This study focuses on forecasting the volatility of TVS Motor Company's stock under various market situations, including stable, bullish, bearish, and highly volatile periods. Understanding the extent of price fluctuations is essential for investors to make informed decisions, reduce risk, and plan their investments more effectively-particularly in a dynamic market like India. To achieve this, the research applies several advanced econometric models. It begins with the ARCH (Autoregressive Conditional Heteroskedasticity) model, which identifies whether the stock's volatility changes over time. It then uses the GARCH (Generalized ARCH) model, which offers a more detailed analysis by considering previous price movements and their influence on current volatility. These models are well-suited for financial data with time-varying fluctuations. In addition, the study uses a VAR (Vector Auto-regression) model to explore how different economic variables influence each other and impact stock prices over time. Although the term "Box-Jenkins" or BJ model is not explicitly used, ARIMA (Auto-Regressive Integrated Moving Average) models are included to forecast stock trends and examine volatility in the automobile sector, with a focus on TVS Motor Company. By studying historical stock data under different market phases, the research provides valuable insights into how TVS Motor's stock behaves, helping investors and financial professionals make better strategic and risk-related decisions.