This chapter introduces the Buffered Threshold Periodic Stochastic Volatility (BTPSV) model, an extension of existing stochastic volatility models that incorporates both periodic correlation and asymmetric responses to market fluctuations. By integrating features from periodic autoregressive stochastic volatility ( \(PAR-SV\) ) and buffered threshold stochastic volatility (BTSV) models, BTPSV provides a more flexible framework for capturing volatility patterns in financial time series. We propose an Expectation-Maximization (EM) algorithm with particle filtering for parameter estimation and validate our approach through Monte Carlo simulations. An empirical application to the EUR/DZD exchange rate demonstrates the models superior performance compared to traditional approaches. The BTPSV model enhances forecasting accuracy and improves risk assessment by accommodating periodic and nonlinear volatility behaviors, making it particularly useful for financial market analysis and policy decisions.