Bayesian option pricing using asymmetric GARCH models

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

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Analytic derivatives of asymmetric Garch models

The Journal of Computational Finance ◽

10.21314/jcf.2003.106 ◽

2003 ◽

Vol 6 (3) ◽

pp. 21-63 ◽

Cited By ~ 1

Author(s):

George Levy

Keyword(s):

Garch Models ◽

Asymmetric Garch ◽

Derivatives Of

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Microfoundations of Heteroscedasticity: A Loss-Aversion-Based Explanation of Asymmetric GARCH Models

SSRN Electronic Journal ◽

10.2139/ssrn.2736390 ◽

2016 ◽

Cited By ~ 1

Author(s):

Mihhly Ormos ◽

Dussn Timotity

Keyword(s):

Loss Aversion ◽

Garch Models ◽

Asymmetric Garch

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Estimating and Forecasting Volatility of Financial Markets Using Asymmetric GARCH Models: An Application on Turkish Financial Markets

International Journal of Economics and Finance ◽

10.5539/ijef.v6n4p23 ◽

2014 ◽

Vol 6 (4) ◽

Cited By ~ 7

Author(s):

Rasim Ilker Gokbulut ◽

Mehmet Pekkaya

Keyword(s):

Financial Markets ◽

Garch Models ◽

Asymmetric Garch ◽

Forecasting Volatility

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Re-Examining the Nifty Returns after the First Decade of Derivative Trading in Indian Capital Market Using Non-Linear Asymmetric GARCH Models

International Journal of Innovation in the Digital Economy ◽

10.4018/jide.2012100102 ◽

2012 ◽

Vol 3 (4) ◽

pp. 29-52 ◽

Cited By ~ 1

Author(s):

Sunita Narang

Keyword(s):

Capital Market ◽

Garch Models ◽

Conditional Heteroskedasticity ◽

Indian Market ◽

Total Period ◽

Dummy Variable ◽

Asymmetric Garch ◽

Indian Stock Market ◽

Surrogate Index ◽

The Impact

This article examines the Indian stock market for conditional volatility using symmetric and asymmetric GARCH (Generalized Autoregressive Conditional Heteroskedasticity) variants with reference to a comprehensive period of 20 years from July 3, 1990 to November 30, 2010 using S&P CNX Nifty. The impact of future trading on Nifty return and volatility is assessed using dummy variable in total period and using Log (Open Interest of Nifty futures) in post-derivative period. Along with the period of two decades the analysis has also been done on a sub-period of a decade from 1995 to 2005 with NiftyJunior as surrogate index as it had no derivatives during this period. The results show that the PGARCH model is best suited to Indian market conditions.

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QML INFERENCE FOR VOLATILITY MODELS WITH COVARIATES

Econometric Theory ◽

10.1017/s0266466617000512 ◽

2018 ◽

Vol 35 (1) ◽

pp. 37-72 ◽

Cited By ~ 10

Author(s):

Christian Francq ◽

Le Quyen Thieu

Keyword(s):

Realized Volatility ◽

Garch Models ◽

Likelihood Estimator ◽

Asymptotic Results ◽

Monte Carlo Experiments ◽

True Value ◽

Volatility Models ◽

Information Sets ◽

Asymmetric Garch ◽

Quasi Maximum Likelihood

The asymptotic distribution of the Gaussian quasi-maximum likelihood estimator (QMLE) is obtained for a wide class of asymmetric GARCH models with exogenous covariates. The true value of the parameter is not restricted to belong to the interior of the parameter space, which allows us to derive tests for the significance of the parameters. In particular, the relevance of the exogenous variables can be assessed. The results are obtained without assuming that the innovations are independent, which allows conditioning on different information sets. Monte Carlo experiments and applications to financial series illustrate the asymptotic results. In particular, an empirical study demonstrates that the realized volatility can be a helpful covariate for predicting squared returns.

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