9,102 research outputs found
Forecasting Value-at-Risk Using the Markov-Switching ARCH Model
This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence†found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the VaR forecasts derived from the Markov-switching ARCH model are preferred to alternative parametric and nonparametric VaR models that only consider time-varying volatility. JEL classification: C22, C52, G28. Keywords: Value-at-Risk, Switching-regime ARCH models.Value-at-Risk, Switching-regime ARCH models
Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response
Classic mechanism/information design imposes the assumption that agents are
fully rational, meaning each of them always selects the action that maximizes
her expected utility. Yet many empirical evidence suggests that human decisions
may deviate from this full rationality assumption. In this work, we attempt to
relax the full rationality assumption with bounded rationality. Specifically,
we formulate the bounded rationality of an agent by adopting the quantal
response model (McKelvey and Palfrey, 1995).
We develop a theory of rationality-robust information design in the canonical
setting of Bayesian persuasion (Kamenica and Gentzkow, 2011) with binary
receiver action. We first identify conditions under which the optimal signaling
scheme structure for a fully rational receiver remains optimal or approximately
optimal for a boundedly rational receiver. In practice, it might be costly for
the designer to estimate the degree of the receiver's bounded rationality
level. Motivated by this practical consideration, we then study the existence
and construction of robust signaling schemes when there is uncertainty about
the receiver's bounded rationality level
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