본문 바로가기
KERD
논문KCI 등재

Quantile Dependence between Stock Markets and Its Application in Volatility Forecasting

Heejoon Han
한국계량경제학회|2019.03|계량경제학보 (Journal of Economic Theory and Econometrics)|제30권 제1호|pp. 96-142|DOI 10.22812/jetem.2019.30.1.004

2

조회

0

다운로드

0

인용

0

참고문헌

키워드

SpilloverQuantile dependenceCross-quantilogramVolatility Forecasting
주제분류경제학

초록

This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we adopt the cross-quantilogram, a correlation statistic of quantile hit processes, and analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. Second, we consider a simple quantile-augmented volatility model that accommodates the quantile dependence and directional predictability from the US market to these other markets. The quantile-augmented volatility model provides superior in-sample and out-of-sample volatility forecasts. Finally, we set up a generalized quantile-based approach to improve volatility forecasting for a wide class of asset portfolios.

Abstract

This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we adopt the cross-quantilogram, a correlation statistic of quantile hit processes, and analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. Second, we consider a simple quantile-augmented volatility model that accommodates the quantile dependence and directional predictability from the US market to these other markets. The quantile-augmented volatility model provides superior in-sample and out-of-sample volatility forecasts. Finally, we set up a generalized quantile-based approach to improve volatility forecasting for a wide class of asset portfolios.

참고문헌 (45)

  1. Ang, A., "Asymmetric correlations of equity portfolios", Journal of Financial Economics, 63, pp.443~494 (2002)
  2. Barndorff-Nielsen, O. E., "Realized kernels in practice: trades and quotes", Econometrics Journal, 12, pp.C1~C32 (2009)
  3. Baele, L., "Volatility spillover effects in European equity markets", Journal of Financial and Quantitative Analysis, 40, pp.373~401 (2005)
  4. Baum¨ohl, "Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis", Finance Research Letters (2017)
  5. Brockwell, P., "Time Series: Theory and Methods" (1991), Springer
  6. Barun´ık, J, "Quantile cross-spectral measures of dependence between economic variables (arXiv:1510.06946v1)", ArXiv e-prints, ~ (2015)
  7. Cappiello, L., "Measuring comovements by regression quantiles", Journal of Financial Econometrics, 12, pp.645~678 (2014)
  8. Das, S., "Systemic risk and international portfolio choice", Journal of Finance, 59, pp.2809~2834 (2004)
  9. Davis, R.A., "The extremogram: a correlogram for extreme events", Bernoulli, 15, pp.977~1009 (2009)
  10. Davis, R.A., "Measures of serial extremal dependence and their estimation", Stochastic Processes and their Applications, 123, pp.2575~2602 (2013)
  11. Demarta, S., "The t copula and related copulas", International Statistical Review, 73, pp.111~129 (2005)
  12. Diebold, F. X., "Comparing predictive accuracy", Journal of Business & Economic Statistics, 13, pp.253~263 (1995)
  13. Dungey, M., "Empirical modelling of contagion: A review of methodologies", Quantitiative Finance, 5, pp.9~24 (2005)
  14. Forbes, K.J., "No contagion, only interdependence: Measuring stock market comovement", Journal of Finance, 57, pp.2223~2261 (2002)
  15. Garcia, R., "Dependence structure and extreme comovements in international equity and bond markets", Journal of Banking and Finance, 35, pp.1954~1970 (2011)
  16. Han, H., "Asymptotic theory for the QMLE in GARCH-X models with stationary and non-stationary covariates", Journal of Business &Economic Statistics, 32, pp.416~429 (2014)
  17. Han, H., "The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series", Journal of Econometrics, 193, pp.251~270 (2016)
  18. Hansen, P. R., "Consistent ranking of volatility models", Journal of Econometrics, 131, pp.97~121 (2006)
  19. Hansen, P.R., "Realized GARCH: a joint model for returns and realized measures of volatility", Journal of Applied Econometrics, 27, pp.877~906 (2012)
  20. Heber, G., "OMI’ realised library, Version 0. 1. Oxford-Man Institute", University of Oxford, ~ (2009)
  21. Jiang, H., "Spillovers and directional predictability with a cross-quantilogram analysis: The case of U.S. and Chinese agricultural futures", Journal of Futures Markets, 36, pp.1231~1255 (2016)
  22. Joe, H., "Multivariate Models and Dependence Concepts" (1997), Chapman and Hall
  23. Karolyi, G. A., "A multivariate GARCH models of international transmission of stock returns and volatility: the case of United States and Canada", Journal of Business & Economic Statistics, 13, pp.11~25 (1995)
  24. King, M., "Volatility and links between national stock markets", Econometrica, 62, pp.901~933 (1994)
  25. Lee, T.-H., "Copula-based multivariate GARCH model with uncorrelated dependent errors", Journal of Econometrics, 150, pp.207~218 (2009)
  26. Li, G., "Quantile correlations and quantile autoregressive modeling", Journal of the American Statistical Association, 110(509), pp.246~261 (2015)
  27. Linton, O., "The quantilogram: With an application to evaluating directional predictability", Journal of Econometrics, 141, pp.250~282 (2007)
  28. Login, F., "Extreme correlations in international equity markets", Journal of Finance, 56, pp.649~676 (2001)
  29. Nelsen, R.B, "An Introductioin to Copulas" (2006), Springer
  30. Patton, A.J., "Volatility forecast comparison using imperfect volatility proxies", Journal of Econometrics, 160, pp.246~256 (2010)
  31. Patton, A. J., "Handbook of Economic Forecasting, Volume 2" (2013), Springer
  32. Patton, A. J., "Correction to ”utomatic block-length selection for dependent bootstrap”by D. Politis and H. White", Econometric Reviews, 28, pp.372~375 (2009)
  33. Patton, A.J., "Handbook of Financial Time Series", pp.801~838 (2009), Springer
  34. Politis, D.N., "The stationary bootstrap", Journal of the American Statistical Association, 89, pp.1303~1313 (1994)
  35. Politis, D.N, "Automatic block-length selection for dependent bootstrap", Econometric Reviews, 23, pp.53~70 (2004)
  36. Poon, S, "Extreme value dependence in financial markets: diagnostics, models, and financial implications", Review of Financial Studies, 17, pp.581~610 (2004)
  37. Reboredo, J., "Quantile dependence of oil price movements and stock returns", Energy Economics, 54, pp.33~49 (2016)
  38. Rodriguez, J. C., "Measuring financial contagion: A Copula approach", Journal of Empirical Finance, 14, pp.401~423 (2007)
  39. Schmitt, T.A., "Quantile correlation: Uncovering temporal dependencies in financial time series", Journal of Theoretical and Applied Finance (2015)
  40. Shahzad, S. J. H., "Directional predictability from oil market uncertainty to sovereign credit spreads of oilexporting countries: Evidence from rolling windows and cross-quantilogram analysis", Energy Economics (2017)
  41. Sim, N., "Oil prices, US stock return, and the dependence between their quantiles", Journal of Banking & Finance, 55, pp.1~8 (2015)
  42. Shephard, N., "Realising the future: Forecasting with high frequency based volatility (HEAVY) models", Journal of Applied Econometrics, 25, pp.197~231 (2010)
  43. Todorova, N., "The intraday directional predictability of large Australian stocks:A cross-quantilogram analysis", Economic Modelling, 64, pp.221~230 (2017)
  44. West, K.D., "Asymptotic inference about predictive ability", Econometrica, 64, pp.1067~1084 (1996)
  45. Yifan, S., "International risk transmission of stock market movements", Economic Modelling (2017)

관련 논문AI추천

Quantile Dependence between Foreign Exchange Market and Stock Market: The Case of Korea이나경, 한희준대외경제정책연구원 · East Asian Economic Review2016 · 제20권 제4호Extreme Spillover Effects of Volatility IndicesWing Lon Ng, Yue Peng경제연구소 · Journal of Economic Research (JER)2012 · 제17권 제1호Semiparametric ARCH-X Model for Leverage Effect and Long Memory in Stock Return Volatility한희준, Shen Zhang한국계량경제학회 · 계량경제학보2014 · 제25권 제3호Assessment of stock market contagion during the subprime mortgage crisis using GARCH-in-VAR model서병선, Jaehong kim경제연구소 · Journal of Economic Research (JER)2019 · 제24권 제2호The Out-of-Sample Predictability of Asymmetric Dependence of Portfolio Returns - The Multivariate Copula Distribution Function Approach이호진예금보험공사 · 금융안정연구2021 · 제22권 제1호A DUAL APPROACH TO MEASURING EQUITY MARKET INTERRELATIONSHIPSPeggy E. Swanson사람과세계경영학회 · Global Business and Finance Review2004 · 제9권 제1호

이 간행물의 다른 논문 (3건)

Measuring the Impact of Population Aging on Tax Revenue: Evidence from Japan and Korea허준영, 이강구pp. 1-32Implementable, Optimal Macroprudential and Monetary Policies: Implications for House Prices and Household Debt정용승, 목정환pp. 33-58KOSPI 200을 이용한 국면전환 연속시간 확률 변동성 모형의 추정최승문pp. 59-95
원문 보기