CRYPTOCURRENCY VOLATILITY AND RISK MODELING: MONTE CARLO SIMULATIONS, GARCH ANALYSIS, AND FINANCIAL MARKET INTEGRATION

Abstract

Cryptocurrencies have rapidly emerged as a significant financial asset class, influencing global monetary systems and financial markets. However, their extreme volatility, speculative nature, and evolving regulatory landscape pose challenges to investors, policymakers, and financial analysts. This study presents an in-depth quantitative analysis of cryptocurrency volatility and risk assessment, focusing on Bitcoin (BTC-USD) and its correlation with traditional financial assets, including the EUR/USD exchange rate and S&P 500 index. Our research employs Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to measure the dynamic volatility patterns of Bitcoin, revealing the asset’s substantial fluctuations over time and its sensitivity to market shocks. Additionally, we utilize Monte Carlo simulations to forecast potential future price movements of Bitcoin, highlighting risk scenarios and the probability distribution of price trajectories over a one-year period. The Value-at-Risk (VaR) model is implemented to estimate potential losses within a given confidence interval, providing a robust measure of downside risk. Furthermore, the study examines the integration of cryptocurrency markets with traditional financial instruments by analyzing cross-asset correlations and volatility spillover effects. The findings suggest that while Bitcoin remains a highly volatile asset, its correlation with the broader financial system is increasing, indicating a potential shift towards mainstream financial adoption. The results contribute to the ongoing debate on whether cryptocurrencies serve primarily as speculative instruments or as viable components of diversified investment portfolios. These insights are valuable for institutional investors, risk managers, and policymakers in designing more effective risk mitigation strategies for cryptocurrency investments.