Option Pricing via Multi-path Autoregressive Monte Carlo Approach

TL;DR

This paper introduces a novel multi-path autoregressive Monte Carlo method for option pricing that learns price characteristics and re-generates paths, offering a potentially efficient alternative to traditional models like Black-Scholes and Binomial Tree.

Contribution

The paper presents a new autoregressive Monte Carlo approach for option pricing that incorporates learned price characteristics to improve path generation.

Findings

Approach produces comparable pricing accuracy to traditional models.

Method effectively captures price dynamics of weekly options on TAIEX.

Demonstrates potential for efficient real-time option pricing.

Abstract

The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.