically and is called Automatic Correction of Errors (ACE). The method is easy to implement and can be combined with any model-based pricing formula to correct the systematic biases of pricing errors. We also develop a nonparametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on Samp;P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.

Author: Qi Li
Publisher: Emerald Group Publishing
ISBN: 1849506248
Category : Business & Economics
Languages : en
Pages : 570

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Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.

Author: Jamie Alcock
Publisher:
ISBN:
Category :
Languages : en
Pages : 34

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We introduce a nonparametric method to accurately price American style contingent claims. This method uses only historical stock price data, not option price data, to generate the American option price. We test the accuracy of this method in a controlled experimental environment under both Black amp; Scholes (1973) and Heston (1993) assumptions and perform an error-metric analysis. These numerical experiments demonstrate that this method is an accurate and precise method of pricing American options under a variety of market conditions.

Author: Jamie Alcock
Publisher:
ISBN:
Category :
Languages : en
Pages : 35

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A nonparametric method of pricing American options was recently developed that requires only historical underlying price data (Alcock and Carmichael, 2008). We derive an extension to this method to include conditioning information from a small number of observed option prices. This additional information improves the overall accuracy of the method and enables pricing of illiquid options in an incomplete market. We explore the statistical properties of both the original method and our extension using a series of simulation studies. The original method slightly outperforms Black-Scholes estimators and numerical estimators (Crank-Nicholson) that use historical volatility. In contrast, the extended method presented here produces significant reductions in mean pricing errors. These reductions are most dramatic for out-of-the-money options; a result that is consistent with empirical results for related entropic methodologies for pricing European options.

Author: Cheng Few Lee
Publisher: World Scientific
ISBN: 9811202400
Category : Business & Economics
Languages : en
Pages : 5053

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This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Author: James M. Hutchinson
Publisher:
ISBN:
Category : Derivative securities
Languages : en
Pages : 68

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We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S & P 500 futures options data from 1987 to 1991. Option pricing, Learning, Finance, Black-Scholes, Hedging.

Author: Toby Daglish
Publisher:
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Category :
Languages : en
Pages :

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This article investigates the extent to which options on the Australian Stock Price Index can be explained by parametric and nonparametric option pricing techniques. In particular, comparisons are made of out-of-sample option pricing performance and hedging performance. The dataset differs from many of those used previously in the empirical options pricing literature in that it consists of American options. In addition, a broader spectrum of techniques are considered: a spline-based nonparametric technique is considered in addition to the standard kernel techniques, while the performance of a Heston stochastic volatility model is also considered. Although some evidence is found of superior performance by nonparametric techniques for in-sample pricing, the parametric methods exhibit a markedly better ability to explain future prices and show superior hedging performance.

Author: Chengzhan Chi
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 78

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Book Description
There are a variety of parametric and nonparametric option pricing models commonly used in Finance. A combination of them can enhance the pricing performance significantly. Specifically, one proposes to fit the data with a parametric method and then correct the pricing errors empirically with a nonparametric learning approach. This thesis extends Fan and Mancini's (2009) model-guided nonparametric method to barrier option pricing using market traded European option data. Adopting automatic correction of errors (ACE) method to estimate the risk neutral conditional survivor function, by which the pricing error of the initial parametric estimates is captured nonparametrically, enables the nonparametric pricing procedure to value a barrier option as a sum of sequence of European options. As a byproduct from the valuation process, this thesis also provides a modified fractional fast Fourier transform technique compute the characteristic function of the running maximum log-price of the underlying asset nonparametrically through the calibrated survivor functions.