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http://dx.doi.org/10.5351/KJAS.2016.29.1.085

Comparison of realized volatilities reflecting overnight returns  

Cho, Soojin (Department of Statistics, Ewha Womans University)
Kim, Doyeon (Department of Statistics, Ewha Womans University)
Shin, Dong Wan (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.1, 2016 , pp. 85-98 More about this Journal
Abstract
This study makes an empirical comparison of various realized volatilities (RVs) in terms of overnight returns. In financial asset markets, during overnight or holidays, no or few trading data are available causing a difficulty in computing RVs for a whole span of a day. A review will be made on several RVs reflecting overnight return variations. The comparison is made for forecast accuracies of several RVs for some financial assets: the US S&P500 index, the US NASDAQ index, the KOSPI (Korean Stock Price Index), and the foreign exchange rate of the Korea won relative to the US dollar. The RV of a day is compared with the square of the next day log-return, which is a proxy for the integrated volatility of the day. The comparison is made by investigating the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). Statistical inference of MAE and RMSE is made by applying the model confidence set (MCS) approach and the Diebold-Mariano test. For the three index data, a specific RV emerges as the best one, which addresses overnight return variations by inflating daytime RV.
Keywords
realized volatility; high frequency data; overnight return; proxy; model confidence set approach; Diebold-Mariano test;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Koopman, S. J., Jungbacker, B., and Hol, E. (2005). Forecasting daily variability of the S&P100 stock index using historical, realised and implied volatility measurements, Journal of Empirical Finance, 12, 445-475.   DOI
2 Martens, M. (2002). Measuring and forecasting S&P500 index-futures volatility using high-frequency data, Journal of Futures Markets, 22, 497-518.   DOI
3 Patton, A. J. (2011). Volatility forecast comparison using imperfect volatility proxies, Journal of Econometrics, 160, 246-256.   DOI
4 Yun, S. and Shin, D. W. (2015). Forecasting the realized variance of the log-return of Korean won US dollar exchange rate addressing jumps both in stock-trading time and in overnight, Journal of the Korean Statistical Society, 44, 390-402.   DOI
5 Anderson, T. G., Bollerslev, T., and Das, A. (1998). Testing for market microstructure effects in intraday volatility: a reassessment of the Tokyo FX experiment (No. w6666). National bureau of economic research.
6 Bandi, F. M. and Russell, J. R. (2005). Realized covariation, realized beta and microstructure noise. Unpublished paper, Graduate School of Business, University of Chicago.
7 Barndorff-Nielsen, O. E. and Shephard, N. (2002). Estimating quadratic variation using realized variance, Journal of Applied Econometrics, 17, 457-477.   DOI
8 Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy, Journal of Business & Economic Statistics, 13, 253-264.
9 Hansen, P. R. and Lunde, A. (2005). A realized variance for the whole day based on intermittent high-frequency data, Journal of Financial Econometrics, 3, 525-554.   DOI
10 Hansen, P. R., Lunde, A., and Nason, J. M. (2011). The model confidence set, Econometrica, 79, 453-497.   DOI
11 Hull, J. and White, A. (1987). The pricing of options on assets with stochastic volatilities, The Journal of Finance, 42, 281-300.   DOI