Tail Risk Dynamics in Stock Returns: Links to the Macroeconomy and Global Markets Connectedness

Published Online:https://doi.org/10.1287/mnsc.2016.2488

References

  • Akgiray V, Booth GG (1988) The stable-law model of stock returns. J. Bus. Econom. Statist. 6(1):51–57.Google Scholar
  • Allen L, Bali TG, Tang Y (2012) Does systemic risk in the financial sector predict future economic downturns? Rev. Financial Stud. 25(10):3000–3036.CrossrefGoogle Scholar
  • Andersen TG, Bollerslev T, Christoffersen PF, Diebold FX (2013) Financial risk measurement for financial risk management. Constantinedes G, Harris M, Stulz R, eds. Handbook of the Economics of Finance, Vol. 2, Part B (Elsevier, Amsterdam), 1127–1220.CrossrefGoogle Scholar
  • Andersen TG, Bollerslev T, Diebold FX, Labys P (2003) Modeling and forecasting realized volatility. Econometrica 71(2):579–625.CrossrefGoogle Scholar
  • Balkema AA, De Haan L (1974) Residual life time at great age. Ann. Probab. 2(5):792–804.CrossrefGoogle Scholar
  • Billio M, Getmansky M, Lo AW, Pelizzon L (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors. J. Financial Econom. 104(3):535–559.CrossrefGoogle Scholar
  • Blasques F, Koopman SJ, Lucas A (2015) Information-theoretic optimality of observation-driven time series models for continuous responses. Biometrika 102(2):325–343.CrossrefGoogle Scholar
  • Bloom N (2009) The impact of uncertainty shocks. Econometrica 77(3):623–685.CrossrefGoogle Scholar
  • Bloom N (2014) Fluctuations in uncertainty. J. Econom. Perspectives 28(2):153–176.CrossrefGoogle Scholar
  • Bloom N, Floetotto M, Jaimovich N, Saporta-Eksten I, Terry SJ (2014) Really uncertain business cycles. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Bollerslev T (1986) Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31(3):307–327.CrossrefGoogle Scholar
  • Bollerslev T (1987) A conditionally heteroskedastic time series model for speculative prices and rates of return. Rev. Econom. Statist. 69(3):542–547.CrossrefGoogle Scholar
  • Carvalho V, Gabaix X (2013) The great diversification and its undoing. Amer. Econom. Rev. 105(5):1697–1727.CrossrefGoogle Scholar
  • Carvalho V, Grassi B (2015) Large firm dynamics and the business cycle. Working paper, University of Cambridge, Cambridge, UK.Google Scholar
  • Chavez-Demoulin V, Davison AC (2012) Modelling time series extremes. RevStat Statist. J. 10(1):109–133.Google Scholar
  • Chavez-Demoulin V, Embrechts P (2004) Smooth extremal models in finance and insurance. J. Risk Insurance 71(2):183–199.CrossrefGoogle Scholar
  • Chavez-Demoulin V, Davison AC, McNeil AJ (2005) Estimating value-at-risk: A point process approach. Quant. Finance 5(2):227–234.CrossrefGoogle Scholar
  • Chavez-Demoulin V, Embrechts P, Sardy S (2014) Extreme-quantile tracking for financial time series. J. Econometrics 188(1):44–52.CrossrefGoogle Scholar
  • Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev. 51(4):661–703.CrossrefGoogle Scholar
  • Cox DR (1981) Statistical analysis of time series: Some recent developments. Scandinavian J. Statist. 8(2):93–115.Google Scholar
  • Creal D, Koopman SJ, Lucas A (2012) Generalized autoregressive score models with applications. J. Appl. Econometrics 28(5):777–795.CrossrefGoogle Scholar
  • Davison AC, Smith RL (1990) Models for exceedances over high thresholds. J. Roy. Statist. Soc. Ser. B Methodological 52(3):393–442.Google Scholar
  • Diebold FX, Yilmaz K (2009) Measuring financial asset return and volatility spillovers, with application to global equity markets. Econom. J. 119(534):158–171.Google Scholar
  • Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: Measuring the connectedness of financial firms. J. Econometrics 182(1):119–134.CrossrefGoogle Scholar
  • Diebold FX, Schuermann T, Stroughair JD (1998) Pitfalls and opportunities in the use of extreme value theory in risk management. Refenes APN, Burgess AN, Moody JD, eds. Decision Technologies for Computational Finance (Kluwer Academic Publishers, Amsterdam), 3–12.CrossrefGoogle Scholar
  • Doornik JA (2012) Ox 7: An Object-Orientated Matrix Programming Language (Timberlake Consultants Press, London).Google Scholar
  • Embrechts P, Klüppelberg C, Mikosch T (1997) Modelling Extremal Events for Insurance and Finance (Springer-Verlag, Berlin).CrossrefGoogle Scholar
  • Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50(4):987–1007.CrossrefGoogle Scholar
  • Engle RF, Manganelli S (2004) CAViaR: Conditional autoregressive value at risk by regression quantiles. J. Bus. Econom. Statist. 22(4):367–381.CrossrefGoogle Scholar
  • Engle RF, Russell JR (1998) Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica 66(5):1127–1162.CrossrefGoogle Scholar
  • Engle RF, Ghysels E, Sohn B (2013) Stock market volatility and macroeconomic fundamentals. Rev. Econom. Statist. 95(3):776–797.CrossrefGoogle Scholar
  • Gabaix X (2011) The granular origins of aggregate fluctuations. Econometrica 79(3):733–772.CrossrefGoogle Scholar
  • Gabaix X (2012) Variable rare disasters: An exactly solved framework for ten puzzles in macro-finance. Quart. J. Econom. 127(2):645–700.CrossrefGoogle Scholar
  • Gabaix X, Gopikrishnan P, Plerou V, Stanley HE (2003) A theory of power-law distributions in financial market fluctuations. Nature 423:267–270.CrossrefGoogle Scholar
  • Galbraith JW, Zernov S (2004) Circuit breakers and the tail index of equity returns. J. Financial Econometrics 2(1):109–129.CrossrefGoogle Scholar
  • Giglio S, Kelly B, Pruitt S (2016) Systemic risk and the macro-economy: An empirical evaluation. J. Financial Econom. 119(3):457–471.CrossrefGoogle Scholar
  • Gourio F, Siemer M, Verdelhan A (2013) International risk cycles. J. Internat. Econom. 89(2):471–484.CrossrefGoogle Scholar
  • Hahn J, Kuersteiner G (2010) Stationarity and mixing properties of the dynamic Tobit model. Econom. Lett. 107(2):105–111.CrossrefGoogle Scholar
  • Hansen BE (1994) Autoregressive conditional density estimation. Internat. Econom. Rev. 35(3):705–730.CrossrefGoogle Scholar
  • Harvey AC (1989) Forecasting, Structural Time Series Models and the Kalman Filter (Cambridge University Press, Cambridge, UK).Google Scholar
  • Harvey AC (2013) Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Hols MCAB, de Vries CG (1991) The limiting distribution of extremal exchange rate returns. J. Appl. Econometrics 6(3):287–302.CrossrefGoogle Scholar
  • Jansen DW, de Vries CG (1991) On the frequency of large stock returns: Putting boom and busts into perspective. Rev. Econom. Statist. 73(1):18–24.CrossrefGoogle Scholar
  • Jurado K, Ludvigson SC, Ng S (2015) Measuring uncertainty. Amer. Econom. Rev. 105(3):1177–1216.CrossrefGoogle Scholar
  • Kearns P, Pagan A (1997) Estimating the density tail index for financial time series. Rev. Econom. Statist. 79(2):171–175.CrossrefGoogle Scholar
  • Kelly B (2014) The dynamic power law model. Extremes 17(4):557–583.CrossrefGoogle Scholar
  • Kelly B, Jiang H (2014) Tail risk and asset prices. Rev. Financial Stud. 27(10):2841–2871.CrossrefGoogle Scholar
  • Kim T, White H (2004) On more robust estimation of skewness and kurtosis. Finance Res. Lett. 1(1):56–73.CrossrefGoogle Scholar
  • Koedijk KG, Schafgans MMA, de Vries CG (1990) The tail index of exchange rate returns. J. Internat. Econom. 29(1–2):93–108.CrossrefGoogle Scholar
  • Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46(1):33–50.CrossrefGoogle Scholar
  • Kritzman M, Li Y, Page S, Rigobon R (2011) Principal components as a measure of systemic risk. J. Portfolio Management 37(4):112–126.CrossrefGoogle Scholar
  • Ledford AW, Tawn JA (1996) Statistics for near independence in multivariate extreme values. Biometrika 83(1):169–187.CrossrefGoogle Scholar
  • Longin F, Solnik B (2001) Extreme correlation of international equity markets. J. Finance 56(2):649–676.CrossrefGoogle Scholar
  • McNeil AJ, Frey R (2000) Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach. J. Empirical Finance 7(3–4):271–300.CrossrefGoogle Scholar
  • Mele A (2007) Asymmetric stock market volatility and the cyclical behavior of expected returns. J. Financial Econom. 86(2):446–478.CrossrefGoogle Scholar
  • Picklands J (1975) Statistical inference using extreme order statistics. Ann. Statist. 3(1):119–131.CrossrefGoogle Scholar
  • Poon S, Rockinger M, Tawn J (2004) Extreme value dependence in financial markets. Rev. Financial Stud. 17(2):581–610.CrossrefGoogle Scholar
  • Quintos C, Fan Z, Phillips PCB (2001) Structural change tests in tail behaviour and the Asian crisis. Rev. Econom. Stud. 68(3):633–663.CrossrefGoogle Scholar
  • Scarrot C, MacDonald A (2012) A review of extreme value threshold estimation and uncertainty quantification. RevStat Statist. J. 10(1):33–60.Google Scholar
  • Schwert GW (1989) Why does stock market volatility change over time? J. Finance 44(5):1115–1153.CrossrefGoogle Scholar
  • Shephard N (2005) Stochastic Volatility: Selected Readings (Oxford University Press, Oxford, UK).Google Scholar
  • Sims CA, Stock JH, Watson MW (1990) Inference in linear time series models with some unit roots. Econometrica 58(1):113–144.CrossrefGoogle Scholar
  • Smith RL (1985) Maximum likelihood estimation in a class of nonregular cases. Biometrika 72(1):67–90.CrossrefGoogle Scholar
  • Stock JH, Watson MW (2014) Estimating turning points using large data sets. J. Econometrics 178(2):368–381.CrossrefGoogle Scholar
  • Wagner N (2005) Autoregressive conditional tail behavior and results on government bond yield spreads. Internat. Rev. Financial Anal. 14(2):247–261.CrossrefGoogle Scholar
  • Werner T, Upper C (2004) Time variation in the behaviour of Bund futures returns. J. Futures Markets 24(4):387–398.CrossrefGoogle Scholar
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