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Author: Giovanni Barone-Adesi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model's flexibility to fit market option prices. An extensive empirical analysis based on Samp;P 500 index options shows that our model outperforms other competing GARCH pricing models and ad hoc Black-Scholes models. We show that the flexible change of measure, the asymmetric GARCH volatility, and the nonparametric innovation distribution induce the accurate pricing performance of our model. Using a nonparametric approach, we obtain decreasing state-price densities per unit probability as suggested by economic theory and corroborating our GARCH pricing model. Implied volatility smiles appear to be explained by asymmetric volatility and negative skewness of filtered historical innovations.
Author: Giovanni Barone-Adesi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We propose a new method for pricing options based on GARCH models with filtered historical innovations. In an incomplete market framework, we allow for different distributions of historical and pricing return dynamics, which enhances the model's flexibility to fit market option prices. An extensive empirical analysis based on Samp;P 500 index options shows that our model outperforms other competing GARCH pricing models and ad hoc Black-Scholes models. We show that the flexible change of measure, the asymmetric GARCH volatility, and the nonparametric innovation distribution induce the accurate pricing performance of our model. Using a nonparametric approach, we obtain decreasing state-price densities per unit probability as suggested by economic theory and corroborating our GARCH pricing model. Implied volatility smiles appear to be explained by asymmetric volatility and negative skewness of filtered historical innovations.
Author: Chrilly Donninger
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
This working paper uses as a starting point the filtered historical simulation (FHS) approach developed by Barone-Adesi et al. One builds a GRJ-GARCH model and generates Monte-Carlo return/price paths with normalized returns. This introduces a severe drift-bias. The Volatility Regime Simulation (VRS) avoids the bias by sampling from the same volatility regime.Barone-Adesi et al. transform the real-world into the risk-neutral measure. They calibrate the GARCH model to the market prices of plain-vanilla options.The current model stays in the real-measure. One simulates a realistic trading behavior by hedging the options along the Monte-Carlo paths. The model generates the stylized facts of S&P-500 index options. The overall agreement with market-prices is quite good. According the model Calls are somewhat under-, Puts are somewhat overpriced. The second part of the paper demonstrates the promising application of the model for index options trading.
Author: Giovanni Barone Adesi
Publisher: Springer
ISBN: 1137465557
Category : Business & Economics
Languages : en
Pages : 183
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Book Description
Practitioners in risk management are familiar with the use of the FHS (filtered historical simulation) to finding realistic simulations of security returns. This approach has become increasingly popular over the last fifteen years, as it is both flexible and reliable, and is now being accepted in the academic community. Simulating Security Returns is a useful guide for researchers, students, and practitioners. It uses the FHS approach to help simulate the returns of large portfolios of securities. While other simulation methods use the covariance matrix of security returns, which suffers the curse of dimensionality even for modest portfolios, Barone Adesi demonstrates how FHS can accurately adjust to current market conditions.
Author: Svetlozar T. Rachev
Publisher: John Wiley & Sons
ISBN: 0470937262
Category : Business & Economics
Languages : en
Pages : 316
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Book Description
An in-depth guide to understanding probability distributions and financial modeling for the purposes of investment management In Financial Models with Lévy Processes and Volatility Clustering, the expert author team provides a framework to model the behavior of stock returns in both a univariate and a multivariate setting, providing you with practical applications to option pricing and portfolio management. They also explain the reasons for working with non-normal distribution in financial modeling and the best methodologies for employing it. The book's framework includes the basics of probability distributions and explains the alpha-stable distribution and the tempered stable distribution. The authors also explore discrete time option pricing models, beginning with the classical normal model with volatility clustering to more recent models that consider both volatility clustering and heavy tails. Reviews the basics of probability distributions Analyzes a continuous time option pricing model (the so-called exponential Lévy model) Defines a discrete time model with volatility clustering and how to price options using Monte Carlo methods Studies two multivariate settings that are suitable to explain joint extreme events Financial Models with Lévy Processes and Volatility Clustering is a thorough guide to classical probability distribution methods and brand new methodologies for financial modeling.
Author: Lars Stentoft
Publisher:
ISBN:
Category :
Languages : en
Pages : 52
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Book Description
It contains an introduction to how simulation methods can be used to price American options and a discussion of various existing methods. An application using one of these methods, the regression based method, to the GARCH option pricing model is also provided.
Author: Chrilly Donninger
Publisher:
ISBN:
Category :
Languages : en
Pages : 14
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Book Description
This working paper uses as a starting point the filtered historical simulation (FHS) approach developed by Barone-Adesi et al. One builds a GJR-GARCH model and generates Monte-Carlo return/price paths with normalized returns. This introduces a severe drift-bias. The Stochastic Volatility Regime Simulation (SVRS) avoids the bias by sampling from the same volatility regime. As an alternative to GJR-GARCH an asymmetric HAR and a GARCH-VIX model is used. Path sampling is done in the same way. As a model free alternative a VIX based approach is additionally investigated. This alternative clearly beats the models during the pre and post-Brexit market turmoil. Barone-Adesi et al. transform the real-world into the risk-neutral measure. The current model stays in the real-measure. One simulates a realistic trading behavior by hedging the options along the Monte-Carlo paths. One can calibrate the model by adding external noise.
Author: Steven L. Heston
Publisher:
ISBN:
Category : Capital assets pricing model
Languages : en
Pages : 44
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Book Description
Author: Stylianos Perrakis
Publisher: Springer
ISBN: 3030115909
Category : Business & Economics
Languages : en
Pages : 277
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Book Description
This book illustrates the application of the economic concept of stochastic dominance to option markets and presents an alternative option pricing paradigm to the prevailing no arbitrage simultaneous equilibrium in the frictionless underlying and option markets. This new methodology was developed primarily by the author, working independently or jointly with other co-authors, over the course of more than thirty years. Among others, it yields the fundamental Black-Scholes-Merton option value when markets are complete, presents a new approach to the pricing of rare event risk, and uncovers option mispricing that leads to tradeable strategies in the presence of transaction costs. In the latter case it shows how a utility-maximizing investor trading in the market and a riskless bond, subject to proportional transaction costs, can increase his/her expected utility by overlaying a zero-net-cost portfolio of options bought at their ask price and written at their bid price, irrespective of the specific form of the utility function. The book contains a unified presentation of these methods and results, making it a highly readable supplement for educators and sophisticated professionals working in the popular field of option pricing. It also features a foreword by George Constantinides, the Leo Melamed Professor of Finance at the Booth School of Business, University of Chicago, USA, who was a co-author in several parts of the book.
Author: B. Wade Brorsen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Generalized autoregressive conditional heteroskedasticity (GARCH) option pricing models (OPM) with historical volatility have proven superior to the log-normality assumption of the Black option pricing model with historical volatility. This paper estimates implied volatilities from GARCH OPM. The estimated implied volatilities are used to forecast option premia. The GARCH implied volatilities are more stable than the Black implied volatilities. The GARCH OPM with implied volatility should provide better guidance to market makers and arbitragers than the Black option pricing model with implied volatility for options ranging from six to sixteen days to maturity. For options ranging from 21 to 50 days to maturity the Black OPM with implied volatility should provide better guidance to market makers and arbitragers than the GARCH OPM with implied volatility.
Author: George Dotsis
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This paper explores the finite sample properties of the GARCH option pricing model proposed by Heston and Nandi (2000). Simulation results show that the maximum likelihood estimators of the GARCH process may contain substantial estimation biases, even when samples as large as 3,000 observations are used. However, we find that these biases cause significant mispricings only for short-term, out-of-the-money options. It is shown that, given an adequate estimation sample, this bias can be reduced considerably by employing the jackknife resampling method.
