Statistical Methods in Finance 2018

Dec 17 - 20, 2018


Cricket and Stock Markets

by Rangan Gupta

The objective of this paper is to analyze whether One Day International (ODI) cricket match performances can predict returns and volatility of the Indian stock market, over the period of 1992-2017. For our purpose, we use a k-th order Nonparametric Causality-in-Quantiles approach, recently developed by Balcilar et al., (2018). This approach has three advantages: Firstly, it is robust to misspecification errors as it detects the underlying dependence structure between the examined time series based on a nonparametric framework. Secondly, we are able to test for not only causality-in-mean (1st moment), but also causality that may exist in the tails of the distribution of the variables. Finally, we are also able to investigate causality-in-variance and, thus, study higher-order dependency. Based on this test, we find strong evidence of predictability over the entire conditional distribution of returns, with losses being a stronger predictor at lower quantiles, while wins performs better at upper quantiles. For volatility predictability is restricted to lower quantiles only, with losses being the stronger predictor. In a robustness analysis, we find more or less similar results for Pakistan as well.