Research Article
BibTex RIS Cite

THE INFORMATION CONTENT OF OPEN INTEREST FOR THE REALIZED RANGE-BASED VOLATILITY: EVIDENCE FROM CHINESE FUTURES MARKET

Year 2018, , 339 - 348, 30.12.2018
https://doi.org/10.17261/Pressacademia.2018.1001

Abstract

Purpose - The paper studies the impact of the infroamtion content of open interst on the realized range-based vaolatility of Chinese futures markets.

Methodology- We employ a hybrid range-based estimator to measure the integrated variance in the heterogeneous autoregressive (HAR) model, which also incorporates the variable of open interest into the HAR model on index futures prices of China Securities Index (CSI) 300.

Findings- Our findings demonstrate that the variable of open interest has a significant explanatory power with regard to the future realized volatility of the CSI 300 index futures.

Conclusion- The modified model enhances volatility forecasting performance, thereby indicating it has more accurate predictive power. Our results provide supports for the implication of the sequential information arrival hypothesis.

References

  • Andersen T.G. (1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. Journal of Finance, 51, 169-204.
  • Andersen, T.G. and T. Bollerslev (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885-905.
  • Andersen, T.G., T. Bollerslev, and F.X. Diebold (2007). Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics, 89, 701-20.
  • Andersen, T.G., T. Bollerslev, F.X. Diebold, and P. Labys (2001). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96: 42-55.
  • Andersen, T.G., T. Bollerslev, and X. Huang (2011). A reduced form framework for modeling volatility of speculative prices based on realized variation measures. Journal of Econometrics, 160, 176-189.
  • Barndorff-Nielsen, O.E. and N. Shephard (2006). Econometrics of testing for jumps in financial economics using bi-power variation. Journal of Financial Econometrics, 4, 1-30.
  • Beine, M., J. Lahaye, S. Laurent, C. J. Neely and F. C. Palm (2007). Central bank intervention and exchange rate volatility, its continuous and jump components. International Journal of Finance & Economics, 12, 201-223.
  • Bessembinder, H., and P.J. Seguin (1993). Price volatility, trading volume, and market depth: Evidence from futures markets. Journal of Financial and Quantitative Analysis, 28, 21-29.
  • Boonvorachote, T. and K. Lakmas (2016). Price volatility, trading volume, and market depth in Asian commodity futures exchanges. Kasetsart Journal of Social Sciences, 37, 53-58.
  • Chan K.C., H.-G. Fung, and W.K. Leung (2004). Daily volatility behavior in Chinese futures markets. Journal of International Financial Markets, institutions and Money, 14, 491-505.
  • Chen N.F., C.J. Cuny, and R.A. Haugen (1995). Stock volatility and the levels of the basis and open interest in futures contracts. Journal of Finance, 25.281-300.
  • Christensen, K., and M. Podolskij (2006). Range-based estimation of quadratic variation. Working paper, Aarhus School of Business.
  • Christensen, K., and M. Podolskij (2007). Realized range-based estimation of integrated variance. Journal of Econometrics, 141, 323-349.
  • Christensen, K. and M. Podolskij (2012). Asymptotic theory of range-based multipower variation. Journal of Financial Econometrics, 10, 417-456.
  • Christoffersen, P., Jacobs, K., and Mimouni, K. (2010). Volatility dynamics for the S&P500: evidence from realized volatility, daily returns, and option prices. Review of Financial Studies, 23(8), 3141-3189.
  • Christoffersen, P., B. Feunou, K. Jacobs, and N. Meddahi (2014). The economic value of realized volatility: Using high-frequency returens for option valuation. Journal of Financial and Quantitative Analysis, 49, 663-697.
  • Copeland, T. (1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 31, 1149-1168.
  • Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174-196.
  • Curci, G. and F. Corsi (2012). Discrete sine transform for multi-scale realized volatility measures. Quantitative Finance, 12, 263-279.
  • Darrat, A.F., S. Rahman, and M. Zhong (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27, 2035-2043.
  • Ferris, S.P., H.Y. Park, and K. Park (2002). Volatility, open interest, volume, and arbitrage: Evidence from the S&P 500 futures market. Applied Economics Letters, 9, 369-372.
  • Forsberg, L., and E. Ghysels. 2007. ‘Why Do Absolute Returns Predict Volatility So Well?. Journal of Financial Econometrics, 5, 31-67.
  • Gallo, G.M., and B. Pacini (2000). The effects of trading activity on market volatility. European Journal of Finance, 6, 163-75.
  • Ghysels, E., P. Santa-Clara and R. Valkanov (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 59-95.
  • Girma, P.B. and M. Mougoue (2002). An empirical examination of the relation between futures spreads volatility, volume, and open interest. Journal of Futures Markets, 22, 1083-1102.
  • Haugom, E., H. Langeland, P. Molnár, and S. Westgaard (2014). Forecasting volatility of the US oil market. Journal of Banking & Finance, 47, 1-14.
  • Iliescu, N., and S. Dutta (2016). The information content of implied volatility in developed versus developing FX markets. Applied Economics, 48, 5396-5404.
  • Jain, K. (2011). Time-varying beta: the heterogeneous autoregressive beta model. Manuscript, Duke University.
  • Jennings, R., L. Starks, and J. Fellingham (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finance, 36, 143-161.
  • Kumar, B., and A. Pandy (2010). Price volatility, trading volume and open interest: Evidence from Indian commodity futures markets. Working paper. India, Indian Institute and Management.
  • Lamoureux, C., and W. Lastrapes (1990). Heteroscedasticity in stock return data: volume versus GARCH effects. Journal of Finance, 45, 221-229.
  • Lee, B.-S., and O. Rui (2002). The dynamic relationship between stock returns and trading volume: domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
  • Liew, K.Y., and R.D. Brooks (1998). Returns and volatility in the Kuala Lumpur crude palm oil futures market. The Journal of Futures Markets, 18, 985–999.
  • Louzis, D.P., S. Xanthopoulos-Sisinis, and A.P. Refenes (2012). Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility. Applied Economics, 44, 3533-3550.
  • Lyócsa, Š., and P. Molnár (2016). Volatility forecasting of strategically linked commodity ETFs: gold-silver. Quantitative Finance, 1-14.
  • Lyócsa, Š., P.Molnár, and I. Fedorko (2016). Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland. Finance a Uver: Czech Journal of Economics & Finance, 66(5).
  • Martens, M., and D. van Dijk (2007). Measuring volatility with the realized range. Journal of Econometrics, 138, 181-207.
  • McAleer, M., and M. Medeiros (2008). Realized Volatility: A Review. Econometric Review, 27, 10-45.
  • Motladiile, B. and E.v.d.M. Smit (2003). Relationship between share index volatility, basis and open interest in futures contracts: The South African experience. South African Journal of Business Management, 34, 41-50.
  • Pu, W., Y. Chen, and F. Ma (2016). Forecasting the realized volatility in the Chinese stock market: further evidence. Applied Economics, 48, 3116-3130.
  • Ripple, R.D., and I.A. Moosa (2009). The effect of maturity, trading volume, and open interest on crude oil futures price range-based volatility. Global Finance Journal, 20, 209-219.
  • Serletis, A., and A. Shahmoradi (2006). Returns and volatility in the NYMEX Henry Hub natural gas futures market. OPEC Review: Energy Economics & Related Issues, 30, 171-186.
  • Shakeel, M., and S. Ashraf (2012). Empirical relationship between index futures prices, volume and open interest: evidence from Indian futures market. The IUP Journal of Applied Finance, 18, 48-66.
  • Souček, M. (2013). Crude oil, equity and gold futures open interest co-movements. Energy Economics, 40, 306-315.
  • Souček, M., and N. Todorova (2013). Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach. Energy Economics, 40, 586-597.
  • Tseng, T.-C., H.-C. Lai, and C.-F. Lin (2012). The impact of overnight returns on realized volatility. Applied Financial Economics, 22, 357–64.
  • Todorova, N. (2012). Volatility estimators based on daily price ranges versus the realized range. Applied Financial Economics, 22, 215-229.
  • Todorova, N. and M. Souček (2014). The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range. Economic Modelling, 36, 332-340.
  • Todorova, N., and S. Husmann (2012). A comparaive study of range-based stock return volatility estimators for the German market. Journal of Futures Markets, 32, 560-586.
  • Vortelinos, D.I., and D.D. Thomakos (2012). Realized volatility and jumps in the Athens Stock Exchange. Applied Financial Economics, 22, 97-112.
  • Xu, D. (2012). Examining realized volatility regimes under a threshold stochastic volatility model. International Journal of Finance & Economics, 17, 373-389.
  • Yen, S.M., and M.-H. Chen (2010). “Open interest, volume, and volatility: evidence from Taiwan futures markets,” Journal of Economics and Finance, 34, 113-141.
Year 2018, , 339 - 348, 30.12.2018
https://doi.org/10.17261/Pressacademia.2018.1001

Abstract

References

  • Andersen T.G. (1996). Return volatility and trading volume: An information flow interpretation of stochastic volatility. Journal of Finance, 51, 169-204.
  • Andersen, T.G. and T. Bollerslev (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885-905.
  • Andersen, T.G., T. Bollerslev, and F.X. Diebold (2007). Roughing it up: including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics, 89, 701-20.
  • Andersen, T.G., T. Bollerslev, F.X. Diebold, and P. Labys (2001). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96: 42-55.
  • Andersen, T.G., T. Bollerslev, and X. Huang (2011). A reduced form framework for modeling volatility of speculative prices based on realized variation measures. Journal of Econometrics, 160, 176-189.
  • Barndorff-Nielsen, O.E. and N. Shephard (2006). Econometrics of testing for jumps in financial economics using bi-power variation. Journal of Financial Econometrics, 4, 1-30.
  • Beine, M., J. Lahaye, S. Laurent, C. J. Neely and F. C. Palm (2007). Central bank intervention and exchange rate volatility, its continuous and jump components. International Journal of Finance & Economics, 12, 201-223.
  • Bessembinder, H., and P.J. Seguin (1993). Price volatility, trading volume, and market depth: Evidence from futures markets. Journal of Financial and Quantitative Analysis, 28, 21-29.
  • Boonvorachote, T. and K. Lakmas (2016). Price volatility, trading volume, and market depth in Asian commodity futures exchanges. Kasetsart Journal of Social Sciences, 37, 53-58.
  • Chan K.C., H.-G. Fung, and W.K. Leung (2004). Daily volatility behavior in Chinese futures markets. Journal of International Financial Markets, institutions and Money, 14, 491-505.
  • Chen N.F., C.J. Cuny, and R.A. Haugen (1995). Stock volatility and the levels of the basis and open interest in futures contracts. Journal of Finance, 25.281-300.
  • Christensen, K., and M. Podolskij (2006). Range-based estimation of quadratic variation. Working paper, Aarhus School of Business.
  • Christensen, K., and M. Podolskij (2007). Realized range-based estimation of integrated variance. Journal of Econometrics, 141, 323-349.
  • Christensen, K. and M. Podolskij (2012). Asymptotic theory of range-based multipower variation. Journal of Financial Econometrics, 10, 417-456.
  • Christoffersen, P., Jacobs, K., and Mimouni, K. (2010). Volatility dynamics for the S&P500: evidence from realized volatility, daily returns, and option prices. Review of Financial Studies, 23(8), 3141-3189.
  • Christoffersen, P., B. Feunou, K. Jacobs, and N. Meddahi (2014). The economic value of realized volatility: Using high-frequency returens for option valuation. Journal of Financial and Quantitative Analysis, 49, 663-697.
  • Copeland, T. (1976). A model of asset trading under the assumption of sequential information arrival. Journal of Finance, 31, 1149-1168.
  • Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174-196.
  • Curci, G. and F. Corsi (2012). Discrete sine transform for multi-scale realized volatility measures. Quantitative Finance, 12, 263-279.
  • Darrat, A.F., S. Rahman, and M. Zhong (2003). Intraday trading volume and return volatility of the DJIA stocks: A note. Journal of Banking and Finance, 27, 2035-2043.
  • Ferris, S.P., H.Y. Park, and K. Park (2002). Volatility, open interest, volume, and arbitrage: Evidence from the S&P 500 futures market. Applied Economics Letters, 9, 369-372.
  • Forsberg, L., and E. Ghysels. 2007. ‘Why Do Absolute Returns Predict Volatility So Well?. Journal of Financial Econometrics, 5, 31-67.
  • Gallo, G.M., and B. Pacini (2000). The effects of trading activity on market volatility. European Journal of Finance, 6, 163-75.
  • Ghysels, E., P. Santa-Clara and R. Valkanov (2006). Predicting volatility: getting the most out of return data sampled at different frequencies. Journal of Econometrics, 131, 59-95.
  • Girma, P.B. and M. Mougoue (2002). An empirical examination of the relation between futures spreads volatility, volume, and open interest. Journal of Futures Markets, 22, 1083-1102.
  • Haugom, E., H. Langeland, P. Molnár, and S. Westgaard (2014). Forecasting volatility of the US oil market. Journal of Banking & Finance, 47, 1-14.
  • Iliescu, N., and S. Dutta (2016). The information content of implied volatility in developed versus developing FX markets. Applied Economics, 48, 5396-5404.
  • Jain, K. (2011). Time-varying beta: the heterogeneous autoregressive beta model. Manuscript, Duke University.
  • Jennings, R., L. Starks, and J. Fellingham (1981). An equilibrium model of asset trading with sequential information arrival. Journal of Finance, 36, 143-161.
  • Kumar, B., and A. Pandy (2010). Price volatility, trading volume and open interest: Evidence from Indian commodity futures markets. Working paper. India, Indian Institute and Management.
  • Lamoureux, C., and W. Lastrapes (1990). Heteroscedasticity in stock return data: volume versus GARCH effects. Journal of Finance, 45, 221-229.
  • Lee, B.-S., and O. Rui (2002). The dynamic relationship between stock returns and trading volume: domestic and cross-country evidence. Journal of Banking and Finance, 26, 51-78.
  • Liew, K.Y., and R.D. Brooks (1998). Returns and volatility in the Kuala Lumpur crude palm oil futures market. The Journal of Futures Markets, 18, 985–999.
  • Louzis, D.P., S. Xanthopoulos-Sisinis, and A.P. Refenes (2012). Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility. Applied Economics, 44, 3533-3550.
  • Lyócsa, Š., and P. Molnár (2016). Volatility forecasting of strategically linked commodity ETFs: gold-silver. Quantitative Finance, 1-14.
  • Lyócsa, Š., P.Molnár, and I. Fedorko (2016). Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland. Finance a Uver: Czech Journal of Economics & Finance, 66(5).
  • Martens, M., and D. van Dijk (2007). Measuring volatility with the realized range. Journal of Econometrics, 138, 181-207.
  • McAleer, M., and M. Medeiros (2008). Realized Volatility: A Review. Econometric Review, 27, 10-45.
  • Motladiile, B. and E.v.d.M. Smit (2003). Relationship between share index volatility, basis and open interest in futures contracts: The South African experience. South African Journal of Business Management, 34, 41-50.
  • Pu, W., Y. Chen, and F. Ma (2016). Forecasting the realized volatility in the Chinese stock market: further evidence. Applied Economics, 48, 3116-3130.
  • Ripple, R.D., and I.A. Moosa (2009). The effect of maturity, trading volume, and open interest on crude oil futures price range-based volatility. Global Finance Journal, 20, 209-219.
  • Serletis, A., and A. Shahmoradi (2006). Returns and volatility in the NYMEX Henry Hub natural gas futures market. OPEC Review: Energy Economics & Related Issues, 30, 171-186.
  • Shakeel, M., and S. Ashraf (2012). Empirical relationship between index futures prices, volume and open interest: evidence from Indian futures market. The IUP Journal of Applied Finance, 18, 48-66.
  • Souček, M. (2013). Crude oil, equity and gold futures open interest co-movements. Energy Economics, 40, 306-315.
  • Souček, M., and N. Todorova (2013). Realized volatility transmission between crude oil and equity futures markets: A multivariate HAR approach. Energy Economics, 40, 586-597.
  • Tseng, T.-C., H.-C. Lai, and C.-F. Lin (2012). The impact of overnight returns on realized volatility. Applied Financial Economics, 22, 357–64.
  • Todorova, N. (2012). Volatility estimators based on daily price ranges versus the realized range. Applied Financial Economics, 22, 215-229.
  • Todorova, N. and M. Souček (2014). The impact of trading volume, number of trades and overnight returns on forecasting the daily realized range. Economic Modelling, 36, 332-340.
  • Todorova, N., and S. Husmann (2012). A comparaive study of range-based stock return volatility estimators for the German market. Journal of Futures Markets, 32, 560-586.
  • Vortelinos, D.I., and D.D. Thomakos (2012). Realized volatility and jumps in the Athens Stock Exchange. Applied Financial Economics, 22, 97-112.
  • Xu, D. (2012). Examining realized volatility regimes under a threshold stochastic volatility model. International Journal of Finance & Economics, 17, 373-389.
  • Yen, S.M., and M.-H. Chen (2010). “Open interest, volume, and volatility: evidence from Taiwan futures markets,” Journal of Economics and Finance, 34, 113-141.
There are 52 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Tseng-chan Tseng This is me 0000-0001-5259-817X

Hung-cheng Lai This is me 0000-0002-3370-4579

Conghua Wen This is me 0000-0002-3938-8108

Publication Date December 30, 2018
Published in Issue Year 2018

Cite

APA Tseng, T.-c., Lai, H.-c., & Wen, C. (2018). THE INFORMATION CONTENT OF OPEN INTEREST FOR THE REALIZED RANGE-BASED VOLATILITY: EVIDENCE FROM CHINESE FUTURES MARKET. Journal of Economics Finance and Accounting, 5(4), 339-348. https://doi.org/10.17261/Pressacademia.2018.1001

Journal of Economics, Finance and Accounting (JEFA) is a scientific, academic, double blind peer-reviewed, quarterly and open-access online journal. The journal publishes four issues a year. The issuing months are March, June, September and December. The publication languages of the Journal are English and Turkish. JEFA aims to provide a research source for all practitioners, policy makers, professionals and researchers working in the area of economics, finance, accounting and auditing. The editor in chief of JEFA invites all manuscripts that cover theoretical and/or applied researches on topics related to the interest areas of the Journal. JEFA publishes academic research studies only. JEFA charges no submission or publication fee.

Ethics Policy - JEFA applies the standards of Committee on Publication Ethics (COPE). JEFA is committed to the academic community ensuring ethics and quality of manuscripts in publications. Plagiarism is strictly forbidden and the manuscripts found to be plagiarized will not be accepted or if published will be removed from the publication. Authors must certify that their manuscripts are their original work. Plagiarism, duplicate, data fabrication and redundant publications are forbidden. The manuscripts are subject to plagiarism check by iThenticate or similar. All manuscript submissions must provide a similarity report (up to 15% excluding quotes, bibliography, abstract and method).

Open Access - All research articles published in PressAcademia Journals are fully open access; immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Open access is a property of individual works, not necessarily journals or publishers. Community standards, rather than copyright law, will continue to provide the mechanism for enforcement of proper attribution and responsible use of the published work, as they do now.