Monte Carlo Simulation for Option Pricing under Asymmetric Market Volatility Conditions
Keywords:
Monte Carlo simulation, option pricing, asymmetric volatility, stochastic volatility, financial derivatives, emerging marketsAbstract
The objective of this study is to develop and empirically evaluate a Monte Carlo simulation framework for accurately pricing financial options under asymmetric volatility conditions in an emerging market environment. This quantitative study employed a computational finance design using real option and stock market data from the Tehran financial market across multiple volatility regimes. Underlying asset returns were modeled using asymmetric stochastic volatility processes capable of capturing skewness, excess kurtosis, leverage effects, and regime dependence. Model parameters were estimated from historical price series, and thousands of simulated price paths were generated for each option contract. Option values were computed as discounted expected payoffs under the risk-neutral measure. The pricing performance of the proposed asymmetric Monte Carlo model was evaluated against the Black–Scholes benchmark and a symmetric-volatility Monte Carlo model using standard accuracy metrics including root mean squared error, mean absolute error, and pricing bias. Robustness and sensitivity analyses were conducted to assess stability across volatility regimes and parameter shocks. The asymmetric Monte Carlo model produced significantly lower pricing errors than both benchmark models across all evaluation metrics (RMSE = 0.87 versus 2.42 for Black–Scholes and 1.59 for symmetric Monte Carlo, p < 0.01). Model superiority was especially pronounced during high-volatility periods, where pricing accuracy improved by over 60% relative to traditional models. Sensitivity analysis demonstrated strong nonlinear amplification of option values in response to volatility asymmetry shocks, confirming the economic significance of incorporating asymmetric risk dynamics. The results demonstrate that ignoring volatility asymmetry leads to substantial and systematic option mispricing, while simulation-based valuation under asymmetric volatility provides robust, stable, and economically meaningful improvements in pricing accuracy, particularly under turbulent market conditions.
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