Rhys Bidder, Economist, Federal Reserve Bank of San Francisco

Rhys Bidder


Macroeconomic Research

Macroeconomics, Financial economics, Computational methods

Rhys.Bidder (at) sf.frb.org


Working Papers
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Drilling into Bank Balance Sheets: Examining Portfolio Responses to an Oil Shock

2017-03 | With Krainer and Shapiro | January 2017

abstract (+)
Using detailed bank balance sheet data obtained under the United States’ stress testing programs we examine how a shock to banks’ net worth affects their portfolio decisions. We focus on the supply of credit (the bank lending channel) and the ultimate effect on borrowers (the credit channel), but also examine how the shock affects banks’ overall risk profile and security holdings. Our shock is derived from variation across banks in their loan exposure to industries adversely affected by the precipitous oil price declines of 2014. For corporate lending, we find significant evidence of a bank lending channel. Banks more exposed to the shock appear to have tightened credit as evidenced by tightening lending standards and reductions in lending to firms. We do not find significant evidence of a credit channel. The effect of the tightening of credit on firms’ scale seems minimal. This appears to be because firms are able to substitute to alternative financing from other banks or by drawing down pre-existing lines of credit. In terms of residential lending, the story is more subtle. Affected banks tightened credit on mortgages that they would ultimately hold in their portfolio but appear to have expanded credit for those mortgages that would predominantly be securitized. This tendency is reflected in a contemporaneous expansion in their holdings of MBS after the shock. While affected banks substantially de-risked their portfolios through adjusting their residential lending in this way, we again find that the ultimate effect on borrowers was minimal.
Stress Testing with Misspecified Models

2016-26 | With Giacomini and McKenna | September 2016

abstract (+)
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.
Robust Stress Testing

2015-13 | With McKenna | September 2015

abstract (+)
Despite the general consensus that stress testing has been useful in financial and macro-prudential regulation, test techniques are still being debated. This paper proposes using robust forecasting analysis to construct adverse scenarios using a benchmark model that includes a modified worst-case distribution. These scenarios give regulators a way to identify vulnerabilities, while acknowledging that models may be misspecified in unknown ways.
Frequency Shifting

2013-29 | September 2013

abstract (+)
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.
Doubts and Variability: A Robust Perspective on Exotic Consumption Series

2013-28 | With Smith | September 2015

abstract (+)
Consumption-based asset-pricing models have experienced success in recent years by augmenting the consumption process in ‘exotic’ ways. Two notable examples are the Long-Run Risk and rare disaster frameworks. Such models are difficult to characterize from consumption data alone. Accordingly, concerns have been raised regarding their specification. 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. Consequently, prices may be misleading in characterizing these phenomena since they encode a pessimistic perspective of the data-generating process.
Published Articles (Refereed Journals and Volumes)
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Long-Run Risk Is the Worst-Case Scenario

American Economic Review 106 (9), September 2016, 2494-2527 | With Dew-Becker

abstract (+)
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. She estimates her consumption process nonparametrically–allowing potentially infinite-order dynamics–and prices assets using a pessimistic model that minimizes lifetime utility subject to a constraint on statistical plausibility. The equilibrium is exactly solvable and the pricing model always includes long-run risks. With risk aversion of 4.7, the model matches major facts about asset prices, consumption, and dividends. The paper provides a novel link between ambiguity aversion and nonparametric estimation.
Robust Animal Spirits

Journal of Monetary Economics 59(8), December 2012, 738-750 | With Smith

abstract (+)
In a real business cycle model, an agent’s fear of model misspecification interacts with stochastic volatility to induce time varying worst case scenarios. These time varying worst case scenarios capture a notion of animal spirits where the probability distributions used to evaluate decision rules and price assets do not necessarily reflect the fundamental characteristics of the economy. Households entertain a pessimistic view of the world and their pessimism varies with the overall level of volatility in the economy, implying an amplification of the effects of volatility shocks. By using perturbation methods and Monte Carlo techniques we extend the class of models analyzed with robust control methods to include the sort of nonlinear production-based DSGE models that are popular in academic research and policymaking practice.
FRBSF Publications
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