This document discusses using independent component analysis (ICA) and locally adaptive volatility estimation (LAVE) to calculate value at risk (VaR). It introduces ICA, LAVE, GARCH, and RiskMetrics models. An empirical study applies these methods to Taiwan stock market data from 2000 to 2010. The results show that combining ICA and LAVE provides more accurate VaR estimates than RiskMetrics or GARCH alone, by better capturing changes in volatility over time. However, the document notes that more flexible settings may be needed for higher confidence levels.