Article2001

The NIG-S&ARCH model: a fat-tailed, stochastic, and autoregressive conditional heteroskedastic volatility model

119 citations0/0 files storedEconometrics Journal

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Complex Systems and Time Series Analysis

Abstract

This paper examines the capabilities of the Normal Inverse Gaussian distribution as a model for stock returns. We extend the model of Barndorff-Nielsen (1997) to allow for a richer volatility structure and compare with the existing GARCH-type models. We conclude that the proposed model outperforms some of the most praised GARCH-M models. In particular, we make a big gain in modelling the skewness of equity returns.

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References

1986Generalized autoregressive conditional heteroskedasticity 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1992ARCH modeling in finance 1973A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices 1994Threshold heteroskedastic models 1998Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models 2002Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models 1995Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation

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