Data-driven Approach for Static Hedging of Exchange Traded Options

TL;DR

This paper introduces an interpretable machine learning approach for static hedging of exchange traded options, demonstrating its effectiveness through empirical testing and benchmarking against industry standards under various market conditions.

Contribution

It develops a novel data-driven, interpretable machine learning algorithm for semi-static hedging that accounts for transaction costs and is validated with extensive empirical analysis.

Findings

The proposed method effectively hedges long-term NSE index options.

It outperforms static and dynamic benchmarks in various market scenarios.

Profit and Loss attribution reveals key factors behind static hedging success.

Abstract

This paper presents a data-driven interpretable machine learning algorithm for semi-static hedging of Exchange Traded options, considering transaction costs with efficient run-time. Further, we provide empirical evidence on the performance of hedging longer-term National Stock Exchange (NSE) Index options using a self-replicating portfolio of shorter-term options and cash position, achieved by the automated algorithm, under different modeling assumptions and market conditions, including Covid period. We also systematically assess the model's performance using the Superior Predictive Ability (SPA) test by benchmarking against the static hedge proposed by Peter Carr and Liuren Wu and industry-standard dynamic hedging. We finally perform a thorough Profit and Loss (PnL) attribution analysis on the target option and hedge portfolios (dynamic and static) to discern the factors explaining the superior performance of static hedging.