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Abstract:
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In this research, we compare the forecasting ability of various volatility models through within-sample and out-of-sample forecasting simulations.Models considered here are heterogeneous auto regression models (HAR), 1/3 model where the weight coefficients are all set to 1/3 in HAR model (ES0), and HAR model of which weight coefficients are determined by empirical similarity. We also try AR(1), ARCH/GARCH and their variants, and models incorporating the Realized Quarticity (RQ) which are referred to as ARQ, HARQ and ESQ. As stock data, we pick 6 index series in Tokyo Stock Exchange, and 24 individual stock series all of which had enough liquidity from April 1st 1999 to December 30th 2013. Minute-by-minute data were created based on high frequency data. Forecasting evaluation depends on what kind of evaluation function we employ. We make use of Patton's error function. Changing the length of estimation period and forecasting period, and also the parameter of Patton's error function, we try 27,000 patterns of forecasting simulations. We find ESQ and HARQ are almost comparative in within-sample forecasting, whereas ES0 is outstanding in out-of-sample forecasting.
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