Build practical skills in financial time-series analysis, quantitative finance, and algorithmic trading. This course teaches you to analyze, forecast, and interpret financial data using linear, nonlinear, and multivariate models.

You’ll examine stationarity, random walks, autocorrelation, persistence, and mean reversion before building AR, MA, ARMA, and ARIMA models and evaluating forecast reliability through residual diagnostics. You’ll then explore nonlinear market dynamics using threshold, kernel, bilinear, neural-network, and regime-based methods. The course also connects forecasting with financial pricing through stochastic processes, no-arbitrage reasoning, Black–Scholes foundations, and jump-diffusion models. You’ll analyze relationships across multiple financial time series using covariance, cointegration, error-correction models, PCA, and vector autoregression. You’ll also interpret impulse responses, variance decomposition, and Granger causality. Designed for learners interested in quantitative finance, investment analysis, risk assessment, and algorithmic trading, this course helps you evaluate quantitative models and use their outputs to support informed financial decisions.