Doubts and Variability: A robust perspective on exotic consumption series
(with Matt Smith)
Asset-pricing models have experienced success by augmenting the consumption process with Long-Run Risk and Rare Disasters. Acknowledging that both phenomena are naturally subject to ambiguity, we show that an ambiguity-averse agent may behave as if Long-Run Risk and disasters exist even if they do not or exaggerate them if they do.
Long-Run Risk Is the Worst-Case Scenario
(with Ian Dew-Becker)
How do you value assets when you don't know how the world works? We study an investor who is unsure of the dynamics of the economy. Not only are parameters unknown, but the investor does not even know what order model to estimate -allowing potentially infinite-order dynamics. She prices assets using a pessimistic model that minimizes lifetime utility subject to a constraint on statistical plausibility. We show this helps explainimportant asset pricing phenomena.
De-leveraging or de-risking? How banks cope with loss
(with Adam Shapiro and John Krainer)
What happens when a bank is damaged? Do they stop lending and shrink? This is the standard intuition and much of the academic literature has focused on testing this by looking at a given type of lending - deciding that this indeed is what happens. We show that the reality is more subtle. We find that banks do retrench when damaged, but by tuning their lending terms to adjust an array of assets in different directions, not to de-lever, but to de-risk.
Robust Animal Spirits
(with Matt Smith)
Keynes coined the phrase "animal spirits" and, ever since, people have been interested in defining this nebulous concept. We provide an elegant interpretation by retaining small r "rationality" while relaxing big "R" Rationality, in the sense of Rational Expectations. If reasonable people "doubt" their model of the world, the interaction of these doubts with fluctuations in volatility induces them to behave as if they are subject to waves of sentiment.
(with Raffaella Giacomini and Andrew McKenna)
Stress tests have become important components of macroprudential regulation yet it is difficult to design such frameworks in the
context of pervasive model uncertainty. We illustrate methods for responding to possible misspecifications in models used for assessing bank vulnerabilities. We show how 'exponential tilting' allows the incorporation of external judgment, captured in moment conditions, into a forecasting model as a partial correction for misspecification. We also make use of methods from robust control to seek the most relevant dimensions in which a regulator's forecasting model might be misspecified - a search for a 'worst case' model that is a `twisted' version of the regulator's initial forecasting model. Finally, we show how the two approaches can be blended.
How much risk comes from high frequencies, business cycles frequencies or low frequency swings? If these properties are under the influence of a person or institution, whose incentives depend upon the distribution of risk across frequencies, then she will manipulate this distribution. If her 'principal' is myopic with regard to certain frequencies, she will choose to hide risk by shifting power from frequencies to which the
regulator is attuned to those to which he is not.
Software (coming soon)
DSGEn is a package written in Mathematica to allow economists to specify DSGE models up to a given parameterization and then generate Fortran or Matlab code that will solve the systems of equations necessary to approximate the model's equilibrium relations up to an arbitrary (though memory constrained) order of perturbation around the non-stochastic steady state. It will also generate code to simulate the resultant model. The main contribution is that the parameterization need not be set during the code generation step.