標題: Forecasting Time-Varying Covariance with a Robust Bayesian Threshold Model
作者: Wu, Chih-Chiang
Lee, Jack C.
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: dynamic conditional correlation;generalized autoregressive conditional heteroskedasticity;hedge performance;Markov chain Monte Carlo;value at risk
公開日期: 1-Aug-2011
摘要: This paper proposes a robust multivariate threshold vector autoregressive model with generalized autoregressive conditional heteroskedasticities and dynamic conditional correlations to describe conditional mean, volatility and correlation asymmetries in financial markets. In addition, the threshold variable for regime switching is formulated as a weighted average of endogenous variables to eliminate excessively subjective belief in the threshold variable decision and to serve as the proxy in deciding which market should be the price leader. The estimation is performed using Markov chain Monte Carlo methods. Furthermore, several meaningful criteria are introduced to assess the forecasting performance in the conditional covariance matrix. The proposed methodology is illustrated using daily S&P500 futures and spot prices. Copyright (C) 2010 John Wiley & Sons, Ltd.
URI: http://dx.doi.org/10.1002/for.1183
http://hdl.handle.net/11536/14031
ISSN: 0277-6693
DOI: 10.1002/for.1183
期刊: JOURNAL OF FORECASTING
Volume: 30
Issue: 5
起始頁: 451
結束頁: 468
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