Risk Analysis of Robust Recursive Updating Model

Robust recursive updating model is insensitive to the outliers in observed flow data and is effective to obtaining stable flood updating accuracy. At the same time, it is risky to detect falsely good value as outliers. Using Monte-Carlo method, the relations between risk and effect of model are got. The study results indicate the risk of the model is impacted by frequency of outliers.

Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock's return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles, and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents' estimates of risk. (JEL D81, D83, E32, G01, G12)