Finance Theory — 14.6: Risk Parameter Estimation and Stationarity скачать в хорошем качестве

Finance Theory — 14.6: Risk Parameter Estimation and Stationarity

How much historical data should you use to estimate portfolio risk parameters — and when should you override the math with judgment? This video walks through the covariance estimation formula with a concrete numerical example, explains the fundamental tradeoff between statistical precision and economic relevance, and builds a practical decision framework for choosing estimation windows. Key concepts covered: • Sample covariance formula: computing cross-products of return deviations and averaging across periods • Numerical example: two-stock, four-period covariance calculation step by step • Non-stationarity: why the statistical properties of financial returns change over time • The estimation window tradeoff: more data reduces noise, but older data introduces systematic bias • Annualizing volatility: converting monthly variance to annual variance using the square root of 12 rule • Portfolio theory vs. stock picking: two fundamentally different investment philosophies and their assumptions • Why crisis data (1987, 1998, 2000, 2008, 2020) should not be excluded from long-term estimates • Rolling correlations: how S&P 500 vs. NASDAQ correlation shifts dramatically during crises • Practitioner decision framework: structural breaks, regime dependence, and investment horizon • The Venn diagram of effective risk estimation: mathematical framework, economic understanding, and contextual judgment ORIGINAL SOURCE This video is based on content from the following source:    • Ses 14: Portfolio Theory II