Measuring Information Flows in Option Markets : A Relative Entropy Approach

In this paper, we propose a methodology for measuring information flows underpinning option price movements, and for analyzing the distribution of these flows. We develop a framework in which flows of information can be measured in terms of relative entropy between risk-neutral distributions obtained from implied volatility data at different dates. We set up a numerical methodology to compute such quantities from empirical market data corresponding to options written on the S&P 500 index. This methodology uses Normal Inverse Gaussian distributions for the log-return of the index. We apply our method to six years of daily data, from 2015 to 2021. In our results, options with short maturities catch a greater share of new information. We also analyze the distribution of the obtained information flows with a mixture of two exponential distributions. This mixture works with one component corresponding to frequent small values and the other to less frequent high values