Animal spirits and business cycles
Animal spirits are often suggested as a cause of business cycles, but they are very difficult to define. Recent research proposes a novel explanation based on the changing level of risk over time and people?s uncertainty about how the world works. The interaction of these two can lead to significant business cycle fluctuations in response to spikes in volatility. This finding gives researchers an alternative to irrational behavior as an explanation for why swings in consumer sentiment appear to drive the business cycle.
AUTHORS: Bidder, Rhys
Worst-case scenarios and asset prices
Investors have a hard time accounting for uncertainty when calculating how much risk they are willing to bear. They can use economic models to project future earnings, but many models are misspecified along important dimensions. One method investors appear to use to protect against particularly damaging errors in their model is by projecting worst-case scenarios. The responses to such pessimistic predictions provide insights that can explain many of the puzzles about asset prices.
AUTHORS: Bidder, Rhys
Trend Job Growth: Where's Normal?
With the U.S. labor market at or near maximum employment, assessing trend job growth has become increasingly important. This ?breakeven? rate, which is the pace of job growth needed to maintain a healthy labor market, depends primarily on growth in the labor force. Estimates that account for population aging and potential labor force participation trends suggest that trend growth ranges between about 50,000 and 110,000 jobs per month. Actual job growth has been well above this pace, implying that it can slow substantially in the future without undermining labor market health.
AUTHORS: Valletta, Robert G.; Mahedy, Tim; Bidder, Rhys
Are wages useful in forecasting price inflation?
Labor costs constitute a substantial share of business expenses, and it is natural to expect wages to be an important determinant of prices. However, research suggests that wages do not contain much useful information for forecasting price inflation that is not available from other indicators. Therefore, one should not infer too much from recent wage data regarding the future path of inflation.
AUTHORS: Bidder, Rhys
How Do Banks Cope with Loss?
When lenders experience unexpected losses, the supply of credit to borrowers can be disrupted. Researchers and policymakers have long sought estimates of how the availability of loans changes following a shock. The sudden oil price decline in 2014 offers an opportunity to observe precisely how affected lenders altered their portfolios. Banks that were involved with oil and gas producers cut back on some types of lending?consistent with traditional views of bank behavior. However, they expanded other types of lending and asset holdings with a bias towards less risky securities.
AUTHORS: Shapiro, Adam Hale; Krainer, John; Bidder, Rhys
Long-Run Risk is the Worst-Case Scenario: Ambiguity Aversion and Non-Parametric Estimation of the Endowment Process
We study an agent who is unsure of the dynamics of consumption growth. She estimates her consumption process non-parametrically to place minimal restrictions on dynamics. We analytically show that the worst-case model that she uses for pricing, given a penalty on deviations from the point estimate, is a model with long-run risks. This result cannot in general be matched in a fixed model with only parameter uncertainty. With a single parameter determining risk preferences, the model generates high and volatile risk premia and matches R2s from return forecasting regressions, even though risk aversion is equal to 5.3 and the worst-case dynamics are statistically nearly indistinguishable from the true model.
AUTHORS: Dew-Becker, Ian; Bidder, Rhys
Stress Testing with Misspecified Models
Stress testing has become an important component of macroprudential regulation yet its goals and implementation are still being debated, reflecting the difficulty of designing such frameworks in the context of enormous 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 so that one can search for a worst case model subject to restrictions on its properties, informed by the regulator?s judgment. We demonstrate the methods using the New York Fed?s CLASS model, a top-down capital stress testing framework that projects the effect of macroeconomic scenarios on U.S. banking firms.
AUTHORS: Bidder, Rhys; McKenna, Andrew; Giacomini, Raffaella
Doubts and Variability: A Robust Perspective on Exotic Consumption Series
In order for consumption based asset pricing models to reconcile data on returns with that on consumption, researchers have resorted to augmenting the consumption series in exotic ways. When an agent?s consumption series is subject to changes in volatility, we show that concerns for model misspecification can induce fears of both disasters and long run risk. We appeal to this pessimistic view to explain why introducing stochastic volatility in the presence of model uncertainty helps generate a more plausible unconditional market price of risk and time variation in the conditional market price of risk. Our analysis is based on a parameterization derived from Bayesian estimation of our stochastic volatility model using US consumption data.
AUTHORS: Smith, Matthew E.; Bidder, Rhys
What determines the frequency domain properties of a stochastic process? How much risk comes from high frequencies, business cycle frequencies or low frequency swings? If these properties are under the influence of an agent, who is compensated by a principal according to the distribution of risk across frequencies, then the nature of this contracting problem will affect the spectral properties of the endogenous outcome. We imagine two thought experiments: in the first, the principal (or ?regulator?) is myopic with regard to certain frequencies?he is characterized by a filter?and the agent (?bank?) chooses to hide risk by shifting power from frequencies to which the regulator is attuned to those to which he is not. Thus, the regulator is fooled into thinking there has been an overall reduction in risk when, in fact, there has simply been a frequency shift. In the second thought experiment, the regulator is not myopic, but simply cares more about risk from certain frequencies, perhaps due to the preferences of the constituents he represents or because certain types of market incompleteness make certain frequencies of risk more damaging. We model this intuition by positing a filter design problem for the agent and also by a particular type of portfolio selection problem, in which the agent chooses among investment projects with different spectral properties. We discuss implications of these models for macroprudential policy and regulatory arbitrage.
AUTHORS: Bidder, Rhys
Robust stress testing
In recent years, stress testing has become an important component of financial and macro-prudential regulation. Despite the general consensus that such testing has been useful in many dimensions, the techniques of stress testing are still being honed and debated. This paper contributes to this debate in proposing the use of robust forecasting analysis to identify and construct adverse scenarios that are naturally interpretable as stress tests. These scenarios emerge from a particular pessimistic twist to a benchmark forecasting model, referred to as a ?worst case distribution?. This offers regulators a method of identifying vulnerabilities, even while acknowledging that their models are misspecified in possibly unknown ways. We first carry out our analysis in the familiar Linear-Quadratic framework of Hansen and Sargent (2008), based on an estimated VAR for the economy and linear regressions of bank performance on the state of the economy. We note, however, that the worst case so constructed features undesirable properties for our purpose in that it distorts moments that we would prefer were left undistorted. In response, we formulate a finite horizon robust forecasting problem in which the worst case distribution is required to respect certain moment conditions. In this framework, we are able to allow for rich nonlinearities in the benchmark process and more general loss functions than in the L-Q setup, thereby bringing our approach closer to applied use.
AUTHORS: Bidder, Rhys; McKenna, Andrew