++ My question is technical and academic in nature. I am currently involved in a research so I need an expert answer in simple terms ++
How can I interpret the ARCH and GARCH coefficients in analyzing financial time series (stock returns)? I know, the among so many other implications, the sum of the coefficients indicates volatility clustering and persistence. But my question is how can I infer conclusions, in simple words, from the results that can be useful for the ordinary investors, the policymakers and the capital market. What can say about the riskiness of stock market when ARCH and GARCH coefficients are statistically significant. My professor asks me to come up with clear implications that is understandable by everybody. I am not a very technical person. I need a comprehensive and non-technical answer please.
(General) auto-regressive conditional heteroskedasticity measures the concentration of variance from the mean trend in a sample. It is useful to determine whether the excess returns observed in a sample are the result of one or two great picks or whether the entire sample exhibits out-performance. If you remove the one or two outliers (which may be errors, or the result of industry or country skewness) and then the sample loses most of its alpha, you have a dangerous model. On the other hand, if there are no general outliers, and the returns are normally distributed, then you have a good stock selection model, which should perform well as long as the underlying assumptions remain valid. |
