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http://dx.doi.org/10.5351/CKSS.2010.17.2.293

Forecasting KOSPI 200 Volatility by Volatility Measurements  

Choi, Young-Soo (Department of Mathematics, Hankuk University of Foreign Studies)
Lee, Hyun-Jung (Department of Mathematics, Hankuk University of Foreign Studies)
Publication Information
Communications for Statistical Applications and Methods / v.17, no.2, 2010 , pp. 293-308 More about this Journal
Abstract
In this paper, we examine the forecasting KOSPI 200 realized volatility by volatility measurements. The empirical investigation for KOSPI 200 daily returns is done during the period from 3 January 2003 to 29 June 2007. Since Korea Exchange(KRX) will launch VKOSPI futures contract in 2010, forecasting VKOSPI can be an important issue. So we analyze which volatility measurements forecast VKOSPI better. To test this hypothesis, we use 5-minute interval returns to measure realized volatilities. Also, we propose a new methodology that reflects the synchronized bidding and simultaneously takes it account the difference between overnight volatility and intra-daily volatility. The t-test and F-test show that our new realized volatility is not only different from the realized volatility by a conventional method at less than 0.01% significance level, also more stable in summary statistics. We use the correlation analysis, regression analysis, cross validation test to investigate the forecast performance. The empirical result shows that the realized volatility we propose is better than other volatilities, including historical volatility, implied volatility, and convention realized volatility, for forecasting VKOSPI. Also, the regression analysis on the predictive abilities for realized volatility, which is measured by our new methodology and conventional one, shows that VKOSPI is an efficient estimator compared to historical volatility and CRR implied volatility.
Keywords
High frequency data; realized volatility; volatility index; VKOSPI; volatility forecasting;
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1 유시용, 고중양 (2009). KOSPI200 실현변동성 예측력 제고에 관한 연구, <선물연구>, 17, 21-49.
2 이재하, 정제련 (2006). KOSPI200 옵션시장에서의 변동성지수 산출 및 분석, <증권학회지>, 35, 109-138.
3 Canina, L. and Figlewski, S. (1993). The informational content of implied volatility, Review of Financial Studies, 6, 659-681.   DOI   ScienceOn
4 Day, T. and Lewis, C. (1992). Stock market volatility and the information content of stock Index options, Journal of Economics, 52, 267-287.   DOI   ScienceOn
5 Koopman, S. J., Jungbacker, B. and Eugenie, H. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realized and implied volatility, Journal of Empirical Finance, 12, 445-475.   DOI   ScienceOn
6 Martens, M. (2002). Measuring and forecasting S&P 500 index-futures volatility using high-frequency data, The Journal of Futures Markets, 22, 497-518.   DOI   ScienceOn
7 Andersen, T. G., Bollerslev, T. and Lange, S. (1999). Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon, Journal of Empirical Finance, 6, 457-477.   DOI   ScienceOn
8 Andersen, T. G. and Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts, International Economic Review, 39, 885-905.   DOI   ScienceOn
9 Jorion, P. (1995). Predicting volatility in the foreign exchange market, Journal of Finance, 50, 507-528.   DOI   ScienceOn
10 Cox, J., Ross, S., and M. Rubinstein (1979). Option pricing: A Simplified approach, Journal of Financial Economics, 7, 220-263.
11 Owain, G. (2001). Forecasting Volatility for options pricing for the U.K stock market, The Journal for Management and Analysis, 14, 55-62.
12 Christensen, B. J. and Prabhala, N. R. (1998). The relation between implied and realized volatility, Journal of Financial Economics, 50, 125-150.   DOI   ScienceOn
13 Corrado, C. J. and Miller, T. W. (2005). The Forecast Quality of CBOE Implied Volatility Indexes, The Journal of Future Markets, 25, 339-373.   DOI   ScienceOn