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Author: Simona Sanfelici
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
The aim of this paper is to measure and assess the accuracy of different volatility estimators based on high frequency data in an option pricing context. For this, we use a discrete-time stochastic volatility model based on Auto-Regressive-Gamma (ARG) dynamics for the volatility.First, ARG processes are presented both under historical and risk-neutral measure, in an affine stochastic discount factor framework. The model parameters are estimated exploiting the informative content of historical high frequency data. Secondly, option pricing is performed via Monte Carlo techniques. This framework allows us to measure the quality of different volatility estimators in terms of mispricing with respect to real option data, leaving to the ARG volatility model the role of a tool. Our analysis points out that using high frequency intra-day returns allows to obtain more accurate ex post estimation of the true (unobservable) return variation than do the more traditional sample variances based on daily returns, and this is reflected in the quality of pricing. Moreover, estimators robust to microstructure effects show an improvement over the realized volatility estimator. The empirical analysis is conducted on European options written on S&P500 index.
Author: Simona Sanfelici
Publisher:
ISBN:
Category :
Languages : en
Pages : 24
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Book Description
The aim of this paper is to measure and assess the accuracy of different volatility estimators based on high frequency data in an option pricing context. For this, we use a discrete-time stochastic volatility model based on Auto-Regressive-Gamma (ARG) dynamics for the volatility.First, ARG processes are presented both under historical and risk-neutral measure, in an affine stochastic discount factor framework. The model parameters are estimated exploiting the informative content of historical high frequency data. Secondly, option pricing is performed via Monte Carlo techniques. This framework allows us to measure the quality of different volatility estimators in terms of mispricing with respect to real option data, leaving to the ARG volatility model the role of a tool. Our analysis points out that using high frequency intra-day returns allows to obtain more accurate ex post estimation of the true (unobservable) return variation than do the more traditional sample variances based on daily returns, and this is reflected in the quality of pricing. Moreover, estimators robust to microstructure effects show an improvement over the realized volatility estimator. The empirical analysis is conducted on European options written on S&P500 index.
Author: Emilie Drop
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The aim of this paper is to evaluate how different volatility estimation techniques impact the quality of pricing option contracts. The theoretical part explains option pricing, qualitative and quantitative parameters of the Black Scholes model, and implied volatility features. The pricing performance of the Black Scholes model with historical volatilities and of the ad hoc Black Scholes model with implied volatilities are assessed with Matlab, using a real option dataset consisting of S & P 500 call options. Moreover, the specification of the regression structure used in the ad hoc Black Scholes model to estimate volatility is analysed. It is shown that the absolute smile regression structure using strike price, time to maturity and their com- bination as independent variables for one-day ahead out of sample pricing is the most accurate technique for pricing options out of all the methods considered.
Author: Evdokia Xekalaki
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
The performance of an ARCH model selection algorithm based on the standardized prediction error criterion (SPEC) is evaluated. The evaluation of the algorithm is performed by comparing different volatility forecasts in option pricing through the simulation of an options market. Traders employing the SPEC model selection algorithm use the model with the lowest sum of squared standardized one-step-ahead prediction errors for obtaining their volatility forecast. The cumulative profits of the participants in pricing one-day index straddle options always using variance forecasts obtained by GARCH, EGARCH and TARCH models are compared to those made by the participants using variance forecasts obtained by models suggested by the SPEC algorithm. The straddles are priced on the Standard and Poor 500 (Samp;P500) index. It is concluded that traders, who base their selection of an ARCH model on the SPEC algorithm, achieve higher profits than those, who use only a single ARCH model. Moreover, the SPEC algorithm is compared with other criteria of model selection that measure the ability of the ARCH models to forecast the realized intra-day volatility. In this case too, the SPEC algorithm users achieve the highest returns. Thus, the SPEC model selection method appears to be a useful tool in selecting the appropriate model for estimating future volatility in pricing derivatives.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 98
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Book Description
Author: Graham A. Davis
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ISBN:
Category :
Languages : en
Pages :
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Book Description
The opportunity to invest in or abandon a project can in principle be valued using real options techniques. In practice, option pricing has as inputs the volatility and dividend yield of the project, which are in most cases not observable via market data. Current methods of estimating these parameters are largely ad hoc, introducing potential error into the valuation process. This paper uses simple production models to formalize concepts for estimating project volatility and dividend yield in single stochastic variable option models, and provides an example of how these estimates can be used in a real option valuation exercise.
Author: Jian Zou
Publisher:
ISBN:
Category :
Languages : en
Pages : 260
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Book Description
Author: Robert A. Jarrow
Publisher:
ISBN:
Category : Derivative securities
Languages : en
Pages : 472
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Book Description
Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.
Author: Juho Kanniainen
Publisher:
ISBN:
Category :
Languages : en
Pages : 33
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Book Description
This paper uses information on VIX to improve the empirical performance of GARCH models for pricing options on the S&P 500. In pricing multiple cross-sections of options, the models' performance can clearly be improved by extracting daily spot volatilities from the series of VIX rather than by linking spot volatility with different dates by using the series of the underlying's returns. Moreover, in contrast to traditional returns-based maximum likelihood estimation (MLE), a joint MLE with returns and VIX improves option pricing performance, and for NGARCH, joint MLE can yield empirically almost the same out-of-sample option pricing performance as direct calibration does to in-sample options, but without costly computations. Finally, consistently with the existing research, this paper finds that non-affine models clearly outperform affine models.
Author: Chitranjan Sinha
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ISBN:
Category : Interest rate risk
Languages : en
Pages : 288
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Book Description
Author: I-Ming Jiang
Publisher:
ISBN:
Category :
Languages : en
Pages : 31
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Book Description
This study investigates the volatility forecasting abilities of return-based and range-based estimators for two stock indices and two individual stocks in the U.S. stock market. The forecasting performances are evaluated by two robust statistical loss functions, and further by financial applications in risk management and option pricing. Consistent with previous studies, the range-based volatility forecasts outperform in terms of statistical evaluation, Value-at-Risk calculation, and option pricing. However, return-based volatility forecasts prove superior in the evaluation of market risk capital requirements.
