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Articles

Modeling Extreme Events: Time-Varying Extreme Tail Shape

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References

  • Blasques, F., Koopman, S. J., and Lucas, A. (2015), “Information Theoretic Optimality of Observation Driven Time Series Models for Continuous Responses,” Biometrika, 102, 325–343. DOI: 10.1093/biomet/asu076.
  • Blasques, F., van Brummelen, J., Koopman, S. J., and Lucas, A. (2022), “Maximum Likelihood Estimation for Generalized Autoregressive Score Models,” Journal of Econometrics, 227, 325–346. DOI: 10.1016/j.jeconom.2021.06.003.
  • Boswijk, H. P., Cavaliere, G., Georgiev, I., and Rahbek, A. (2021), “Bootstrapping Non-stationary Stochastic Volatility,” Journal of Econometrics, 224, 161–180. DOI: 10.1016/j.jeconom.2021.01.005.
  • Bougerol, P. (1993), “Kalman Filtering with Random Coefficients and Contractions,” SIAM Journal on Control and Optimization, 31, 942–959. DOI: 10.1137/0331041.
  • Castro-Camilo, D., de Carvalho, M., and Wadsworth, J. (2018), “Time-Varying Extreme Value Dependence with Application to Leading European Stock Markets,” Annals of Applied Statistics, 12, 283–309.
  • Catania, L., and Luati, A. (in press), “Semiparametric Modeling of Multiple Quantiles,” Journal of Econometrics.
  • Chavez-Demoulin, V., Embrechts, P., and Sardy, S. (2014), “Extreme-Quantile Tracking for Financial Time Series,” Journal of Econometrics, 181, 44–52. DOI: 10.1016/j.jeconom.2014.02.007.
  • Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, London: Springer Press.
  • Cox, D. R. (1981), “Statistical Analysis of Time Series: Some Recent Developments,” Scandinavian Journal of Statistics, 8, 93–115.
  • Creal, D., Koopman, S. J., and Lucas, A. (2013), “Generalized Autoregressive Score Models with Applications,” Journal of Applied Econometrics, 28, 777–795. DOI: 10.1002/jae.1279.
  • Creal, D., Schwaab, B., Koopman, S. J., and Lucas, A. (2014), “An Observation Driven Mixed Measurement Dynamic Factor Model with Application to Credit Risk,” The Review of Economics and Statistics, 96, 898915. DOI: 10.1162/REST_a_00393.
  • Davidson, A. C., and Smith, R. L. (1990), “Models for Exceedances over High Thresholds,” Journal of the Royal Statistical Association, Series B, 52, 393–442. DOI: 10.1111/j.2517-6161.1990.tb01796.x.
  • de Haan, L., and Zhou, C. (2021), “Trends in Extreme Value Indices,” Journal of the Americal Statistical Association, 116, 1265–1279. DOI: 10.1080/01621459.2019.1705307.
  • Einmahl, J., de Haan, L., and Zhou, C. (2016), “Statistics of Heteroscedastic Extremes,” Journal of the Royal Statistical Society, Series B, 78, 31–51. DOI: 10.1111/rssb.12099.
  • Embrechts, P., Klüppelberg, C., and Mikosch, T. (1997), Modelling Extremal Events for Insurance and Finance, Berlin: Springer Verlag.
  • Engle, R. F., and Manganelli, S. (2004), “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,” Journal of Business & Economic Statistics, 22, 367–381. DOI: 10.1198/073500104000000370.
  • Escobar-Bach, M., Goegebeur, Y., and Guillou, A. (2018), “Local Robust Estimation of the Pickands Dependence Function,” Annals of Statistics, 46, 2806–2843.
  • Eser, F., and Schwaab, B. (2016), “Evaluating the Impact of Unconventional Monetary Policy Measures: Empirical Evidence from the ECB’s Securities Markets Programme,” Journal of Financial Economics, 119, 147–167. DOI: 10.1016/j.jfineco.2015.06.003.
  • Galbraith, J. W., and Zernov, S. (2004), “Circuit Breakers and the Tail Index of Equity Returns,” Journal of Financial Econometrics, 2, 109–129. DOI: 10.1093/jjfinec/nbh005.
  • Ghysels, E., Idier, J., Manganelli, S., and Vergote, O. (2017), “A High Frequency Assessment of the ECB Securities Markets Programme,” Journal of European Economic Association, 15, 218–243. DOI: 10.1093/jeea/jvw003.
  • Hafner, C. M., and Wang, L. (in press), “A Dynamic Conditional Score Model for the Log Correlation Matrix,” Journal of Econometrics.
  • Harvey, A. C. (2013), Dynamic Models for Volatility and Heavy Tails with Applications to Financial and Economic Time Series, Cambridge: Cambridge University Press.
  • Hautsch, N., and Herrera, R. (2020), “Multivariate Dynamic Intensity Peaks-Over-Threshold Models,” Journal of Applied Econometrics, 35, 248–272. DOI: 10.1002/jae.2741.
  • Hetland, S., Pedersen, R. S., and Rahbek, A. (in press), “Dynamic Conditional Eigenvalue GARCH,” Journal of Econometrics.
  • Hill, B. (1975), “A Simple General Approach to Inference About the Tail of a Distribution,” The Annals of Statistics, 3, 1163–1174. DOI: 10.1214/aos/1176343247.
  • Hoga, Y. (2017), “Testing for Changes in (Extreme) VaR,” Econometrics Journal, 20, 23–51. DOI: 10.1111/ectj.12080.
  • Huisman, R., Koedijk, K., Kool, C., and Palm, F. (2001), “Tail-Index Estimates in Small Samples,” Journal of Business & Economic Statistics, 19, 208–216. DOI: 10.1198/073500101316970421.
  • Jensen, S. T., and Rahbek, A. (2004), “Asymptotic Inference for Nonstationary GARCH,” Econometric Theory, 20, 1203–1226. DOI: 10.1017/S0266466604206065.
  • Johnson, N. L., Kotz, S., and Balakrishnan, N. (1994), Continuous Univariate Distributions (Vol. 1, 2nd ed.), New York: Wiley.
  • Koenker, R. (2005), Quantile Regression, Cambridge: Cambridge University Press.
  • Lin, C.-H., and Kao, T.-C. (2018), “Multiple Structural Changes in the Tail Behavior: Evidence from Stock Market Futures Returns,” Nonlinear Analysis: Real World Applications, 9, 1702–1713. DOI: 10.1016/j.nonrwa.2007.05.011.
  • Lucas, A., Schwaab, B., and Zhang, X. (2014), “Conditional Euro Area Sovereign Default Risk,” Journal of Business and Economics Statistics, 32, 271–284. DOI: 10.1080/07350015.2013.873540.
  • Lucas, A., and Zhang, X. (2016), “Score Driven Exponentially Weighted Moving Average and Value-at-Risk Forecasting,” International Journal of Forecasting, 32, 293–302. DOI: 10.1016/j.ijforecast.2015.09.003.
  • Massacci, D. (2017), “Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness,” Management Science, 63, 112–132. DOI: 10.1287/mnsc.2016.2488.
  • McNeil, A., and Frey, R. (2000), “Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach,” Journal of Empirical Finance, 7, 271–300. DOI: 10.1016/S0927-5398(00)00012-8.
  • McNeil, A. J., Frey, R., and Embrechts, P. (2010), Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton, NJ: Princeton University press.
  • Mhalla, L., de Carvalho, M., and Chavez-Demoulin, V. (2019), “Regression-Type Models for Extremal Dependence,” Scandinavian Journal of Statistics, 46, 1141–1167. DOI: 10.1111/sjos.12388.
  • Oh, D. H., and Patton, A. J. (2018), “Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads,” Journal of Business & Economic Statistics, 36, 181–195. DOI: 10.1080/07350015.2016.1177535.
  • Patton, A. (2006), “Modelling Asymmetric Exchange Rate Dependence,” International Economic Review, 47, 527–556. DOI: 10.1111/j.1468-2354.2006.00387.x.
  • Patton, A. J., Ziegel, J. F., and Chen, R. (2019), “Dynamic Semiparametric Models for Expected Shortfall (and Value-at-Risk),” Journal of Econometrics, 211, 388–413. DOI: 10.1016/j.jeconom.2018.10.008.
  • Quintos, C., Fan, Z., and Phillips, P. C. (2001), “Structural Change Tests in Tail Behaviour and the Asian Crisis,” The Review of Economic Studies, 68, 633–663. DOI: 10.1111/1467-937X.00184.
  • Rocco, M. (2014), “Extreme Value Theory in Finance: A Survey,” Journal of Economic Surveys, 28, 82–108. DOI: 10.1111/j.1467-6419.2012.00744.x.
  • Straumann, D., and Mikosch, T. (2006), “Quasi-Maximum-Likelihood Estimation in Conditionally Heteroscedastic Time Series: A Stochastic Recurrence Equations Approach,” The Annals of Statistics, 34, 2449–2495. DOI: 10.1214/009053606000000803.
  • Wang, H., and Tsai, C.-L. (2009), “Tail Index Regression,” Journal of the American Statistical Association, 104, 1233–1240. DOI: 10.1198/jasa.2009.tm08458.
  • Werner, T., and Upper, C. (2004), “Time Variation in the Tail Behavior of Bund Future Returns,” Journal of Futures Markets, 24, 387–398. DOI: 10.1002/fut.10120.