Interdependence Modeling for the Major Stock Markets and the Stock Portfolio Risk Management

다국어 초록 (Multilingual Abstract)

We employ a variety of dependence measures to test interdependence structure of the Korean and the US stock markets. We use daily returns on the KOSPI 200 and S&P 500. We measure a variety of dependence measures other than the linear correlation coefficient to characterize the copula function. The scale invariant dependence measure whose attribute can determine the form of the copula is a function of the ranks and is solely dependent upon the copula and not the marginal distributions of the data. Firstly, we calculate the quantile dependence which provides with the degree of asymmetric dependence in the extreme quantile by weighing the left tail to the right. Quantile dependence between the two variables is different from linear correlation or rank correlation whose values are scalars in the sense that it provides with varying degrees of asymmetric dependence from the center of the distribution to each extreme. Secondly, we compute the tail dependence which measures the synchronicity between extreme events and can be calculated as the population quantile dependence at the limit. Thirdly, we test for the existence of asymmetric and time-varying dependence. The time-varying conditional volatility of each series may induce time-varying conditional dependence. The test for time-varying dependence between the KOSPI 200 and S&P 500 standardized residuals is implemented. We then use the stationary bootstrap to construct the confidence intervals for the dependence measures. Lastly, we use the multi-stage GMM to estimate the constant parametric copula function and the time-varying copula function.

참고문헌 (Reference)

1 Politis, D. N., "The Stationary Bootstrap" 89 : 1303-1313, 1994

2 Andrews, D. W. K., "Tests for Parameter Instability and Structural Change with an Unknown Change Point" 61 : 821-856, 1993

3 Rémillard, B., "Testing for Equality Between Two Copulas" 100 (100): 377-386, 2009

4 Patton, A. J., "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation" 2 (2): 130-168, 2004

5 Patton, A. J., "Modelling Asymmetric Exchange Rate Dependence" 47 (47): 527-556, 2006

6 Gonçalves, S., "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models" 119 : 199-220, 2004

7 Patton, A. J., "Handbook of Financial Time Series" Springer Verlag 2009

8 Rémillard, B., "Goodness-of-fit tests for copulas of multivariate time series" 5 : 1-23, 2017

9 Creal, D, "Generalized Autoregressive Score Models with Applications" 2011

10 Chen, X., "Estimation of copula-based semiparametric time series models" 130 : 307-335, 2006

11 Patton, A. J., "Estimation of Multivariate Models for Time Series of Possibly Different Lengths" 21 (21): 147-173, 2006

12 Chen, X., "Estimation and model selection of semiparametric multivariate survival functions under general censorship" 157 : 129-142, 2010

13 Chen, X., "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification" 135 : 125-154, 2006

14 Frahm, G., "Estimating the tail-dependence coefficient: Properties and pitfalls" 37 : 80-100, 2005

15 Chen, X., "Efficient estimation of semiparametric multi-variate copula models" 101 (101): 1228-1240, 2006

16 Chen, X., "Efficient estimation of copula-based semiparametric Markov models" 37 : 4214-4253, 2009

17 Rémillard, B., "Copula-based semiparametric models for multivariate time series" 110 : 30-42, 2012

18 Patton, A. J., "Copula Methods for Forecasting Multivariate Time Series" 2 : 899-960, 2013

19 Harvey, C. R., "Conditional Skewness in Asset Pricing Tests" 55 (55): 1263-1295, 2000

20 Harvey, C. R., "Autoregressive Conditional Skewness" 34 : 465-487, 1999

21 Engle, R. F., "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation" 50 : 987-1007, 1982

22 Patton, A. J., "Are “Market Neutral” Hedge Funds Really Market Neutral?" 22 (22): 2495-2530, 2009

23 Genest, C., "A semiparametric estimation procedure of dependence parameters in multivariate families of distributions" 82 (82): 543-552, 1995

24 Patton, A. J., "A Review of Copula Models for Economic Time Series" 110 : 4-18, 2012