Value-at-Risk (VaR), as a risk measure, has been widely accepted all over the world. However, VaR is not the best risk measure. VaR is not sub-additive. Moreover, it doesn't indicate the size of the potential loss. CVaR is the most attractive coherent risk measure and has been studied by many authors. In this paper, CVaR calculations are studied. In addition, the issue of volatility forecasting for CVaR calculations by using realized volatility is investigated. Realized volatility is a non- parametric measure of volatility and can be modeled and forecasted with usual time series models. Furthermore, realized volatility is based on high frequency financial data and can fully take advantage of the intraday information. Finally, empirical research is made in Chinese stock market.
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