Asymmetry, Loss Aversion, and Forecasting
Conditional volatility models have been used extensively in finance to capture predictable variation in the second moment of returns. However, with recent theoretical literature emphasizing the loss-averse nature of agents, this paper considers models that capture time variation in the second lower partial moment. Utility-based evaluation is carried out on several approaches to modeling the conditional second-order lower partial moment. The findings show that when agents are loss averse, there are utility gains to be made from using models that explicitly capture this feature. These results link the theoretical discussion on loss aversion to empirical modeling.
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