Francq, Christian and Zakoian, Jean-Michel (2024): Finite moments testing in a general class of nonlinear time series models.
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Abstract
We investigate the problem of testing the finiteness of moments for a class of semi-parametric time series encompassing many commonly used specifications. The existence of positive-power moments of the strictly stationary solution is characterized by the Moment Determining Function (MDF) of the model, which depends on the parameter driving the dynamics and on the distribution of the innovations. We establish the asymptotic distribution of the empirical MDF, from which tests of moments are deduced. Alternative tests based on estimation of the Maximal Moment Exponent (MME) are studied. Power comparisons based on local alternatives and the Bahadur approach are proposed. We provide an illustration on real financial data and show that semi-parametric estimation of the MME provides an interesting alternative to Hill's nonparametric estimator of the tail index.
Item Type: | MPRA Paper |
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Original Title: | Finite moments testing in a general class of nonlinear time series models |
Language: | English |
Keywords: | Efficiency comparisons of tests; maximal moment exponent; stochastic recurrence equation; tail index |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 121193 |
Depositing User: | Pr. Jean-Michel Zakoian |
Date Deposited: | 16 Jun 2024 17:28 |
Last Modified: | 16 Jun 2024 17:28 |
References: | [1] Aue A., Berkes, I. and L. Horváth (2006). Strong approximation for the sums of squares of augmented GARCH sequences. Bernoulli 12 583-608. [2] Baek, C., Pipiras, V., Wendt, H. and P. Abry (2009). Second order properties of distribution tails and estimation of tail exponents in random difference equations. Extremes 12 361-400. [3] Basrak, B., Davis, R.A. and T. Mikosch (2002). Regular variation of GARCH processes. Stochastic Process. Appl. 99 95-116. [4] Berkes, I. and L. Horváth (2004). The efficiency of the estimators of the parameters in GARCH processes. Ann. Statist. 32 633-655. [5] Berkes, I., Horváth, L. and P.S. Kokoszka (2003). Estimation of the maximal moment exponent of a GARCH(1,1) sequence. Econometric Theory 19 565-586. [6] Berkes, I., Horváth, L. and P.S. Kokoszka (2003). GARCH processes: structure and estimation. Bernoulli 9 201-227. [7] Billingsley P. (1968). Convergence of Probability Measures 1st ed. John Wiley, New York. [8] Billingsley, P. (1961). The Lindeberg-Lévy theorem for martingales. Proc. Amer. Math. Soc. 12 788-792. [9] Blasques, F., Francq, C. and S. Laurent (2023). Quasi score-driven models. J. Econometrics 234 251-275. [10] Blasques, F., Gorgi, P., Koopman, S.J. and O. Wintenberger (2018). Feasible invertibility conditions and maximum likelihood estimation for observation-driven models. Electron. J. Stat. 12 1019-1052. [11] Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31 307-327. [12] Bougerol, P. (1993). Kalman fltering with random coefficients and contractions. SIAM J. Control and Optim. 31 942-959. [13] Brandt, A. (1986) The stochastic equation with stationary coefficients. Adv. Appl. Probability 18 211-220. [14] Chan, N.H., Li, D., Peng, L. and R. Zhang (2013). Tail index of an AR(1) model with ARCH(1) errors. Econometric Theory 29 920-940. [15] Creal, D., Koopman, S.J. and A. Lucas (2013). Generalized autoregressive score models with applications. J. Appl. Econometrics 28 777-795. [16] Davis, R. and T. Mikosch (2009). Extreme value theory for GARCH processes. In T. Andersen, R. Davis, J.-P. Kreiss, and T. Mikosch (Eds.), Handbook of Financial Time Series, 187-200. New York: Springer. [17] Davis, R. and S. Resnick (1986) Limit theory for the sample covariance and correlation functions of moving averages. Ann. Statist. 14 533-558. [18] Delaigle, A., Meister, A. and J. Rombouts (2016). Root-T consistent density estimation in GARCH models. J. Econometrics 192 55-63. [19] Ding, Z., Granger, C. W. and R.F. Engle (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance 1, 83-106. [20] Drees, H., Resnick, S. and L. de Haan (2000). How to make a Hill plot. Ann. Statist. 28 254-274. [21] Drost, F.C. and C.A.J. Klaassen (1997). Efficient estimation in semiparametric GARCH models. J. Econometrics 81 193-221. [22] Drost, F.C., Klaassen, C.A.J. and B.J.M. Werker (1997). Adaptive estimation in time-series models. Ann. Statist. 25 786-817. [23] Embrechts, P., Klüppelberg, C. and T. Mikosch (1997). Modelling extremal events: for insurance and finance. Springer, New-York. [24] Engle, R.F. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica 50 987-1007. [25] Francq, C. and J.M. Zakoïan (2004). Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes. Bernoulli 10 605-637. [26] Francq, C. and J.M. Zakoïan (2006). On efficient inference in GARCH processes. In: Bertail P, Doukhan P, Soulier P. (eds) Statistics for dependent data. Springer, New-York: 305-327. [27] Francq, C. and J-M. Zakoïan (2009). A tour in the asymptotic theory of GARCH estimation. In Handb. of Financial Time Series, pp 85-111. Berlin, Heidelberg: Springer. [28] Francq, C. and J-M. Zakoïan (2013a) Inference in nonstationary asymmetric GARCH models. Ann. Statist. 41 1970-1998. [29] Francq, C. and J-M. Zakoïan (2013b) Optimal predictions of powers of conditionally heteroskedastic processes. J. Roy. Statist. Soc. - Series B 75 345-367. [30] Francq, C. and J.M. Zakoïan (2019). GARCH Models: Structure, Statistical Inference and Financial Applications. John Wiley, Second edition. [31] Francq, C. and J-M. Zakoïan (2022). Testing the existence of moments for GARCH processes. J. Econometrics 227 47-64. [32] Francq, C. and J-M. Zakoïan (2023). Local asymptotic normality of general conditionally heteroskedastic and score-driven time-series models. Econometric Theory 39 1067-1092. [33] Glosten, L.R., Jaganathan, R. and D. Runkle (1993). On the relation between the expected values and the volatility of the nominal excess return on stocks. J. Finance 48 1779-1801. [34] Goldie, C.M. (1991). Implicit renewal theory and tails of solutions of random equations. Ann. Appl. Probability 1 126-166. [35] Hamadeh, T.. and J-M. Zakoïan (2011). Asymptotic properties of LS and QML estimators for a class of nonlinear GARCH Processes. J. Statist. Plann. Inference 141 488-507. [36] Harvey, A.(2013) Dynamic Models for Volatility and Heavy Tails. Cambridge University Press. [37] Heinemann, A. (2019). A bootstrap test for the existence of moments for GARCH processes. Preprint arXiv:1902.01808v3. [38] Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Ann. Statist. 3 1163-1174. [39] Hörmann, S. (2008). Augmented GARCH sequences: dependence structure and asymptotics. Bernoulli 14 543-561. [40] Kesten, H. (1973). Random difference equations and renewal theory for products of random matrices. Acta Math. 131 207-248. [41] Lee, S. and M. Taniguchi (2005). Asymptotic theory for ARCH-SM models: LAN and residual empirical processes. Statist. Sinica 15 215-234. [42] Ling, S. and M. McAleer (2002). Stationarity and the existence of moments of a family of GARCH processes. J. Econometrics 106 109-117. [43] Ling, S. and M. McAleer (2003). Adaptative estimation in nonstationary ARMA models with GARCH errors. Ann. Statist. 31 642-674. [44] Mikosch, T. and C. St ric (2000). Limit theory for the sample autocorrelations and extremes of a GARCH(1,1) process. Ann. Statist. 28 1427-1451. [45] Ng, W.L. and C.Y. Yau (2018) Test for the existence of finite moments via bootstrap. Nonparametr. Stat. 30, 28-48. [46] Straumann, D. (2005). Estimation in Conditionally Heteroscedastic Time Series Models. Lecture Notes in Statistics, Springer Berlin Heidelberg. [47] Straumann, D. and T. Mikosch (2006). Quasi-maximum likelihood estimation in conditionally heteroscedastic time series: a stochastic recurrence equations approach. Ann. Statist. 34 2449-2495. [48] Taylor, S. J. (1994) Modeling stochastic volatility: A review and comparative study. Math. Finance 4, 183-204. [49] Trapani, L. (2016). Testing for (in)finite moments. J. Econometrics 191 57-68. [50] van der Vaart, A.W. (1998). Asymptotic statistics. Cambridge University Press, United Kingdom. [51] Zakoïan, J-M. (1994). Threshold heteroskedastic models. J. Econom. Dynam. Control 18,931-955. [52] Zhang, R., Li, C. and L. Peng (2019). Inference for the tail index of a GARCH(1,1) model and an AR(1) model with ARCH(1) errors. Econometric Rev. 38 151-169. [53] Zhang, R. and S. Ling (2015). Asymptotic inference for AR models with heavy-tailed G-GARCH noises. Econometric Theory 31 880-890. [54] Zhu, K. and S. Ling (2011). Global self-weighted and local quasi-maximum exponential likelihood estimators for ARMA-GARCH/IGARCH models. Ann. Statist. 39 2131-2163. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/121193 |