Abstract
This paper investigates whether implied volatility index can be predicted and whether the prediction of implied volatility index can improve option trading performances by checking Hang Seng Index Volatility (VHSI). The results indicate that VHSI can be predicted more accurately when considering day-of-week effect and spillover effect. Furthermore, this paper uses straddle to examine the trading performance with the real data from Hong Kong option trading market. The results suggest that option trading based on the prediction of VHSI can generate extra returns, and model specifications with day-of-week and spillover effects perform better than ones without these two effects. The results also suggest that the prediction of VHSI adds value to practical investors.
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Notes
This paper set the first sample period to start from Jan 2003 with the consideration of 9/11 terrorist attack. During that period the political crisis was huge in United States which impacted the financial markets of the whole world. The fluctuation of stock market is not representative.
The S&P 500 is a free-float capitalization-weighted index of prices of 500 large-cap common stocks actively traded in the United States. The stocks included in the S&P 500 are those of large publicly held companies that trade on either of the two largest American stock market exchanges: the New York Stock Exchange and the NASDAQ. Different from the Dow Jones index, which focuses on the performance of different industry sectors, Nasdaq is an indicator of performance of stocks of technology and growth companies.
The S&P 500 is a free-float capitalization-weighted index of prices of 500 large-cap common stocks actively traded in the United States. The stocks included in the S&P 500 are those of large publicly held companies that trade on either of the two largest American stock market exchanges: the New York Stock Exchange and the NASDAQ. Different from the Dow Jones index, which focuses on the performance of different industry sectors, Nasdaq is an indicator of performance of stocks of technology and growth companies.
The time difference between opening of the Hong Kong stock market and closure of the New York Stock Market during weekdays is only 5 h.
References
Ahoniemi, K. (2008) Modeling and Forecasting the VIX Index. Unpublished working paper.
Ahoniemi, K., & Lanne, M. (2009). Joint modeling of call and put implied volatility. International Journal of Forecasting, 25(2), 239–258.
Bollen, N. P. B., & Whaley, R. E. (2004). Does net buying pressure affect the shape of implied volatility functions? The Journal of Finance, 59(2), 711–753.
Brooks, C. & Oozeer, M. C. (2002). Modelling the implied volatility of options on long gilt futures. Journal of Business Finance and Accounting, 29(1–2), 111–37.
Buraschi, A., & Jackwerth, J. (2001). The price of a smile: hedging and spanning in option markets. Review of Financial Studies, 14(2), 495–527.
Chen, Y., & Lai, K. K. (2013). Examination on the relationship between VHSI, HSI and future realized volatility with Kalman filter. Eurasian Business Review, 3(2), 200–216.
Chen, H., & Tai-Leung, C. (2010). A principal-component approach to measuring investor sentiment. Quantitative Finance, 10(4), 339–347.
Corsi, F. (2008). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174–196.
Driessen, J., & Maenhout, P. (2007). An empirical portfolio perspective on option pricing anomalies. Review of Finance, 11(4), 561–603.
Dunis, C., Kellard, N. M., & Snaith, S. (2013). Forecasting EUR–USD implied volatility: the case of intraday data. Journal of Banking and Finance, 37(12), 4943–4957.
Engle, R. F., & Rosenberg, J. V. (2000). Testing the volatility term structure using option hedging criteria. The Journal of Derivatives, 8(1), 10–28.
Fleming, J., Ostdiek, B., & Whaley, R. E. (1995). Predicting stock market volatility: a new measure. Journal of Futures Markets, 15(3), 265–302.
Franks, J. R., & Schwartz, E. S. (1991). The stochastic behaviour of market variance implied in the prices of index options. The Economic Journal, 101(409), 1460–1475.
Giot, P. (2005a). Implied volatility indexes and daily value at risk models. The Journal of Derivatives, 12(4), 54–64.
Giot, P. (2005b). Relationships between implied volatility indexes and stock index returns. The Journal of Portfolio Management, 31(3), 92–100.
Harvey, C. R., & Whaley, R. E. (1992). Market volatility prediction and the efficiency of the S&P 100 index option market. Journal of Financial Economics, 31(1), 43–73.
Konstantinidi, E., Skiadopoulos, G., & Tzagkaraki, E. (2008). Can the evolution of implied volatility be forecasted? Evidence from European and US implied volatility indices. Journal of Banking and Finance, 32(11), 2401–2411.
Le, C. & David, D. (2014) Asset price volatility and financial contagion: analysis using the MS-VAR framework. Eurasian Economic Review, 4(2), 1–30.
Low, C. (2004). The fear and exuberance from implied volatility of S&P 100 index options. The Journal of Business, 77(3), 527–546.
Müller, U. A., Dacorogna, M. M., Davé, R., Pictet, O. V., Olsen, R. B., & Ward, J. (1995). Fractals and intrinsic time: a challenge to econometricians. Olsen and Associates preprint, 3, 9–11.
Ni, S. X., Pan, J., & Poteshman, A. M. (2008). Volatility information trading in the option market. The Journal of Finance, 63(3), 1059–1091.
Noh, J., Engle, R. F., & Kane, A. (1994). Forecasting volatility and option prices of the S&P 500 index. The Journal of Derivatives, 2(1), 17–30.
Poon, S. H., & Pope, P. (2002). Trading volatility spreads: a test of index option market efficiency. European Financial Management, 6(2), 235–260.
Simon, D. P. (2003). The Nasdaq volatility index during and after the bubble. The Journal of Derivatives, 11(2), 9–24.
Siriopoulos, C. & Fassas, A. (2008). The Information Content of VFTSE. http://ssrn.com/abstract=1307702
Tanai, Y. & Lin, K.-P. (2013). Mongolian and world equity markets: volatilities and correlations. Eurasian Economic Review, 4(1), 136–64.
Whaley, R. E. (2000). The investor fear gauge. The Journal of Portfolio Management, 26(3), 12–17.
Acknowledgments
We are particularly grateful to two anonymous referees, Dr. Hakan Danis and Merve Aricilar, editors of Eurasian Economic Review, for their constructive comments on the final version of this paper.
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Chen, Y., Lai, K.K. & Du, J. Modeling and forecasting Hang Seng index volatility with day-of-week effect, spillover effect based on ARIMA and HAR. Eurasian Econ Rev 4, 113–132 (2014). https://doi.org/10.1007/s40822-015-0013-x
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DOI: https://doi.org/10.1007/s40822-015-0013-x