A0275
Title: Time-varying parameter heterogeneous autoregressive model with stochastic volatility
Authors: Jouchi Nakajima - Hitotsubashi University (Japan)
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Abstract: The heterogeneous autoregressive (HAR) model is known to perform well in volatility forecasting. This model formulates realized volatility (RV) as a function of past RVs with different frequencies such as daily, weekly and monthly RVs. The HAR model is extended such that the coefficients of daily, weekly and monthly RVs and the error variance may change over time. The coefficients and the log of the error variance are assumed to follow first-order autoregressive processes. A Bayesian method using an efficient Markov chain Monte Carlo is developed for the analysis of the proposed model. An empirical application with the RV calculated using the 5-minute returns of the Nikkei 225 stock index is provided.
