New frontiers for ARCH models

Journal of Applied Econometrics

Volumn 17, Issue 5, 2002, Pages 425-446

  Engle, Robert ^a  

^a NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0036406980     PISSN: 08837252     EISSN: None     Source Type: Journal    
DOI: 10.1002/jae.683     Document Type: Article

References (77)

1. Alexander C. 1998. Risk Management and Analysis. Wiley: New York.
2. Andersen TG, Bollerslev T. 1997. Intraday periodicity and volatility persistence in financial markets. Journal of Empirical Finance 4(2): 115-158.
3. Andersen TG, Bollerslev T. 1998. Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review 39(4): 885-905.
4. Andersen TG, Bollerslev T, Diebold FX, Labys P. 2001. The distribution of exchange rate volatility. Journal of the American Statistical Association 96: 42-55.
5. Baillie RT, Bollerslev T, Mikkelsen HO. 1996. Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 74: 3-30.
6. Bera A, Higgins ML. 1993. ARCH models: properties, estimation and testing. Journal of Economic Surveys 7: 305-366.
7. Bollerslev T. 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31: 307-327.
8. Bollerslev T. 1987. A conditional heteroskedastic model for speculating prices and rates of return. Review of Economics and Statistics 69: 542-547.
9. Bollerslev T. 1990. Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH approach. Review of Economics and Statistics 72: 498-505.
10. Bollerslev T, Engle RF, Wooldridge JM. 1988. A capital asset pricing model with time-varying covariance. Journal of Political Economy 96: No. 1, 116-131.
11. Bollerslev T, Chou RY, Kroner KF. 1992. ARCH modeling in finance: a review of the theory and empirical evidence. Journal of Econometrics 52: 5-59.
12. Bollerslev T, Engle RF, Nelson DB 1994. ARCH model. In Handbook of Econometrics Volume IV, Engle RF, McFadden DL (eds). Elsevier Science: New York; 2961-3031.
13. Bollerslev T, Wooldridge JM. 1992. Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances. Econometric Reviews 11(2): 143-172.
14. Bollerslev T, Cai J, Song FM. 2000. Intra-day periodicity, long memory volatility and macroeconomic announcement effects in the US Treasury bond market. Journal of Empirical Finance 7: 37-55.
15. Breidt FJ, Crato N, deLima P. 1998. The detection and estimation of long memory in stochastic volatility. Journal of Econometrics 83: 325-348.
16. Carnero MA, Pena D, Ruiz E. 2001. Is stochastic volatility more flexible than GARCH? Unpublished manuscript.
17. Chou RY. 2001. Forecasting financial volatilities with extreme values. The Conditional Autoregressive Range (CARR) model. Unpublished manuscript.
18. Cox J, Ingersoll J, Ross S. 1985. A theory of the term structure of interest rates. Econometrica 53: 385-408.
19. Christoffersen P, Diebold FX. 2000. How relevant is volatility forecasting for financial risk management? Review of Economics and Statistics 82(1): 12-22.
20. Christoffersen P, Hahn J, Inoue A. 2001. Testing and comparing value-at-Risk measures. Journal of Empirical Finance 8(3): July, 325-342.
21. Deo R, Hurvich C. 2000. Estimation of long memory in volatility. Unpublished manuscript.
22. De Santis G, Gerard B, Hillion P. 1997. How big is the premium for currency risk? Journal of Financial Economics 49(3): 375-412.
23. Ding Z, Granger CWJ, Engle RF. 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance 1: 83-106.
24. Duan JC. 1995. The GARCH option pricing model. Mathematical Finance 5: 13-32.
25. Duan JC. 1997. Augmented GARCH(p, q) process and its diffusion limit. Journal of Econometrics 79(1): 97-127.
26. Engel C, Frankel JA, Froot KA, Rodrigues AP. 1995. Tests of conditional mean-variance efficiency of the U.S. stock market. Journal of Empirical Finance 2: March, 3-18.
27. Engle RF. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation. Econometrica 50(4): 987-1007.
28. Engle RF. 2000. The econometrics of ultra high frequency data. Econometrica 1-22.
29. Engle RF. 2001. GARCH 101: An introduction to the use of ARCH/GARCH models in applied Econometrics, (forthcoming). Journal of Economic Perspectives.
30. Engle RF, Ishida I. 2001. Modeling variance of variance: the square-root, the afine and the CEV GARCH models. Unpublished manuscript.
31. Engle RF. 2002. Dynamic conditional correlation: a new simple class of multivariate GARCH models JBES (forthcoming).
32. Engle RF. Bollerslev T. 1986. Modeling the persistence of conditional variances. Econometric Reviews 5: 1-50.
33. Engle RF, Kane A, Noh J. 1994. Forecasting volatility and option prices of the S & P 500 Index. Journal of Derivatives 217-30.
34. Engle RF, Lilien D, Robins R. 1987. Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica 55: 391-407.
35. Engle RF. Kroner K. 1995. Multivariate simultaneous GARCH. Econometric Theory 11: 122-150.
36. Engle RF, Mustafa C. 1992. Implied ARCH models from options prices. Journal of Econometrics 52: 289-311.
37. Engle RF, Ng V. 1993. Measuring and testing the impact of news on volatility. Journal of Finance 48: 1749-1778.
38. Engle RF, Patton A. 2001. What good is a volatility model? Quantitative Finance 237-245.
39. Engle RF, Rosenberg J. 2000. Testing the volatility term structure using option hedging criteria. Journal of Derivatives 8: 10-28.
40. Engle RF, Russell JR. 1998. Autoregressive conditional duration: a new model for irregularly spaced transaction data. Econometrica 66: 1127-1162.
41. Engle RF, Shephard K. 2001. Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. UCSD Discussion Paper 2001-15, NYU Discussion Paper.
42. Engle RF, Gallo E. 2002. A model for intra-daily volatility with multiple indicators. Unpublished manuscript.
43. Epps TW. 1979. Comovements in stock prices in the very short run. Journal of the American Statistical Association 74(366): 291-298.
44. French KR. Schwert GW, Stambaugh RF. 1987. Expected stock returns and volatility. Journal of Financial Economics 19: 3-30.
45. Gallant AR, Tauchen GE. 1998. Reprojecting partially observed systems with application to interest rate diffusions. Journal of the American Statistical Association 93: 10-24.
46. Glosten LR, Jaganathan R, Runkle D. 1993. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48: 1779-1801.
47. He C, Terasvirta T. 1999a. Fourth moment structure of the GARCH (p.q) process. Econometric Theory 15: 824-846.
48. He C, Terasvirta T. 1999b. Properties of moments of a family of GARCH processes. Journal of Econometrics 92: 173-192.
49. Heston S. 1993. A closed form solution for options with stochastic volatility with applications bond and currency options. Review of Financial Studies 6: 327-343.
50. Higgins ML, Bera AK. 1992. A class of nonlinear ARCH models. International Economic Review 33: No. 1, 137-158.
51. Jacquier E, Polson NG, Rossi PE. 1994. Bayesian analysis of stochastic volatility models. Journal of Business and Economic Statistics 12: 371-417.
52. Kim Sangjoon, Shephard N, Chib S. 1998. Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65: 361-393.
53. Ledoit O, Santa-Clara P. 1998. Estimating large conditional covariance matrices with an application to international stock markets. Unpublished manuscript.
54. Lee S, Hansen B. 1994. Asymptotic theory for the GARCH(1,1) quasi-maximum likelihood estimator. Econometric Theory 10: 29-52.
55. Li WK, Ling S, McAleer M. 2002. Recent theoretical results for time series models with GARCH errors. Journal of Economic Surveys 16: 245-269.
56. Ling S, McAleer M. 2002a. Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r,s) Models. Econometric Theory 18: 722-729.
57. Ling S, McAleer M. 2002b. Stationarity and the existence of moments of a family of GARCH processes. Journal of Econometrics 106: 109-117.
58. Longstaff F, Schwartz E. 2001. Valuing American options by simulation: a simple least squares approach. The Review of Financial Studies 14: No. 1 Spring, 113-147.
59. Lumsdaine R. 1996. Consistency and asymptotic normality of the quasi-maximum likelihood estimator in IGARCH(1,1) and covariance stationary GARCH(1,1) models. Econometrica 64: 575-596.
60. Manganelli S. 2000. Volume, Volatility and the Price Impact of Trades. PhD Dissertation, UCSD.
61. McCurdy T, Stengos T. 1992. A comparison of risk premium forecasts implied by parametric and nonparametric conditional mean estimators. Journal of Econometrics 52: 225-244.
62. Nelson DB. 1990. Stationarity and persistence in the GARCH(1,1) model. Econometric Theory 6: 318-334.
63. Nelson DB. 1991. Conditional heteroscedasticity in asset returns: a new approach. Econometrica 59(2): 347-370.
64. Nelson DB. 1998. Time Series Behavior of Stock Market Volatility and Returns. PhD thesis, MIT.
65. Nelson DB, Cao CQ. 1992. Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10(2): 229-235.
66. Poon S-H, Granger CWJ. 2002. Forecasting Volatility in Financial Markets: A Review, Strathclyde Univeristy, Glasgow.
67. Rabemananjara R, Zakoian JM. 1993. Threshold ARCH models and asymmetries in volatility. Journal of Applied Econometrics 8: No. 1, 31-49.
68. Ritchken P, Trevor R. 1999. Pricing options under generalized GARCH and stochastic volatility processes. Journal of Finance 54(1): 377-402.
69. Rosenberg J, Engle RF. 2001. Empirical pricing kernels. Journal of Financial Economics (forthcoming).
70. Sandmann G, Koopman SJ. 1998. Estimation of stochastic volatility models via Monte Carlo maximum likelihood. Journal of Econometrics 87: 271-301.
71. Scholes M, Williams JT. 1977. Estimating betas from nonsynchronous data. Journal of Financial Economics 5(3): 309-327.
72. Schwert GW. 1989. Why does stock market volatility change over time? Journal of Finance 44: 1115-53.
73. Shephard N, Pitt MK. 1997. Likelihood analysis of non-Gaussian measurement time series. Biometrika Vol 84: 653-67.
74. Styan GPH. 1973. Hadamard products and multivariate statistical analysis. Linear Algebra and Its Applications 6: 217-240.
75. Taylor SJ. 1982. Financial returns modelled by the product of two stochastic processes- a study of daily sugar prices, 1961-1979. In Time Series Analysis: Theory and Practice, Andersen OE (ed.). North-Holland: Amsterdam; 203-226.
76. Taylor SJ. 1986. Modelling Financial Time Series. John Wiley: Chichester.
77. White H. 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4): May, 817-838.