Modeling Market Turbulence: Nonlinear Time Series Approaches to Financial Shocks and Regulatory Impact

Financial markets are inherently complex and characterized by volatility, regime shifts, and nonlinear dynamics. This complexity necessities robust analytical frameworks for better understanding and forecasting market behavior. This study integrates traditional econometric models with advanced machine learning techniques to examine market turbulence, volatility forecasting, and the impact of regulatory interventions. By employing Markov Switching models, Threshold Autoregressive (TAR) models, and Smooth Transition Autoregressive (STAR) models, we capture regime shifts and asymmetries in financial time series. Realized Volatility (RV) and Realized GARCH models further refine volatility estimation at high frequencies. To detect sudden jumps and structural breaks, we apply the Barndorff-Nielsen and Shephard test which allowed us to identify abrupt market movements with statistical rigor. Wavelet transform analysis decomposes financial time series into frequency components, enhancing our understanding of multi-scale financial fluctuations. Moreover, Long Short-Term Memory (LSTM) networks are implemented to capture nonlinear dependencies and improve volatility forecasting. In parallel, Support Vector Machines (SVM) classify market conditions into crisis and stable periods, aiding in risk assessment and market monitoring. Furthermore, this study evaluates the effectiveness of regulatory policies and central bank interventions using event study analysis, measuring their impact on market volatility and investor sentiment. Sentiment analysis of financial news and central bank statements complements this, revealing how policy communication influences market reactions. The findings underscore the critical role of combining traditional econometric approaches with machine learning methodologies in financial market analysis. The results provide valuable insights for investors, policymakers, and financial institutions in developing data-driven strategies for risk management and market stability.