Prices, returns and volatilities are not independent draws: today depends on yesterday, quiet weeks follow quiet weeks and two prices can drift together for years. This skill teaches the tools that respect that order, with short checks (numbers you compute, steps you order, concepts you match) and finance examples: why a price series needs differencing before you regress on it, how the ACF and PACF point to an AR or MA model, what a unit root means for a trading rule, why volatility clusters and how GARCH uses it, when two stocks are truly cointegrated, how a Kalman filter tracks a hidden hedge ratio and why a forecast must be scored out of sample. The capstone is a code lab where you build volatility forecasters yourself and compare them on real SPY returns.
Statistics is expected (regression, hypothesis tests, maximum likelihood) and basic probability helps. No time-series background is needed.
