Archive
Standard diversification models have a fundamental weakness: during periods of large market fluctuations and crises, historical relationships stop working. This happens because correlations between assets tend to increase, […]
π― GARCH is not just a descriptive model β its purpose is to forecast the volatility of future returns on invested capital. In previous posts, we: Now we […]
Models of return volatility such as EWMA and GARCH aim to explain volatility clustering. In real markets, calm periods tend to be followed by calm periods, while turbulent […]
The maximum likelihood method is used in modeling to estimate the parameters that make historical events most probable. Suppose an event has occurred. If we assume that this […]
GARCH (1,1) and Volatility Clustering Financial markets exhibit an important property: volatility clustering and reversion toward a long-run average. In other words, large movements tend to be followed […]
Volatility is such an overused term that we may forget how important the assumptions are that lead to the final number. Let us start with the basics. When […]
In previous posts, I covered the calculation of portfolio Value at Risk (VaR) and Expected Shortfall using the historical simulation and linear modeling methods. Now, in order to […]
