| Summary: | 碩士 === 國立交通大學 === 經營管理研究所 === 95 === The conditional autoregressive range (CARR) model was proposed a dynamic model for the high/low range of asset prices within fixed time intervals. However, adopting range as the proxy of volatility has some problems. Firstly, range is highly sensitive to outliers. In addition, the CARR model with range will probably overestimate the variance of realized volatility. Based on the purpose of avoiding the effects of outliers and that of properly characterizing the dynamic structure of volatility, we utilize the robust measure of range. In other words, we adopt interquantile range as the proxy of volatility and compare the forecasting performance of the CARR model with either interquantile range or standard range. The forecasting performance measures include Mincer-Zarnowitz (MZ) regression in in-sample forecasts, quadratic loss functions, proportional loss functions and t test in out-of-sample forecasts. The samples include NY Light Crude (CL)、Dow Futures (DJ)、Nasdaq 100 Futures (ND)、NY Natural Gas (NG) and S&P 500 Futures(SP). The empirical results reveal that in the sample which has more volatile realized volatility and many extreme outliers, like NY Light Crude (CL) and NY Natural Gas (NG), the CARR model with interquantile range outperforms the CARR model with standard range, in terms of both in-sample forecasts and out-of-sample forecasts. On the contrary, in the sample which has less volatile realized volatility and small outliers, like Dow Futures (DJ), Nasdaq 100 Futures (ND) and S&P 500 Futures (SP), the results are opposite.
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