We study a worst-case approach to measure the sensitivity to model misspecification in the performance analysis of stochastic systems. The situation of interest is when only minimal parametric information is available on the form of the true model. Under this setting, we post optimization programs that compute the worst-case performance measures, subject to constraints on the amount of model mi...

This article contains two novelties. First, a unified framework for testing and evaluating the adequacy of an estimated autoregressive conditional duration (ACD) model is presented. Second, two new classes of ACD models, the smooth transition ACD model and the time-varying ACD model, are introduced and their properties discussed. New misspecification tests for the ACD class of models are introd...

Simon’s and Theil’s certainty equivalence property justifies a convenient algorithm for solving dynamic programming problems with quadratic objectives and linear transition laws: first, optimize under perfect foresight, then substitute optimal forecasts for unknown future values. A similar decomposition into separate optimization and forecasting steps prevails when a decision maker wants a deci...

In the empirical analysis of panel data the Breusch Pagan statistic has become a standard tool to infer on unobserved heterogeneity over the cross section. Put differently, the test statistic is central to discriminate between the pooled regression and the random effects model. Conditional versions of the test statistic have been provided to immunize inference on unobserved heterogeneity agains...

In this work we investigate the impact of misspecification of the innovations in fitting Garch$(1,1)$ models. We show that an incorrect specification of the innovations together with the reduction of the parameter space to the weak stationarity region, can give rise to a spurious IGARCH effect. We address this point through an extensive Monte Carlo simulation study. We also analyse the impact o...

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different rewarding distinct aspects forecast performance. Herein, we re-investigate practice using proper produce forecasts that ‘optimal’ according a given score and when their is superior alternative score. Particular attention paid relative predictive performance under misspecification model....

This work takes a critical look at the application of conventional machine learning methods to wireless communication problems through lens reliability and robustness. Deep techniques adopt frequentist framework, are known provide poorly calibrated decisions that do not reproduce true uncertainty caused by limitations in size training data. Bayesian learning, while principle capable addressing ...

This paper examines a continuous-time intertemporal consumption and portfolio choice problem for an investor with recursive preferences. The investor worries about model misspecification and seeks robust decision rules. The expected excess return of a risky asset follows a mean-reverting process. I find that whether the concern about model misspecification decreases the total demand for equitie...

This comment discusses some errors in a recent paper by Jacobsen and Marquering [Jacobsen, B., Marquering, W., 2008. Is it the weather? Journal of Banking and Finance 32 (4), 526–540], in which the authors challenge our previous finding that stock market returns exhibit seasonal patterns consistent with the influence of seasonal affective disorder on investor risk aversion. We find that we cann...

We derive a parsimonious returns-based stochastic discount factor that is robust to model misspecification. We consider a general equilibrium model with heterogeneous agents who can invest their wealth in many assets. As long as (i) agents have (individual-, time-, and state-dependent) recursive preferences that are homothetic in current consumption and continuation value with a common relative...