Volatility forecasting using related markets' information for the Tokyo stock exchange
Abstract Due to lack of information, volatility cannot be estimated via a high frequency approach when markets are non trading. In this paper, we focus on volatility forecasting for the Tokyo Stock Exchange (TSE) using high frequency data of related assets traded in international markets when TSE is closed. We use the heterogenous autoregressive model to identify an optimal approach of this additional information for the ten largest TSE listed stocks, TOPIX and Nikkei 225. The usefulness of harnessing global and neighbour market information in forecasting the TSE market volatility is confirmed through in depth empirical analysis. Our findings have important implications for investors and policy makers.