Financial Econometrics & Volatility Modeling for Quants

What Will You Learn?

• Time Series Foundations: Build a strong understanding of financial time series data, stationarity, autocorrelation, seasonality, and the key assumptions underlying time series modeling.
• Time Series Models: Learn how Autoregressive (AR), Moving Average (MA), ARMA, ARIMA, SARIMA, and SARIMAX models are constructed, estimated, and applied to financial markets.
• Model Selection & Diagnostics: Master model identification using ACF and PACF plots, parameter estimation, residual analysis, and model selection using AIC and BIC criteria.
• Multivariate Time Series Modeling: Explore Vector Autoregression (VAR), cointegration techniques, and Error Correction Models (ECM) to model relationships among multiple financial variables.
• Volatility Modeling: Understand volatility clustering and learn industry-standard models including EWMA, GARCH, EGARCH, and GJR-GARCH for volatility forecasting and risk estimation.
• Risk Analytics & Forecast Evaluation: Apply time series and volatility models to Value-at-Risk (VaR), Expected Shortfall (ES), backtesting, stress testing, and quantitative risk management.
• Python Implementation: Build end-to-end forecasting and volatility models using Python and real financial market data, following industry best practices.
• Industry Applications & Projects: Develop practical projects in forecasting, trading, risk management, and quantitative research that can strengthen your resume and interview preparation.