White, Halbert and Kim, TaeHwan and Manganelli, Simone (2010): VAR for VaR: measuring systemic risk using multivariate regression quantiles.

PDF
MPRA_paper_35372.pdf Download (241kB)  Preview 
Abstract
This paper proposes methods for estimation and inference in multivariate, multiquantile models. The theory can simultaneously accommodate models with multiple random variables, multiple confidence levels, and multiple lags of the associated quantiles. The proposed framework can be conveniently thought of as a vector autoregressive (VAR) extension to quantile models. We estimate a simple version of the model using market returns data to analyse spillovers in the values at risk (VaR) of different financial institutions. We construct impulseresponse functions for the quantile processes of a sample of 230 financial institutions around the world and study how financial institutionspecific and systemwide shocks are absorbed by the system.
Item Type:  MPRA Paper 

Original Title:  VAR for VaR: measuring systemic risk using multivariate regression quantiles. 
Language:  English 
Keywords:  Quantile impulseresponses; spillover; codependence; CAViaR 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General 
Item ID:  35372 
Depositing User:  TaeHwan/T. Kim 
Date Deposited:  13 Dec 2011 03:02 
Last Modified:  28 Sep 2019 18:58 
References:  Acharya, V., Pedersen, L., Philippe, T., and Richardson, M. (2010). Measuring systemic risk. Technical report, Department of Finance, NYU. Adrian, T. and Brunnermeier, M. (2009). CoVaR. Manuscript, Princeton University. Andersen, T. G., Bollerslev, T., Diebold, F. X. and Labys, P. (2003). Modeling and Forecasting Realized Volatility. Econometrica 71: 579625. Andrews, D.W.K. (1988). Laws of large numbers for dependent nonidentically distributed random variables. Econometric Theory 4, 458467. Bartle, R. (1966). The Elements of Integration. New York: Wiley. Engle, R.F. and C.T. Brownlees (2010). Volatility, Correlation and Tails for Systemic Risk Measurement. Manuscript, Stern School of Business, New York University. Engle, R.F. and Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics 22, 367381. Huber, P.J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley: University of California Press, pp. 221233. Kim, T.H. and White, H. (2003). Estimation, inference, and specification testing for possibly misspecified quantile regression. In T. Fomby and C. Hill, eds., Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later. New York: Elsevier, pp. 107132. Koenker, R. and Bassett, G. (1978). Regression quantiles. Econometrica 46, 3350. Komunjer, I. (2005). Quasimaximum likelihood estimation for conditional quantiles. Journal of Econometrics 128, 127164. Komunjer, I. and Vuong, Q. (2006). Efficient conditional quantile estimation: the time series case. University of California, San Diego Department of Economics Discussion Paper 200610. Komunjer, I. and Vuong, Q. (2007a). Semiparametric efficiency bound and Mestimation in timeseries models for conditional quantiles. University of California, San Diego Department of Economics Discussion Paper Komunjer, I. and Vuong, Q. (2007b). Efficient estimation in dynamic conditional quantile models. University of California, San Diego Department of Economics Discussion Paper. Newey, W.K. and Powell, J.L. (1990). Efficient estimation of linear and type I censored regression models under conditional quantile restrictions. Econometric Theory 6, 295317. Otsu, T. (2003). Empirical likelihood for quantile regression. University of Wisconsin, Madison Department of Economics Discussion Paper. Powell, J. (1984). Least absolute deviations estimators for the censored regression model. Journal of Econometrics 25, 303325. Stinchcombe, M. and White, H. (1998). Consistent specification testing with nuisance parameters present only under the alternative. Econometric Theory 14, 295324. Weiss, A. (1991). Estimating nonlinear dynamic models using least absolute error estimation. Econometric Theory 7, 4668. White, H. (1994). Estimation, Inference and Specification Analysis. New York: Cambridge University Press. White, H. (2001). Asymptotic Theory for Econometricians. San Diego: Academic Press. White, H. (2006). Approximate nonlinear forecasting methods. In G. Elliott, C.W.J. Granger, and A. Timmermann, eds., Handbook of Economic Forecasting. New York: Elsevier, pp. 460512. White, H., Kim, T.H., and Manganelli, S. (2008). Modeling autoregressive conditional skewness and kurtosis with multiquantile CAViaR. In J. Russell and M. Watson, eds., Volatility and Time Series Econometrics: A Festschrift in Honor of Robert F. Engle, forthcoming. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/35372 