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Sheera, Ved Pal
- Spillovers and Transmission in Emerging and Mature Markets Implied Volatility Indices
Abstract Views :282 |
PDF Views:0
Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March. Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets.
Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Authors
Affiliations
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
Source
International Journal of Financial Management, Vol 2, No 4 (2012), Pagination: 33-46Abstract
Purpose: The present study examine implied volatility spillover and transmission between emerging (India) and mature stock markets (US, France, Germany and Switzerland), measured by their respective implied volatility indices i.e. IVIX, VIX, VCAC, VDAX and VSMI.Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March. Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets.
Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Keywords
Implied Volatility Index, Indian Stock Market, BEKK-GARCH, DCC, VIX, VSMI
References
- Aboura, S. (2003). International Transmission of Volatility: A Study on the Volatility Indexes VX1, VDAX and VIX. Working Paper: ESSEC Business School.
- Ahoniemi, K. (2008). Modelling and Forecasting the VIX Index. Retrieved 25 August, 2010 from http://ssrn. com/sol3/papers.cfm?abstract_id=1033812
- Äjiö, J. (2007). Implied Volatility Term Structure Linkages between VDAX, VSMI and VSTOXX Volatility Indices. Global Finance Journal, 18, pp. 290-302.
- Badshah, I. U. (2010). Asymmetric Return-Volatility Relation, Volatility Transmission and Implied Volatility Indices. Working Paper New Zealand: Auckland University of Technology.
- Blair, B., Poon, S. H. & Taylor, S. (2001). Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns. Journal of Econometrics, 105, pp. 5-27.
- Bollerslev, T. (1990). Modeling the Coherence in Short- Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, pp. 498-505.
- Brenner, M. & Galai, D. (1993). Hedging Volatility in Foreign Currencies. The Journal of Derivatives, 1, pp. 53-59.
- Carr, P. & Wu, L. (2006). A Tale of Two Indices. Journal of Derivatives, 13, pp. 13-29.
- Christenssen, B. & Prabbala, N. (1998). The Relation between Implied and Realized Volatility. Journal of Financial Economics, 50, pp. 125-150.
- Cohen, G. & Qadan, M. (2010). Is Gold Still a Shelter to Fear? American Journal of Social and Management Sciences, 1, pp. 39-43.
- D.Börse. (2005). Guide to The Volatility Indices of Deutche Börse, Version 2.0.
- Degiannakis, S. A. (2008). Forecasting VIX. Journal of Money, Investment and Banking, 4, pp. 5-19.
- Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with A Unit Root. Econometrica, 49, pp. 1057-72.
- Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics, 20, pp. 339-350.
- Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Economic Theory, 11, pp. 122-150.
- Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp. 1749-1801.
- Engle, R. F. & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Paper Series, 8554.
- Fernandes, M., Medeiros, M. C. & Scharth, M. (2007). Modelling and Predicting the CBOE Market Volatility Index. Retrieved 25 August, 2011 from http://www.econ.puc-rio.br/PDF/td548.pdf
- Fleming, J., Ostdiek, B. & Whaley, R. E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Fleming, J., Ostdiek, B. & Whaley, R. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Galai, D. (1979). A Proposal for Indexes for Traded Call Options. Journal of Finance, 34, pp. 1157-72.
- Gastineau, G. (1977). An Index of Listed Option Premiums. Financial Analyst’s Journal, 33, pp. 70-75.
- Gazda, V. & Výrost, T. (2003). Application Of GARCH Models In Forecasting The Volatility of The Slovak Share Index (Sax). BIATEC, pp. 11, 17-20.
- Giot, P. (2005). Relationships between Implied Volatility Indices and Stock Index Returns. Journal of Portfolio Management, 31, pp. 92-100.
- Gonz´alez, M. T. & Novales, A. (2009). Are Volatility Indices in International Stock Markets Forward Looking? RACSAM Rev. R. Acad. Cien. Serie A. Mat. (Applied Mathematics), pp. 103, pp. 339-352.
- Hamao, Y., Masulis, R. & Ng, V. (1990). Correlation in Price Changes and Volatility across International Stock Markets. Review of Financial Studies, 3, pp. 281-307.
- Koutmos, G. (1996). Modeling the Dynamic Interdependence of Major European Stock Markets. Journal of Business Finance and Accounting, 23, pp. 975-988.
- Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992). Testing the Null Hypothesis of Trend Stationarity. Journal of Econometrics, 54, pp. 159- 178.
- Maghrebi, N., Kim, M. S. & Nishina, K. (2007). The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations. Asia- Pacific Journal of Financial Studies, 36, pp. 163- 187.
- Mayhew, S. (1995). Implied Volatility. Financial Analyst Journal, July-August, pp. 8-20.
- Moraux, F., Navatte, P. & Villa, C. (1999). The Predictive Power of the French Market Volatility Index: A Multi Horzon Study. European Finance Review, 2, pp. 303-320.
- Nikkinen, J. & Sahlström, P. (2004). International Transmission of Uncertainty Implicit in Stock Index Option Prices. Global Finance Journal, 15, pp. 1-15. Phillips, R. C. B. & Perron. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, pp. 335-346.
- Sarwar, G. (2010). The VIX Market Volatility Index and U.S. Stock Index Returns. International Journal of Business Research, 10, pp. 166-176.
- SIX Swiss Exchange Ltd. (2010). Guide Governing Volatility Index VSMI®. .
- Skiadopoulos, G. (2004). The Greek Implied Volatility Index: Construction and Properties. Applied Financial Economics, 14, pp. 187-1196.
- Tse, Y. K. & Tsui, A. K. C. (2002). A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, pp. 351-362.
- Wagner, N. & Szimayer, A. (2004). Local and Spillover Shocks in Implied Market Volatility: Evidence for the U.S. and Germany. Research in International Business and Finance, 18, pp. 237-251.
- Whaley, R. E. (1993). Derivatives on Market Volatility: Hedging Tools Long Overdue. Journal of Derivatives, 1, pp. 71-84.
- Whaley, R. E. (2000). The Investor Fear Gauge. Journal of Portfolio Management, 26, pp. 12-17.
- Spillovers and Transmission in Emerging and Mature Markets Implied Volatility Indices
Abstract Views :300 |
PDF Views:0
Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March.
Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets. Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Authors
Affiliations
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
1 Professor, Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar (Haryana)
2 Professor, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
3 JRF, Haryana School of Business, Guru Jambheshwar University of Science & Technology, Hisar, Haryana
Source
International Journal of Financial Management, Vol 2, No 4 (2012), Pagination: 47-59Abstract
Purpose: The present study examine implied volatility spillover and transmission between emerging (India) and mature stock markets (US, France, Germany and Switzerland), measured by their respective implied volatility indices i.e. IVIX, VIX, VCAC, VDAX and VSMI.Methodology: The asymmetries in Implied Volatility (IV) indices of selected countries are examined using Engle and Ng (1993) test. The spillovers and transmission are examined in multivariate-GARCH framework using BEKK and DCC model. The analysis is done using weekly data for period spanning from Nov, 2007 to Oct, 2011March.
Findings: The main findings of study document asymmetries in the IV indices exist for the Indian, American and French markets. The BEKK-GARCH model results show that conditional variances of implied VI of India, Germany, French and Switzerland strongly affected by their own past shocks and volatility effects. The DCC model reveals that there is a moderate-level of correlation between the selected markets. Practical Implications: The results of the present study can be used by the portfolio managers and market participant for yielding the diversification benefits in short-run by including IV indices as an asset in their portfolio.
Keywords
Implied Volatility Index, Indian Stock Market, BEKK-GARCH, DCC, VIX, VSMIReferences
- Aboura, S. (2003). International Transmission of Volatility: A Study on the Volatility Indexes VX1, VDAX and VIX. Working Paper: ESSEC Business School.
- Ahoniemi, K. (2008). Modelling and Forecasting the VIX Index. Retrieved 25 August, 2010 from http://ssrn. com/sol3/papers.cfm?abstract_id=1033812
- Äjiö, J. (2007). Implied Volatility Term Structure Linkages between VDAX, VSMI and VSTOXX Volatility Indices. Global Finance Journal, 18, pp. 290-302.
- Badshah, I. U. (2010). Asymmetric Return-Volatility Relation, Volatility Transmission and Implied Volatility Indices. Working Paper New Zealand: Auckland University of Technology.
- Blair, B., Poon, S. H. & Taylor, S. (2001). Forecasting S&P 100 Volatility: The Incremental Information Content of Implied Volatilities and High Frequency Index Returns. Journal of Econometrics, 105, pp. 5-27.
- Bollerslev, T. (1990). Modeling the Coherence in Short- Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, pp. 498-505.
- Brenner, M. & Galai, D. (1993). Hedging Volatility in Foreign Currencies. The Journal of Derivatives, 1, pp. 53-59.
- Carr, P. & Wu, L. (2006). A Tale of Two Indices. Journal of Derivatives, 13, pp. 13-29.
- Christenssen, B. & Prabbala, N. (1998). The Relation between Implied and Realized Volatility. Journal of Financial Economics, 50, pp. 125-150.
- Cohen, G. & Qadan, M. (2010). Is Gold Still a Shelter to Fear? American Journal of Social and Management Sciences, 1, pp. 39-43.
- D.Börse. (2005). Guide to The Volatility Indices of Deutche Börse, Version 2.0.
- Degiannakis, S. A. (2008). Forecasting VIX. Journal of Money, Investment and Banking, 4, pp. 5-19.
- Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with A Unit Root. Econometrica, 49, pp. 1057-72.
- Engle, R. F. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models. Journal of Business and Economic Statistics, 20, pp. 339-350.
- Engle, R. F. & Kroner, K. F. (1995). Multivariate Simultaneous Generalized ARCH. Economic Theory, 11, pp. 122-150.
- Engle, R. F. & Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp. 1749-1801.
- Engle, R. F. & Sheppard, K. (2001). Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. NBER Working Paper Series, 8554.
- Fernandes, M., Medeiros, M. C. & Scharth, M. (2007). Modelling and Predicting the CBOE Market Volatility Index. Retrieved 25 August, 2011 from http://www.econ.puc-rio.br/PDF/td548.pdf
- Fleming, J., Ostdiek, B. & Whaley, R. E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Fleming, J., Ostdiek, B. & Whaley, R. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, pp. 265-302.
- Galai, D. (1979). A Proposal for Indexes for Traded Call Options. Journal of Finance, 34, pp. 1157-72.
- Gastineau, G. (1977). An Index of Listed Option Premiums. Financial Analyst’s Journal, 33, pp. 70-75.
- Gazda, V. & Výrost, T. (2003). Application Of GARCH Models In Forecasting The Volatility of The Slovak Share Index (Sax). BIATEC, pp. 11, 17-20.
- Giot, P. (2005). Relationships between Implied Volatility Indices and Stock Index Returns. Journal of Portfolio Management, 31, pp. 92-100.
- Gonz´alez, M. T. & Novales, A. (2009). Are Volatility Indices in International Stock Markets Forward Looking? RACSAM Rev. R. Acad. Cien. Serie A. Mat. (Applied Mathematics), pp. 103, pp. 339-352.
- Hamao, Y., Masulis, R. & Ng, V. (1990). Correlation in Price Changes and Volatility across International Stock Markets. Review of Financial Studies, 3, pp. 281-307.
- Koutmos, G. (1996). Modeling the Dynamic Interdependence of Major European Stock Markets. Journal of Business Finance and Accounting, 23, pp. 975-988.
- Kwiatkowski, D., Phillips, P. C. B., Schmidt, P. & Shin, Y. (1992). Testing the Null Hypothesis of Trend Stationarity. Journal of Econometrics, 54, pp. 159- 178.
- Maghrebi, N., Kim, M. S. & Nishina, K. (2007). The KOSPI200 Implied Volatility Index: Evidence of Regime Switches in Volatility Expectations. Asia- Pacific Journal of Financial Studies, 36, pp. 163- 187.
- Mayhew, S. (1995). Implied Volatility. Financial Analyst Journal, July-August, pp. 8-20.
- Moraux, F., Navatte, P. & Villa, C. (1999). The Predictive Power of the French Market Volatility Index: A Multi Horzon Study. European Finance Review, 2, pp. 303-320.
- Nikkinen, J. & Sahlström, P. (2004). International Transmission of Uncertainty Implicit in Stock Index Option Prices. Global Finance Journal, 15, pp. 1-15.
- Phillips, R. C. B. & Perron. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, pp. 335-346.
- Sarwar, G. (2010). The VIX Market Volatility Index and U.S. Stock Index Returns. International Journal of Business Research, 10, pp. 166-176.
- SIX Swiss Exchange Ltd. (2010). Guide Governing Volatility Index VSMI®. .
- Skiadopoulos, G. (2004). The Greek Implied Volatility Index: Construction and Properties. Applied Financial Economics, 14, pp. 187-1196.
- Tse, Y. K. & Tsui, A. K. C. (2002). A Multivariate GARCH Model with Time-Varying Correlations. Journal of Business and Economic Statistics, 20, pp. 351-362.
- Wagner, N. & Szimayer, A. (2004). Local and Spillover Shocks in Implied Market Volatility: Evidence for the U.S. and Germany. Research in International Business and Finance, 18, pp. 237-251.
- Whaley, R. E. (1993). Derivatives on Market Volatility: Hedging Tools Long Overdue. Journal of Derivatives, 1, pp. 71-84.
- Whaley, R. E. (2000). The Investor Fear Gauge. Journal of Portfolio Management, 26, pp. 12-17.