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Enrique Sentana (1995)
Quadratic Arch ModelsThe Review of Economic Studies, 62
A. Hibbert, R. Daigler, Brice Dupoyet (2008)
A behavioral explanation for the negative asymmetric return–volatility relationJournal of Banking and Finance, 32
Journal of Econometrics, 169
the 1st observation, add a new observation at the end of the subsample and re-estimate the model and test again for leverage
S. Laurent, C. Lecourt, F. Palm (2016)
Testing for jumps in conditionally Gaussian ARMA-GARCH models, a robust approachComput. Stat. Data Anal., 100
R. Cumby, J. Huizinga (1990)
Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables RegressionsNBER Working Paper Series
D. Straumann, T. Mikosch (2006)
Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approachAnnals of Statistics, 34
P. Manimaran, J. Parikh, P. Panigrahi, S. Basu, C. Kishtawal, M. Porecha (2006)
Modelling Financial Time Series
L. Kristoufek (2014)
Leverage effect in energy futuresEnergy Economics, 45
Mark Jensen, J. Maheu (2011)
FEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES Estimating a Semiparametric Asymmetric Stochastic Volatility Model with a Dirichlet Process Mixture
Zhuanxin Ding, C. Granger, R. Engle (1993)
A long memory property of stock market returns and a new model
G. Schwert (1988)
Why Does Stock Market Volatility Change Over Time?NBER Working Paper Series
E. Ruiz, Mª Rodríguez (2009)
GARCH models with leverage effect : differences and similarities
K. Zhu, S. Ling (2015)
LADE-Based Inference for ARMA Models With Unspecified and Heavy-Tailed Heteroscedastic NoisesJournal of the American Statistical Association, 110
Jianqing Fan, Lei Qi, D. Xiu (2014)
Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed LikelihoodsJournal of Business & Economic Statistics, 32
Yacine Ait-Sahalia, Jianqing Fan, Yingying Li (2011)
The Leverage Effect Puzzle: Disentangling Sources of Bias at High FrequencyNBER Working Paper Series
(1976)
Studies in stock price volatility changes, Proceedings of the 1976 Business Meeting of the Business and Economics Statistics Sections
F. Diebold (1988)
Empirical modeling of exchange rate dynamics
C. Hafner, O. Linton (2015)
AN ALMOST CLOSED FORM ESTIMATOR FOR THE EGARCH MODELEconometric Theory, 33
Ludger Hentschel (1995)
All in the family Nesting symmetric and asymmetric GARCH modelsJournal of Financial Economics, 39
M. Carnero, Ana Pérez, E. Ruiz (2016)
Identification of asymmetric conditional heteroscedasticity in the presence of outliersSERIEs, 7
Beatriz Mendes (2000)
Assessing the bias of maximum likelihood estimates of contaminated garch modelsJournal of Statistical Computation and Simulation, 67
Daniel Nelson (1991)
CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACHEconometrica, 59
Da Huang, Hansheng Wang, Q. Yao (2008)
Estimating GARCH Models: When to Use What?Capital Markets: Asset Pricing & Valuation
Changli He, Annastiina Silvennoinen, Timo Terasvirta (2007)
Parameterizing Unconditional Skewness in Models for Financial Time SeriesInternational Finance eJournal
T. Teräsvirta, Zhenfang Zhao (2011)
Stylized facts of return series, robust estimates and three popular models of volatilityApplied Financial Economics, 21
C. Francq, J. Zakoian (2010)
GARCH Models: Structure, Statistical Inference and Financial Applications
C. Francq, J. Zakoïan (2013)
Optimal predictions of powers of conditionally heteroscedastic processesJournal of the Royal Statistical Society: Series B (Statistical Methodology), 75
M. Carnero, D. Peña, E. Ruiz (2012)
Estimating GARCH volatility in the presence of outliersEconomics Letters, 114
R. Engle (2011)
Long Term Skewness and Systemic RiskJournal of Financial Econometrics, 9
S. Sakata, H. White (1998)
HIGH BREAKDOWN POINT CONDITIONAL DISPERSION ESTIMATION WITH APPLICATION TO S&P 500 DAILY RETURNS VOLATILITYEconometrica, 66
(1976)
“ StudiesinstockPriceVolatilityChanges. ”
O. Wintenberger (2012)
Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) ModelScandinavian Journal of Statistics, 40
L. Peng, Q. Yao (2003)
Least absolute deviations estimation for ARCH and GARCH modelsBiometrika, 90
T. Andersen, T. Bollerslev, F. Diebold, Heiko Ebens (2001)
The distribution of realized stock return volatilityJournal of Financial Economics, 61
Jiazhu Pan, Hui Wang, H. Tong (2007)
Estimation and tests for power-transformed and threshold GARCH models☆Journal of Econometrics, 142
J. Zakoian (1994)
Threshold heteroskedastic modelsJournal of Economic Dynamics and Control, 18
Walid Chkili, S. Hammoudeh, D. Nguyen (2014)
Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memoryEnergy Economics, 41
A. Harvey (2013)
Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series
D. Straumann, T. Mikosch
QUASI – MAXIMUM – LIKELIHOOD ESTIMATION IN HETEROSCEDASTIC TIME SERIES : A STOCHASTIC RECURRENCE EQUATIONS APPROACH
M. Rodríguez, E. Ruiz (2012)
Revisiting Several Popular GARCH Models with Leverage Effect: Differences and SimilaritiesJournal of Financial Econometrics, 10
L. Glosten, R. Jagannathan, D. Runkle (1993)
On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on StocksJournal of Finance, 48
E. Zivot (2009)
Practical Issues in the Analysis of Univariate GARCH Models
Jonathan Hill (2014)
Robust Estimation and Inference for Heavy Tailed GARCHEconometrics: Multiple Equation Models eJournal
Jun Yu (2012)
A Semiparametric Stochastic Volatility ModelJournal of Econometrics, 167
Whitney Newey, D. Steigerwald (1997)
Asymptotic Bias for Quasi-Maximum-Likelihood Estimators in Conditional Heteroskedasticity ModelsEconometrica, 65
F. Bandi, R. Renò (2010)
Time-Varying Leverage EffectsERN: Econometric Modeling in Financial Economics (Topic)
N. Muler, V. Yohai (2008)
Robust estimates for GARCH modelsJournal of Statistical Planning and Inference, 138
S. Hwang, I. Basawa (2004)
Stationarity and moment structure for Box-Cox transformed threshold GARCH(1,1) processesStatistics & Probability Letters, 68
online version of this article offers Supplementary Material
I. Berkes, Lajos Horv'ath (2004)
The efficiency of the estimators of the parameters in GARCH processesAnnals of Statistics, 32
Drew Creal, S. Koopman, A. Lucas (2011)
Generalized Autoregressive Score Models with Applications ∗
Changli He, T. Teräsvirta (1999)
Properties of Moments of a Family of GARCH ProcessesJournal of Econometrics, 92
M. Carnero, D. Peña, E. Ruiz (2007)
Effects of outliers on the identification and estimation of GARCH modelsJournal of Time Series Analysis, 28
AbstractThis paper illustrates how outliers can affect both the estimation and testing of leverage effect by focusing on the TGARCH model. Three estimation methods are compared through Monte Carlo experiments: Gaussian Quasi-Maximum Likelihood, Quasi-Maximum Likelihood based on the Student-t likelihood and Least Absolute Deviation method. The empirical behavior of the t-ratio and the Likelihood Ratio tests for the significance of the leverage parameter is also analyzed. Our results put forward the unreliability of Gaussian Quasi-Maximum Likelihood methods in the presence of outliers. In particular, we show that one isolated outlier could hide true leverage effect whereas two consecutive outliers bias the estimated leverage coefficient in a direction that crucially depends on the sign of the first outlier and could lead to wrongly reject the null of no leverage effect or to estimate asymmetries of the wrong sign. By contrast, we highlight the good performance of the robust estimators in the presence of one isolated outlier. However, when there are patches of outliers, our findings suggest that the sizes and powers of the tests as well as the estimated parameters based on robust methods may still be distorted in some cases. We illustrate these results with two series of daily returns.
Studies in Nonlinear Dynamics & Econometrics – de Gruyter
Published: Mar 1, 2021
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