Monitoring disruptions in financial markets

Journal of Econometrics

Volumn 135, Issue 1-2, 2006, Pages 77-124

  Andreou, Elena ^a   Ghysels, Eric ^b,c  

^a UNIVERSITY OF CYPRUS   (Cyprus)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

^c UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Boundary crossing; Brownian bridge; CUSUM; GARCH; High frequency data; Local power; Power variation; Quadratic variation; Sequential tests; Structural change

Indexed keywords

BOUNDARY CROSSING; BROWNIAN BRIDGE; CUSUM; GARCH; HIGH-FREQUENCY DATA; LOCAL POWER; POWER VARIATION; QUADRATIC VARIATION; SEQUENTIAL TESTS; STRUCTURAL CHANGE;

COMPUTER SIMULATION; INFORMATION ANALYSIS; MARKETING; NONLINEAR PROGRAMMING; REAL TIME SYSTEMS; RISK MANAGEMENT; SAMPLING;

FINANCIAL DATA PROCESSING;

EID: 33747888277     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2005.07.023     Document Type: Article

References (61)

1. Altissimo F., and Corradi V. Strong rules for detecting the number of breaks in a time series. Journal of Econometrics 117 (2003) 207-244
2. Andersen, T.G., Bollerslev, T., Diebold, F.X., 2003. Parametric and nonparametric volatility measurement. In: Hansen, L.P., Ait-Sahalia, Y. (Eds.), Handbook of Financial Econometrics. North-Holland, Amsterdam, forthcoming.
3. Andreou E., and Ghysels E. Detecting multiple breaks in financial market volatility dynamics. Journal of Applied Econometrics 17 5 (2002) 579-600
4. Andreou, E., Ghysels, E., 2003. Quality control for financial risk management. Discussion paper, UNC.
5. Andreou E., and Ghysels E. The impact of sampling frequency and volatility estimators on change-point tests. Journal of Financial Econometrics 2 (2004) 290-318
6. Back K. Asset pricing for general processes. Journal of Mathematical Economics 20 (1991) 305-371
7. Barndorff-Nielsen O., and Shephard N. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society Series B 64 (2001) 253-280
8. Barndorff-Nielsen, O., Shephard, N., 2003. Realised power variation and stochastic volatility models. Bernoulli 243-265, 1109-1111.
9. Barndorff-Nielsen, O., Jacod, J., Shephard, N., 2004. Limit theorems for bipower variation in financial econometrics. Discussion paper, Aarhus and Oxford University.
10. Berkes I., Gombay E., Horvath L., and Kokoszka P. Sequential change-point detection in GARCH(p,q) models. Econometric Theory 20 (2004) 1140-1167
11. Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 3 (1986) 307-327
12. Bollerslev, T., Engle, R.F., Nelson, D.B., 1994. ARCH models. In: Engle, R.F., McFadden, D. (Eds.), Handbook of Econometrics, vol. IV. North-Holland: Amsterdam, pp. 2959-3038.
13. Boswijk, P., Klassen, F., 2004. Why frequency matters for unit root testing. Tinbergen Institute Discussion Paper, University of Amsterdam.
14. Chu C.S.J., Stinchcombe M., and White H. Monitoring structural change. Econometrica 64 (1996) 1045-1065
15. Davidson J. Establishing conditions for the functional central limit theorem in nonlinear and semiparametric time series processes. Journal of Econometrics 106 (2002) 243-269
16. De Jong R.M. Central limit theorems for dependent heterogeneous random variables. Econometric Theory 13 (1997) 353-367
17. De Jong R.M., and Davidson J. The functional central limit theorem and weak convergence to stochastic integrals I: weakly dependent processes. Econometric Theory 16 (2000) 621-642
18. Den Haan, W.J., Levin, A., 1997. A practioner's guide to robust covariance matrix estimation. In: Rao, C.R., Maddala, G.S. (Eds.), Handbook of Statistics, vol. 15. pp. 291-341.
19. Diebold F.X., and Inoue A. Long memory and regime shifts. Journal of Econometrics 105 1 (2001) 131-159
20. Ding Z., Granger C.W.J., and Engle R.F. A long-memory properties of stock market returns and a new model. Journal of Empirical Finance 2 1 (1993) 83-106
21. Drost F.C., and Nijman T. Temporal aggregation of GARCH processes. Econometrica 61 (1993) 727-909
22. Drost F.C., and Werker B.M.J. Closing the GARCH gap: continuous time GARCH modeling. Journal of Econometrics 74 (1996) 31-57
23. Embrechts P., Klüppelberg C., and Mikosch T. Modelling Extremal Events (1997), Springer, New York
24. Engle R.F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (1982) 987-1008
25. Forsberg, L., Ghysels, E., 2004. Why do absolute returns predict volatility so well? Discussion paper, UNC.
26. Ghysels, E., Harvey, A., Renault, E., 1996. Stochastic volatility. In: Maddala, G.S., Rao, C.R. (Eds.), Handbook of Statistics. vol. 14. Elsevier, Amsterdam.
27. Ghysels, E., Santa-Clara, P., Valkanov, R., 2005. Predicting volatility: getting most out of return data sampled at different frequencies. Journal of Econometrics, forthcoming.
28. Granger, C.W.J., 1959. Estimating the probability of flooding on a tidal river. Journal of the Institution of Water Engineers 13, 165-174. Reprinted. In: Ghysels, E., Swanson, N., Watson, M. (Eds.), Essays in Econometrics-Collected Papers of Clive Granger, W.J., vol. I: Spectral Analysis, Seasonality, Nonlinearity, Methodology and Forecasting. Cambridge University Press, Cambridge.
29. Granger, C.W.J., Hyung, N., 1999. Occasional structural breaks and long memory. Discussion Paper 99-14, Department of Economics, University of California, San Diego.
30. Giraitis L., Kokoszka P., and Leipus R. Stationary ARCH models: dependence structure and central limit theorem. Econometric Theory 16 (2000) 3-22
31. Giraitis L., Robinson P., and Surgailis D. A model for long memory conditional heteroskedasticity. The Annals of Applied Probability 10 (2000) 1002-1024
32. He C., and Terasvirta T. Properties of moments of a family of GARCH processes. Journal of Econometrics 2 1 (1999) 173-192
33. He C., and Terasvirta T. Fourth moment structure of the GARCH(p,q) process. Econometric Theory 15 6 (1999) 824-846
34. Hendry D. The econometrics of macroeconomic forecasting. Economic Journal 107 (1997) 1330-1357
35. Horvath L., Kokoszka P., and Teyssière G. Empirical process of the squared residuals of an ARCH sequence. Annals of Statistics 29 (2001) 445-469
36. Kaminsky G.L., and Reinhart A. The twin crises: the causes of banking and balance-of-payments problems. American Economic Review 83 (1999) 473-500
37. Kho B.-C., Dong L., and Rene M.S. US banks, cirses and bailouts: from Mexico to LTCM. American Economic Review 90 (2000) 28-31
38. Kulperger, R., Yu, H., 2005. High moment partial sum processes of residuals in GARCH models and their applications. Annals of Statistics, forthcoming.
39. Kokoszka P., and Leipus R. Testing for parameter changes in ARCH models. Lithuanian Mathematical Journal 39 (1999) 231-247
40. Kokoszka P., and Leipus R. Change-point estimation in ARCH models. Bernoulli 6 3 (2000) 513-539
41. Konaris, G., 2003. Derivative pricing under non-Gaussian stochastic volatility. D. Phil. Thesis, Department of Economics, Oxford University.
42. Krishnan C.N., Ritchken P.N., and Thomson J.B. Monitoring and controlling bank risk: does risk debt help?. Journal of Finance 1 (2005) 343-378
43. Kuan C.M., and Hornik K. The generalized fluctuation test: a unifying view. Econometric Reviews 14 (1995) 135-161
44. Lai T.L. Sequential changepoint detection in quality control and dynamical systems. Journal of the Royal Statistical Society Series B 57 (1995) 613-658
45. Lai T.-Z. Sequential analysis: some classical problems and new challenges. Statistica Sinica 11 2 (2001) 303-408
46. Lamourex C., and Lastrapes W. Persistence in variance, structural-change and the GARCH model. Journal of Business and Economic Statistics 8 (1990) 225-234
47. Leisch F., Hornik K., and Kuan C.M. Monitoring structural changes with the generalized fluctuation test. Econometric Theory 16 (2000) 835-854
48. Meddahi, N., 2003. ARMA representation of integrated and realized variances. Discussion paper CIRANO.
49. Meddahi, N., Renault, E., 2004. Temporal aggregation of volatility models. Journal of Econometrics forthcoming.
50. Mendoza C. Realised variance and realised absolute variation (2004), Department of Statistics, Oxford University
51. Merton R.C. On estimating the expected return on the market: an exploratory investigation. Journal of Financial Economics 8 (1980) 323-361
52. Revesz P. On the increments of Wiener and related processes. Annals of Probability 10 (1982) 613-622
53. Robbins H., and Seigmund D. Boundary crossing probabilities for the Wiener process and partial sums. Annals of Mathematical Statistics 41 (1970) 1410-1429
54. Robinson P.M. Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. Journal of Econometrics 47 (1991) 67-84
55. Segen, J., Sanderson, A., 1980. Detection of changes in a time series. IEEE Transaction in Information Theory IT-26, 249-257.
56. Shephard, N., 2005. Stochastic Volatility: Selected Readings, edited volume. Oxford University Press, forthcoming.
57. Shiller R., and Perron P. Testing the Ramdon walk hypothesis: power versus frequency of observation. Economics Letters 18 (1985) 381-386
58. Stoumbos Z., Reynolds G., Ryan M.R., and Woodall W.H. The state of statistical process control as we proceed into the 21st century. Journal of the American Statistical Association 95 (2000) 992-998
59. Taylor S. Modelling Financial Time Series (1986), Wiley, New York
60. Woerner, J., 2004. Variational sums and power variation: a unifying approach to model selection and estimation in semimartingale models. Statistics and Decision, forthcoming.
61. Zeileis A., Leisch F., Kleiber C., and Hornik K. Monitoring structural change in dynamic econometric models. Journal of Applied Econometrics 20 (2005) 99-121