Data-driven Approach for Static Hedging of Exchange Traded Options
Data-driven machine learning for static hedging of NSE index options, comparing with dynamic hedging and Carr-Wu model.
What it examines
This paper introduces a data-driven machine learning algorithm for semi-static hedging of exchange-traded options, considering transaction costs. It empirically evaluates the performance of hedging longer-term NSE Index options using shorter-term options and cash positions, comparing it with dynamic hedging and the Carr-Wu static hedge.
What it concludes
The study concludes that Lasso regression-based static hedging is superior to dynamic hedging, particularly in volatile markets. Potential applications include improved risk management strategies for financial institutions. Future research could explore higher-order risk factors and interaction terms in static hedging.
Evidence objects
The study concludes that Lasso regression-based static hedging is superior to dynamic hedging, particularly in volatile markets. Potential applications include improved risk management strategies for financial institutions. Future research could explore higher-order risk factors and interaction terms in static hedging.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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… ▽ More 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. △ Less
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