Paper: arXiv 2302.00728

Authors: Vikranth Lokeshwar Dhandapani, Shashi Jain

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper combines solid mathematical modeling of option replication with strong empirical rigor using real NSE data across stressed market regimes (e.g., COVID-19). Its inclusion of statistical benchmark tests like SPA and rigorous PnL attribution elevates its practical and academic value.

Research Flowchart

  flowchart TD
    A["Research Goal: Data-Driven Semi-Static Option Hedging with Transaction Costs"] --> B["Data Inputs: NSE Index Options<br/>(Including COVID-19 Period)"]
    B --> C["Interpretable ML Algorithm<br/>(Self-Replicating Portfolio Strategy)"]
    C --> D["Benchmarking & SPA Test<br/>vs. Carr-Wu Static & Dynamic Hedging"]
    D --> E["PnL Attribution Analysis<br/>(Decomposing Performance Factors)"]
    E --> F["Outcomes: Superior Empirical Performance,<br/>Cost Efficiency & High Interpretability"]