A Multivariate Generalized Orthogonal Factor GARCH Model

Markku Lanne · Pentti Saikkonen

151 citations0/0 files storedJournal of Business and Economic Statistics

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Market Dynamics and Volatility

Abstract

We propose a factor generalized autoregressive conditional heteroscedasticity (GARCH) model and develop test procedures for checking the correctness of the number of factors. Maximum likelihood estimation of the model is straightforward once computationally simple preliminary estimates are obtained. Motivated by the empirical application of the article, a mixture of Gaussian distributions is considered in addition to the conventional Gaussian likelihood. Interestingly, some parameters of the conditional covariance matrix that are not identifiable under normality, can be identified when the mixture specification is used. As an empirical example, modeling a system of exchange rate returns and testing for volatility transmission is considered.

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References

1995Multivariate Simultaneous Generalized ARCH n.d.Multivariate GARCH models: a survey 2001Testing Density Forecasts, With Applications to Risk Management 1990Asset pricing with a factor-arch covariance structure 2002GO‐GARCH: a multivariate generalized orthogonal GARCH model 1999Multivariate Density Forecast Evaluation and Calibration In Financial Risk Management: High-Frequency Returns on Foreign Exchange 2003Asymptotic theory for multivariate GARCH processes 2000Multivariate GARCH Modeling of Exchange Rate Volatility Transmission in the European Monetary System

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