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Working Paper
Uncertainty and Growth Disasters
This paper documents several stylized facts on the real effects of economic uncertainty. First, higher uncertainty is associated with a more dispersed and negatively skewed distribution of output growth. Second, the response of economic growth to an increase in uncertainty is highly nonlinear and asymmetric. Third, higher asset volatility magnifies the negative impact of uncertainty on growth. We develop and estimate an analytically tractable model in which rapid adoption of new technology may raise economic uncertainty which causes measured productivity to decline. The equilibrium growth ...
Working Paper
Spectral backtests unbounded and folded
In the spectral backtesting framework of Gordy and McNeil (JBF, 2020) a probability measure on the unit interval is used to weight the quantiles of greatest interest in the validation of forecast models using probability-integral transform (PIT) data. We extend this framework to allow general Lebesgue-Stieltjes kernel measures with unbounded distribution functions, which brings powerful new tests based on truncated location-scale families into the spectral class. Moreover, by considering uniform distribution preserving transformations of PIT values the test framework is generalized to allow ...
Working Paper
The Relationship between Market Depth and Liquidity Fragility in the Treasury Market
Analysis of market liquidity often focuses on measures of the current cost of trading. However, investors and policy-makers also care about what would happen to liquidity in the event of an adverse shock. If liquidity were to deteriorate rapidly at times when investors were seeking to rebalance portfolios, this could amplify the effects of shocks to the financial system even if liquidity is high most of the time. We examine the potential for such fragility of liquidity in the Treasury market. We show that a reduction in the availability of resting orders to trade ("market depth") increases ...
Working Paper
Capital Controls and the Global Financial Cycle
Capital flows into emerging markets are volatile and associated with risks. A common prescription is to impose counter-cyclical capital controls that tighten during economic booms to mitigate future sudden-stop dynamics, but it has been challenging to document such patterns in the data. Instead, we show that emerging markets tighten their capital controls in response to volatility in international financial markets and elevated risk aversion. We develop a model in which this behavior arises from a desire to manipulate the risk premium. When investors are more risk-averse or markets are ...
Working Paper
When do low-frequency measures really measure transaction costs?
We compare popular measures of transaction costs based on daily data with their high-frequency data-based counterparts. We find that for U.S. equities and major foreign exchange rates, (i) the measures based on daily data are highly upward biased and imprecise; (ii) the bias is a function of volatility; and (iii) it is primarily volatility that drives the dynamics of these liquidity proxies both in the cross section as well as over time. We corroborate our results in carefully designed simulations and show that such distortions arise when the true transaction costs are small relative to ...
Working Paper
Taxonomy of Global Risk, Uncertainty, and Volatility Measures
A large number of measures for monitoring risk and uncertainty surrounding macroeconomic and financial outcomes have been proposed in the literature, and these measures are frequently used by market participants, policy makers, and researchers in their analyses. However, risk and uncertainty measures differ across multiple dimensions, including the method of calculation, the underlying outcome (that is, the asset price or macroeconomic variable), and the horizon at which they are calculated. Therefore, in this paper, we review the literature on global risk, uncertainty, and volatility ...
Working Paper
Spectral Backtests of Forecast Distributions with Application to Risk Management
We study a class of backtests for forecast distributions in which the test statistic is a spectral transformation that weights exceedance events by a function of the modeled probability level. The choice of the kernel function makes explicit the user's priorities for model performance. The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and propose novel variants as well. In an empirical application, we backtest forecast distributions for the overnight ...