Multivariate fractionally integrated APARCH modeling of stock market volatility: A multi-country study

Journal of Empirical Finance - Tập 18 - Trang 147-159 - 2011
Christian Conrad1, Menelaos Karanasos2, Ning Zeng3
1University of Heidelberg, Faculty of Economics and Social Studies, Bergheimer Str. 58, 69115 Heidelberg, Germany
2Economics and Finance, Brunel University, Uxbridge, West London, UB3 3PH, UK
3International Business School, Jinan University, Zhuhai 519070, China

Tài liệu tham khảo

Baillie, 1996, Long memory processes and fractional integration in econometrics, J. Econometrics, 73, 5, 10.1016/0304-4076(95)01732-1 Baillie, 2009, Modeling long memory and structural breaks in conditional variances: an adaptive FIGARCH approach, J. Econ. Dyn. Control, 33, 1577, 10.1016/j.jedc.2009.02.009 Baillie, 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, J. Econometrics, 74, 3, 10.1016/S0304-4076(95)01749-6 Bauwens, 2005, A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models, J. Bus. Econ. Stat., 23, 346, 10.1198/073500104000000523 Bauwens, 2006, Multivariate GARCH models, J. Appl. Econometrics, 21, 79, 10.1002/jae.842 Beine, 2002, Central bank intervention and foreign exchange rates: new evidence from FIGARCH estimations, J. Int. Money Finance, 21, 115, 10.1016/S0261-5606(01)00040-7 Bollerslev, 1986, Generalized autoregressive conditional heteroskedasticity, J. Econometrics, 31, 307, 10.1016/0304-4076(86)90063-1 Bollerslev, 1996, Modeling and pricing long memory in stock market volatility, J. Econometrics, 73, 151, 10.1016/0304-4076(95)01736-4 Bollerslev, 1992, ARCH modeling in finance: a review of the theory and empirical evidence, J. Econometrics, 52, 5, 10.1016/0304-4076(92)90064-X Brooks, 2000, A multi-country study of power ARCH models and national stock market returns, J. Int. Money Finance, 19, 377, 10.1016/S0261-5606(00)00011-5 Campos, 2008, Growth, volatility and political instability: non-linear time-series evidence for Argentina, 1890–2000, Econ. Lett., 100, 135, 10.1016/j.econlet.2007.12.013 Caporin, 2003, Identification of long memory in GARCH models, Stat. Meth. Appl., 12, 133, 10.1007/s10260-003-0056-0 Christensen, 2007, The effect of long memory in volatility on stock market fluctuations, Rev. Econ. Stat., 89, 684, 10.1162/rest.89.4.684 Christensen, 2010, Long memory in stock market volatility and the volatility-in-mean effect: the FIEGARCH-M model, J. Empirical Finance, 17, 460, 10.1016/j.jempfin.2009.09.008 Conrad, C., forthcoming. Non-negativity conditions for the hyberbolic GARCH model. J. Econometrics. doi:10.1016/j.jeconom.2010.03.045. Conrad, 2006, Inequality constraints in the fractionally integrated GARCH model, J. Financ. Econometrics, 3, 413, 10.1093/jjfinec/nbj015 Conrad, 2006, The impulse response function of the long memory GARCH model, Econ. Lett., 90, 34, 10.1016/j.econlet.2005.07.001 Conrad, 2010, Negative volatility spillovers in the unrestricted ECCC-GARCH model, Econometric Theory, 26, 838, 10.1017/S0266466609990120 Conrad, C., Lamla, M.J., 2010. The high-frequency response of the EUR-USD exchange rate to ECB communication. Journal of Money, Credit and Banking, forthcoming. Conrad, 2010, On the transmission of memory: inflation persistence and the Great Moderation, Unpublished manuscript, University of Heidelberg Dark, J., 2004. Bivariate error correction FIGARCH and FIAPARCH models on the Australian All Ordinaries Index and its SPI futures. Monash University, Unpublished paper. Davidson Degiannakis, 2004, Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model, Appl. Financ. Econ., 14, 1333, 10.1080/0960310042000285794 Diebold, 1995, Comparing predictive accuracy, J. Bus. Econ. Stat., 13, 253 Ding, 1996, Modeling volatility persistence of speculative returns: a new approach, J. Econometrics, 73, 185, 10.1016/0304-4076(95)01737-2 Ding, 1993, A long memory property of stock market returns and a new model, J. Empirical Finance, 1, 83, 10.1016/0927-5398(93)90006-D Engle, 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation, Econometrica, 50, 987, 10.2307/1912773 Ericsson, 1992, Parameter constancy, mean square forecast errors, and measuring forecast performance: an exposition, extension and illustration, J. Policy Model., 14, 465, 10.1016/0161-8938(92)90017-7 Geweke, 1986, Modeling the persistence of conditional variances: a comment, Econometric Rev., 5, 57, 10.1080/07474938608800097 Glosten, 1993, On the relation between the expected value and the volatility of the nominal excess return on stocks, J. Finance, 48, 1779, 10.1111/j.1540-6261.1993.tb05128.x Granger, 2004, Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns, J. Empirical Finance, 11, 399, 10.1016/j.jempfin.2003.03.001 Hansen, 2005, A forecast comparison of volatility models: does anything beat a GARCH(1, 1), J. Appl. Econometrics, 20, 873, 10.1002/jae.800 Hansen, 2006, Consistent ranking of volatility models, J. Econometrics, 131, 97, 10.1016/j.jeconom.2005.01.005 Harvey, 1997, Testing the equality of prediction mean squared errors, Int. J. Forecasting, 13, 281, 10.1016/S0169-2070(96)00719-4 Harvey, 1998, Tests for forecast encompassing, J. Bus. Econ. Stat., 16, 254 He, 1999, Statistical properties of the asymmetric power ARCH model, 462 Higgins, 1992, A class of nonlinear ARCH models, Int. Econ. Rev., 33, 137, 10.2307/2526988 Hyung, 2008, A source of long memory in volatility, 329 Karanasos, 2009, Dual long-memory, structural breaks and the link between turnover and the range-based volatility, J. Empirical Finance, 16, 838, 10.1016/j.jempfin.2009.06.001 Karanasos, 2006, A re-examination of the asymmetric power ARCH model, J. Empirical Finance, 13, 113, 10.1016/j.jempfin.2005.05.002 Karanasos, 2008, Is the relationship between inflation and its uncertainty linear?, Ger. Econ. Rev., 9, 265, 10.1111/j.1468-0475.2008.00433.x Karanasos, 2003, On the autocorrelation properties of long-memory GARCH processes, J. Time Ser. Anal., 25, 265, 10.1046/j.0143-9782.2003.00349.x Karanasos, 2006, On the order of integration of monthly US ex-ante and ex-post real interest rates: new evidence from over a century of data, Econ. Lett., 90, 163, 10.1016/j.econlet.2005.07.021 Kim, 2005, The volume–volatility relationship and the opening of the Korean stock market to foreign investors after the financial turmoil in 1997, Asia-Pac. Financ. Mark., 12, 245, 10.1007/s10690-006-9024-7 McCurdy, T. H., Michaud, P. K., 1996. Capturing long memory in the volatility of equity returns: a fractionally integrated asymmetric power ARCH model. University of Toronto, unpublished manuscript. Ñíguez, 2007, Volatility and VaR forecasting in the Madrid stock exchange, Span. Econ. Rev., 10, 169, 10.1007/s10108-007-9030-6 Pantula, 1986, Modeling the persistence in conditional variances: a comment, Econometric Rev., 5, 71, 10.1080/07474938608800099 Patton, A. J., forthcoming. Volatility forecast comparison using imperfect volatility proxies. J. Econometrics. doi:10.1016/j.jeconom.2010.03.034. Poon, 2003, Forecasting volatility in financial markets: a review, J. Econ. Lit., XLI, 478, 10.1257/.41.2.478 Schwert, 1990, Stock volatility and the crash of '87, Rev. Financ. Stud., 3, 77, 10.1093/rfs/3.1.77 Silvennoinen, 2007, Multivariate GARCH models Taylor, 1986 Tse, 1998, The conditional heteroscedasticity of the yen–dollar exchange rate, J. Appl. Econometrics, 13, 49, 10.1002/(SICI)1099-1255(199801/02)13:1<49::AID-JAE459>3.0.CO;2-O West, 1998, Regression-based tests of predictive ability, Int. Econ. Rev., 39, 817, 10.2307/2527340 Zakoian, 1994, Threshold heteroskedastic model, J. Econ. Dyn. Control, 18, 931, 10.1016/0165-1889(94)90039-6