Abstract

The dependence structure between the main energy markets (such as crude oil, natural gas, and coal) and the main stock index plays a crucial role in the economy of a given country. As the dependence structure between these series is dramatically complex and it appears to change over time, time-varying dependence structure given by a class of dynamic copulas is taken into account.To this end, each pair of time series returns with a dynamic t-Student copula is modelled, which takes as input the time-varying correlation. The correlation evolves with the DCC(1,1) equation developed by Engle.The model is tested through a simulation by employing empirical data issued from the Italian Stock Market and the main connected energy markets. The author considers empirical distributions for each marginal series returns in order to focus on the dependence structure. The model’s parameters are estimated by maximization of the log-likelihood. Also evidence is found that the proposed model fits correctly, for each pair of series, the left tail dependence coefficient and it is then compared with a static copula dependence structure which clearly underperforms the number of joint extreme values at a given confidence level.

Highlights

  • The dependence structure between the energy markets and the main stock index plays a fundamental role in the strategic development of the economy of a given country

  • We developed a procedure able to determine the complex and dynamic dependence structure between a stock market index (MIB index for the Italian market) and the main energy markets

  • The scheme proposed in our survey assumes that a general class of dynamic copulas represent the dependence structure between each pair of variables to face the empirical nature of time-varying dependence structure

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Summary

INTRODUCTION

The dependence structure between the energy markets and the main stock index plays a fundamental role in the strategic development of the economy of a given country. The second stage foresees to use copula functions, within each time segment, to fit the dependence structure involving the given energy commodities and the U.S stock market. Some authors such as Adams et al (2017), Engle and Sheppard (2001) and Tse (2000) developed several tests to ascertain the variability of correlation. The literature did not investigate yet the dependence structure among energy markets and stock price index from a dynamic framework In this survey, we investigate the Italian market and the main energy markets (crude oil, natural gas, and coal) to test the proposed model.

Copula functions
THE DATABASE DESCRIPTION
THE NUMERICAL
CONCLUSION
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