Paper: arXiv 2409.08205

Authors: Anindya Goswami, Nimit Rana

Abstract

In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous mathematical bridge between no-arbitrage option pricing theory and domain adaptation in machine learning. It validates the proposed common representation space approach empirically using NIFTY 50 and NIFTY Bank index options, specifically demonstrating resilience during domain-shifted periods like the COVID-19 lockdown.

Research Flowchart

  flowchart TD
    A[Goal: Market-Resilient Option Price Prediction] --> B[Data: Real-World Option Market Data]
    B --> C[No-Arbitrage Theory Derivation]
    C --> D[Construct Common Representation Space for Domain Adaptation]
    D --> E[Train Ensemble Learning Models]
    E --> F[Experimental Evaluation & Performance Testing]
    F --> G[Outcome: Accurate, Domain-Adapted Option Pricing]

Cited References (16)

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  2. A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks (1994)
  3. Option pricing using artificial neural networks: The case of S&P 500 Index Call Options (1996) — link
  4. Using neural networks to forecast the S & P 100 implied volatility (1996) — link
  5. A hybrid neural network approach to the pricing of options (1995) — link
  6. Option price forecasting using neural networks (2000) — link
  7. Option pricing with modular neural networks (2009)
  8. A note on a simple, accurate formula to compute implied standard deviations (1996) — link
and 8 more
  1. Data-driven option pricing using single and multi-asset supervised learning (2021) — link
  2. Pricing and hedging derivative securities with neural networks and a homogeneity hint (2000) — link
  3. Enhancing Option Pricing Accuracy in the I ndian Market: A CNN - B i LSTM Approach (2024) — link
  4. Predictive Learning Methods to Price European Options Using Ensemble Model and Multi-asset Data (2023) — link
  5. XGB oost: A Scalable Tree Boosting System (2016) — link
  6. Computing the B lack- S choles implied volatility (1996)
  7. A note on spurious model selection (2022) — link
  8. Neural networks for option pricing and hedging: a literature review (2020) — link

0 of 16 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.