Power transformation models and volatility forecasting

Journal of Forecasting

Volumn 27, Issue 7, 2008, Pages 587-606

  Sadorsky, Perry ^a   McKenzie, Michael D ^b,c  

^a YORK UNIVERSITY   (Canada)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c RMIT UNIVERSITY   (Australia)

Author keywords

Forecasting; GARCH; Power transformations; Volatility

Indexed keywords

RISK ASSESSMENT; STATISTICAL METHODS; VALUE ENGINEERING;

AUTOREGRESSIVE MODELLING; FORECAST ACCURACY; GARCH; MODEL FORECASTING; POWER; POWER TRANSFORMATION; TRANSFORMATION MODELING; VOLATILITY; VOLATILITY FORECASTING; VOLATILITY MODELING;

FORECASTING;

EID: 55349146930     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1079     Document Type: Article

References (45)

1. Alexander C. 2001. Market Models: A Guide to Financial Data Analysis. Wiley: New York.
2. Alizadeh S, Brandt MW, Diebold FX. 2002. Range-based estimation of stochastic volatility models. Journal of Finance 57(3): 1047-1091.
3. Andersen TG, Bollerslev T. 1998. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 39: 885-905.
4. Andersen TG, Bollerslev T, Christoffersen PF, Diebold FX. 2005. Volatility forecasting. Working paper, Penn Institute for Economic Research, Department of Economics.
5. Artzner P, Delbaen F, Eber JM, Heath D. 1999. Coherent measures of risk. Mathematical Finance 9: 203-228.
6. Bollerslev T. 1986. Generalised autoregressive conditional heteroskedasticity. Journal of Econometrics 31: 307-328.
7. Bollerslev T, Chou RY, Kroner KF. 1992. ARCH modelling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52: 5-59.
8. Brooks C. 2002. Introductory Econometrics for Finance. Cambridge University Press: Cambridge, UK.
9. Brooks C, Persand G. 2003. Volatility forecasting for risk management. Journal of Forecasting 22: 1-22.
10. Brooks RD, Faff RW, McKenzie MD, Mitchell H. 2000. A multi-country study of power arch models and national stock market returns. Journal of International Money and Finance 19: 377-397.
11. Christoffersen PF. 2003. Elements of Financial Risk Management. Elsevier Science: New York.
12. Degiannakis S. 2004. Volatility forecasting: evidence from a fractional integrated asymmetric power arch skewed-t model. Applied Financial Economics 14: 1333-1342.
13. Dickey D, Fuller W. 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74: 427-431.
14. Diebold FX. 1998. Elements of Forecasting. South Western College Publishing: Cincinnati, OH.
15. Diebold FX, Mariano R. 1995. Comparing predictive accuracy. Journal of Business and Economic Statistics 13: 253-265.
16. Ding Z, Granger CWJ, Engle RF. 1993. Along memory property of stock market returns and a new model. Journal of Empirical Finance 1: 83-106.
17. Engle RF. 2001. GARCH 101: an introduction to the use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives 15: 157-168.
18. Engle RF. 2002. New frontiers for ARCH models. Journal of Applied Econometrics 17: 425-446.
19. Engle RF, Lee GGJ. 1999. A long run and short run component model of stock return volatility. In Cointegration, Causality, and Forecasting: A Festschrift in Honor of Clive W. J. Granger, Engle RF, White H (ed.). Oxford University Press: Oxford; 475-497.
20. Engle RF, Ng VK. 1993. Measuring and testing the impact of news on volatility. Journal of Finance 48: 1749-1778.
21. Engle RF, Patton A. 2001. What good is a volatility model? Quantitative Finance 1: 237-245.
22. Glosten LR, Jagannathan R, Runkle DE. 1993. On the relationship between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48: 1779-1801.
23. Granger CWJ, Sin C-Y. 2000. Modelling the absolute returns of different stock indices: exploring the forecastability of an alternative measure of risk. Journal of Forecasting 19: 277-298.
24. Gray SF. 1996. Modelling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics 42: 27-62.
25. Hanke JE, Wichern DW. 2005. Business Forecasting (8th edn). Prentice-Hall: Englewood Cliffs, NJ.
26. Harris R, Sollis R. 2003. Applied Time Series Modelling and Forecasting. Wiley: Chichester.
27. Harvey D, Leybourne S, Newbold P. 1997. Testing the equality of prediction mean squared errors. International Journal of Forecasting 13: 281-291.
28. Hentschel L. 1995. All in the family: nesting symmetric and asymmetric GARCH models. Journal of Financial Economics 39: 71-104.
29. Jarque CM, Bera AK. 1980. Efficient tests for normality, homoskedasticity and serial independence of regression residuals. Economics Letters 6: 225-259.
30. Jorion P. 2002. Value at Risk: The New Benchmark for Managing Financial Risk (2nd edn). McGraw-Hill: Singapore.
31. JP Morgan. 1996. RiskMetrics: Technical Document (4th edn). JP Morgan: New York.
32. Li WK, Ling S, McAleer M. 2002. Recent theoretical results for time series models with GARCH errors. Journal of Economic Surveys 16: 245-269.
33. Lunde A, Hansen PR. 2005. A forecast comparison of volatility models: does anything beat a GARCH(1, 1)? Journal of Applied Econometrics 20: 873-889.
34. McClain KT, Humphreys HB, Boscan A. 1996. Measuring risk in the mining sector with ARCH models with important observations on sample size. Journal of Empirical Finance 3: 369-391.
35. McKenzie MD. 1999. Power transformation and forecasting the magnitude of exchange rate changes. International Journal of Forecasting 15: 49-55.
36. McKenzie MD, Mitchell H. 2002. Generalised asymmetric power ARCH modelling of exchange rate volatility. Applied Financial Economics 12: 555-564.
37. McKenzie MD, Mitchell H, Brooks RD, Faff RW. 2001. Power ARCH modelling of commodity futures data on the London metals exchange. European Journal of Finance 7: 22-38.
38. Nelson DB. 1991. Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59: 347-370.
39. Phillips PCB, Perron P. 1988. Testing for a unit root in time series regression. Biometrika 75: 335-346.
40. Sadorsky P. 2005. Stochastic volatility forecasting and risk management. Applied Financial Economics 15: 121-135.
41. Taylor S. 1986. Modelling Financial Time Series. Wiley: New York.
42. Tse YK. 1998. The conditional heteroscedasticity of the yen-dollar exchange rate. Journal of Applied Econometrics 13: 49-55.
43. Tse YK, Tsui KC. 1997. Conditional volatility in foreign exchange rates: evidence from the Malaysian Ringgit and Singapore dollar. Pacific-Basin Finance Journal 5: 345-356.
44. Tsui AK, Ho K-Y. 2004. Conditional heteroscedasticity of exchange rates: further results based on the fractionally integrated approach. Journal of Applied Econometrics 19: 637-642.
45. Zakoian J-M. 1994. Threshold heteroskedastic models. Journal of Economic Dynamics and Control 18: 931-995.