Forecasting stock return volatility in the presence of structural breaks

Frontiers of Economics and Globalization

Volumn 3, Issue , 2008, Pages 381-416

  Rapach, David E ^a   Strauss, Jack K ^a   Wohar, Mark E ^b  

^a SAINT LOUIS UNIVERSITY   (United States)

^b UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

Author keywords

Estimation window; GJR GARCH model; Out of sample forecasts; Structural breaks; Volatility

Indexed keywords

EID: 84867637020     PISSN: 15748715     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S1574-8715(07)00210-2     Document Type: Review

References (51)

1. Andersen, T.G., Bollerslev, T. (1998), Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 39, 885-905.
2. Andreou, E., Ghysels, E. (2002), Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics 17, 579-600.
3. Awartani, B.M.A., Corradi, V. (2005), Predicting the volatility of the S&P 500 stock index via GARCH models: the role of asymmetries. International Journal of Forecasting 21, 167-183.
4. Bates, J.M., Granger, C.W.J. (1969), The combination of forecasts. Operations Research Quarterly 20, 451-468.
5. Bekaert, G., Wu, G. (2000), Asymmetric volatility and risk in equity markets. Review of Financial Studies 13, 1-42.
6. Black, F. (1976), Studies of stock price volatility changes. In: Proceeding of the 1976 Meetings of the American Statistical Association, Business and Economics Statistics Section, pp. 177-181.
7. Bollerslev, T. (1986), Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-327.
8. Brailsford, T.J., Faff, R.W. (1996), An evaluation of volatility forecasting techniques. Journal of Banking and Finance 20, 419-438.
9. Campbell, J.Y., Hentschel, L. (1992), No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31, 281-318.
10. Christie, A. (1982), The stochastic behavior of common stock variance: value, leverage and interest rate effects. Journal of Financial Economics 10, 407-432.
11. Clark, T.E., McCracken, M.W. (2004), Improving forecast accuracy by combining recursive and rolling forecasts. Federal Reserve Bank of Kansas City Research Working Paper 04-10.
12. Clark, T.E., West, K.D. (2007), Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics 138, 291-311.
13. Corradi, V., Swanson, N.R. (2006), Predictive density evaluation. In: Elliott, G., Granger, C.W.J., Timmermann, A. (Eds.), Handbook of Economic Forecasting. Elsevier, Amsterdam, pp. 197-284.
14. de Pooter, M., van Dijk, D. (2004), Testing for changes in volatility in heteroskedastic time series -a further examination. Manuscript, Econometrics Institute Research Report 2004-38/A.
15. Diebold, F.X. (1986), Modelling the persistence of conditional variance: a comment. Econometric Reviews 5, 51-56.
16. Diebold, F.X.,Mariano, R.S. (1995), Comparing predictive accuracy. Journal of Business and Economic Statistics 13, 253-263.
17. Engle, R.F. (1982), Autoregressive conditional heteroskedasticity with estimates of UK inflation. Econometrica 50, 987-1008.
18. Engle, R.F., Bollerslev, T. (1986), Modelling the persistence of conditional variance. Econometric Reviews 5, 1-50.
19. Engle, R.F., Ng, V. (1993), Measuring and testing the impact of news on volatility. Journal of Finance 48, 1749-1778.
20. Franses, P.H., van Dijk, D. (2000), Non-Linear Time Series Models in Empirical Finance. Cambridge University Press, Cambridge.
21. French, K., Schwert, G.W., Stambaugh, R. (1987), Expected stock returns and volatility. Journal of Financial Economics 19, 3-29.
22. Glosten, L.R., Jagannathan, R., Runkle, D.E. (1993), On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48, 1779-1801.
23. Granger, C.W.J., Hyung, N. (2004), Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns. Journal of Empirical Finance 11, 399-421.
24. Hansen, P.R., Lunde, A. (2005), A forecast comparison of volatility models: does anything beat a GARCH(1, 1)? Journal of Applied Econometrics 20, 873-889.
25. Hendry, D.F. (1986), An excursion into conditional variance land. Econometric Reviews 5, 63-69.
26. Hillebrand, E. (2005), Neglecting parameters changes in GARCH models. Journal of Econometrics 129, 121-138.
27. Inclán, C., Tiao, G.C. (1994), Use of cumulative sums of squares for retrospective detection of changes in variance. Journal of the American Statistic Association 89, 913-923.
28. Jensen, S.T., Rahbek, A. (2004), Asymptotic inference for nonstationary GARCH. Econometric Theory 20, 1203-1226.
29. Kim, D., Kon, S.J. (1999), Structural change and time dependence in models of stock returns. Journal of Empirical Finance 6, 283-308.
30. Kokoszka, P., Leipus, R. (1999), Testing for parameter changes in ARCH models. Lithuanian Mathematic Journal 39, 182-195.
31. Lamoureux, C.G., Lastrapes, W.D. (1990), Persistence in variance, structural change and the GARCH model. Journal of Business and Economic Statistics 8, 225-234.
32. Lee, S., Park, S. (2001), The CUSUM of squares test for scaled changes in infinite order moving average processes. Scandinavian Journal of Statistics 28, 625-644.
33. Ling, S., McAleer, M. (2003), Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory 19, 280-310.
34. Mikosch, T., Sťariča, C. (2004), Nonstationarities in financial time series, the long-range dependence, and the IGARCH effects. Review of Economics and Statistics 86, 378-390.
35. Nelson, D.B. (1991), Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59, 347-370.
36. Newey, W.K., West, K.D. (1987), A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55, 703-708.
37. Newey, W.K., West, K.D. (1994), Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61, 631-654.
38. Ng, H.G., McAleer, M. (2004), Recursive modelling of symmetric and asymmetric volatility in the presence of extreme observations. International Journal of Forecasting 20, 115-129.
39. Pagan, A., Schwert, G.W. (1990), Alternative models for conditional stock volatility. Journal of Econometrics 45, 267-290.
40. Perron, P. (1989), The Great Crash, the oil price shock and the unit root hypothesis. Econometrica 57, 1361-1401.
41. Pesaran, M.H., Timmermann, A. (2005), Small-sample properties of forecasts from autoregressive models under structural breaks. Journal of Econometrics 129, 183-217.
42. Pesaran, M.H., Timmermann, A. (2007), Selection of estimation window in the presence of breaks. Journal of Econometrics 137, 134-161.
43. Poon, S.-H., Granger, C.W.J. (2003), Forecasting volatility in financial markets: a review. Journal of Economic Literature 41, 478-539.
44. Rapach, D.E., Strauss, J.K. (2008), Structural breaks and GARCH models of exchange rate volatility. Journal of Applied Econometrics 23, 65-90.
45. Sansó, A., Arragó, V., Carrion, J.L. (2004), Testing for change in the unconditional variance of financial time series. Revista de Economiá Financiera 4, 32-53.
46. Sentana, E. (1995), Quadratic ARCH models. Review of Economic Studies 62, 639-661.
47. Sťariča, C., Granger, C.W.J. (2005), Nonstationarities in stock returns. Review of Economics and Statistics 87, 503-522.
48. Sťariča, C., Herzel, S., Nord, T. (2005), Why does the GARCH(1, 1) model fail to provide sensible longer-horizon volatility forecasts? Manuscript, Chalmers University of Technology.
49. Timmermann, A. (2006), Forecast combinations. In: Elliott, G., Granger, C.W.J., Timmermann, A. (Eds.), Handbook of Economic Forecasting. Elsevier, Amsterdam, pp. 135-196.
50. West, K.D., Cho, D. (1995), The predictive ability of several models of exchange rate volatility. Journal of Econometrics 69, 367-391.
51. Zakoian, J.-M. (1994), Threshold heteroskedastic models. Journal of Economic Dynamics and Control 18, 931-955.