A Semi-Parametric Copula Analysis of Asymmetric Dependence Between Indian and U.S. Stock-Equity Markets

Abstract

This study examines the dependence structure between Indian and U.S. equity markets using a semi-parametric copula-based framework that accounts for nonlinear and asymmetric co-movements. Daily closing price data for the NIFTY index and the NASDAQ Composite index, obtained from Yahoo Finance over the period January 2016 to March 2025, are analyzed. Marginal return distributions are modeled nonparametrically using empirical distribution functions, while the joint dependence structure is characterized through parametric copula models capable of capturing tail dependence. Copula parameters are estimated
using likelihood-based methods, and model adequacy is evaluated through formal goodness-of-fit criteria. The empirical findings reveal significant asymmetric dependence between the two markets, with stronger co-movements observed during extreme market conditions than in the central regions of the return distribution. These results indicate that linear correlation measures understate cross-market dependence during periods of stress, thereby overstating potential diversification benefits and underscoring the importance of tail-sensitive dependence modeling in international equity markets.