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

Data-driven Approach for Static Hedging of Exchange Traded Options