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Session 3: Asset Pricing, Macro Finance and Computation

July 15-17, 2020, 9 am-2 pm PDT
Organized by: 
  • Kenneth Judd, Hoover Institution
  • Walter Pohl, University of Groningen
  • Karl Schmedders, IMD Lausanne and University of Zurich
  • Ole Wilms, Tilburg University

This session focuses on recent advances in asset pricing and macro finance as well as the use of computational techniques in these areas. Possible topics include but are not limited to the following: investor heterogeneity, learning and ambiguity, new preference structures for pricing models, or using machine learning to understand the cross-section of returns. As the analysis of such models often requires the use of computational methods, we encourage submissions that develop and make use of new numerical techniques.

In this Session

Jul 15 | 9:00 am to 9:45 am

Feedback and Contagion Through Distressed Competition

Presented by: Winston Wei Dou (University of Pennsylvania)
Co-Author(s): Hui Chen (MIT), Hongye Guo (University of Pennsylvania) and Yan Ji (Hong Kong University of Science and Technology)

Firms tend to compete more aggressively when they are in financial distress; the intensified competition reduces the profit margins for all firms in the industry, pushing everyone further into distress. To study such feedback and contagion effects, we incorporate a supergame of strategic competition into a dynamic model of long-term defaultable debt. Depending on the relative market share and financial strength as well as entry threats, firms in the model exhibit a rich variety of strategic interactions, including predation, self-defense, and collaboration. A key result of our model is that, due to financial contagion, the credit risks of leading firms in an industry are jointly determined, whereby firm-specific shocks can significantly affect the credit risk of peer firms. In addition, the competition-distress feedback affects firms’ aggregate risk exposure, which helps explain the puzzling joint cross sectional patterns of equity and bond returns. Finally, we also provide empirical support for our model’s predictions. In particular, we exploit exogenous variations in market structure – large tariff cuts – to test the endogenous competition mechanism directly.

Jul 15 | 9:45 am to 10:30 am

Climate Change and Uncertainty: An Asset Pricing Perspective

Presented by: Michael Barnett (Arizona State University)

I document key empirical facts about the time-series and cross-sectional effects of climate change and climate model uncertainty on production and asset prices, and construct a model that highlights the mechanisms needed to replicate these facts. Empirically, I find that higher temperatures increase green energy use, whereas economic downturns increase fossil fuel reliance. I estimate a negative climate risk price that is magnified by rising temperatures. Negatively (positively) exposed firms display higher (lower) returns as climate change intensifies. These empirical results are most significant during the last 20 years. Using a production-based model where the consumption good is produced using a mix of cheap, polluting oil and expensive, clean green energy, I show the importance of non-linear climate impacts and agents with recursive preferences and aversion to climate model uncertainty for replicating these facts. I use the calibrated model to show how alternative climate scenarios will impact future outcomes.

Jul 15 | 10:30 am to 10:45 am


Jul 15 | 10:45 am to 11:30 am

The Global Factor Structure of Exchange Rates

Presented by: Andrea Vedolin (Boston University)
Co-Author(s): Sofonias Korsaye (University of Geneva) and Fabio Trojani (University of Geneva)

We provide a model-free framework to study the global factor structure of exchange rates. To this end, we propose a new methodology to estimate model free global stochastic discount factors (SDFs) pricing large cross-sections of international assets, such as stocks, bonds, and currencies, independently of the currency denomination and in the presence of trading frictions. We derive a unique mapping between the optimal portfolios of global investors trading in international markets with frictions and international SDFs, which allows us to recover global SDFs from asset return data alone. Trading frictions shrink portfolio weights of some assets to zero, leading to endogenously segmented markets and robust properties of international SDFs. From the cross-section of num´eraire invariant SDFs, we extract global exchange rate factors and show that they are strongly related to dollar and carry. Using these factors, we obtain an excellent in- and out-of-sample fit of up to 80% for the cross-section of international asset returns, significantly improving upon the performance of benchmark factor models. Finally, we estimate the cost to obtain the portfolio home bias observed in the data and find it to be small.

Jul 15 | 11:30 am to 12:15 pm

Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models

Presented by: Svetlana Bryzgalova (London Business School)
Co-Author(s): Jiantao Huang (London School of Economics) and Christian Julliard (London School of Economics)

We propose a novel, and simple, Bayesian estimation and model selection procedure for crosssectional asset pricing. Our approach, that allows for both tradable and non-tradable factors, and is applicable to high dimensional cases, has several desirable properties. First, weak and spurious factors lead to diffuse, and centered at zero, posteriors for their market price of risk, making such factors easily detectable. Second, posterior inference is robust to the presence of such factors. Third, we show that flat priors for risk premia lead to improper marginal likelihoods, rendering model selection invalid. Therefore, we provide a novel prior, that is diffuse for strong factors but shrinks away useless ones, under which posterior probabilities are well behaved, and can be used for factor and (non necessarily nested) model selection, as well as model averaging, in large scale problems. We apply our method to a very large set of factors proposed in the literature, and analyse 2.25 quadrillion possible models, gaining novel insights on the empirical drivers of asset returns.

Jul 15 | 12:15 pm to 12:30 pm


Jul 15 | 12:30 pm to 1:15 pm

Dissecting Mechanisms of Financial Crises: Intermediation and Sentiment

Presented by: Wenhao Li (USC)
Co-Author(s): Arvind Krishnamurthy (Stanford GSB)

We develop a model of nancial crises with both a nancial amplication mechanism, via frictional intermediation, and a role for sentiment, via time varying beliefs about a crash state. We confront the model with data on credit spreads, equity prices, credit, and output across the nancial crisis cycle. In particular, we ask the model to match data on the frothy pre-crisis behavior of asset markets and credit, the sharp transition to a crisis where asset values fall, disintermediation occurs and output falls, and the post-crisis period characterized by a slow recovery in output. We find that a pure amplication mechanism quantitatively matches the crisis and aftermath period but fails to match the pre-crisis evidence. Mixing sentiment and amplication allows the model to additionally match the pre-crisis evidence. We consider two versions of sentiment, a Bayesian belief updating process and one that overweighs recent observations. We find that both models match the crisis patterns qualitatively, generating froth pre-crisis, non-linear behavior in the crisis, and slow recovery. The non-Bayesian model improves quantitatively on the Bayesian model in matching the extent of the pre-crisis froth.

Jul 16 | 9:00 am to 9:45 am

Belief Distortions and Macroeconomic Fluctuations

Presented by: Francesco Bianchi (Duke & NBER)
Co-Author(s): Sydney Ludvigsony (NYU & NBER) and Sai Ma (Federal Reserve Board)

This paper combines a data rich environment with a machine learning algorithm to provide estimates of time-varying systematic expectational errors embedded in survey responses, a measure of belief distortions. Our evidence suggests that such distortions are sometimes large even for professional forecasters. Survey respondents almost always place too much weight on their own forecasts relative to other information. Estimates imply that consensus survey fore- casts for both inflation and GDP growth oscillate between optimism and pessimism and range over time from 50 to 400% of average annual inflation or GDP growth. To investigate their dynamic relation with the macroeconomy, we construct indexes of aggregate (across surveys and respondents) biases in forecasts of GDP growth and inflation. A positive innovation in a GDP growth biases (indicating over prediction of growth) is associated with an increase in aggregate economic activity, while a positive innovation in a inflation biases (indicating over prediction of inflation) is associated a decline in activity. Our estimates provide a benchmark to evaluate theories for which information capacity constraints, extrapolation, sentiments, ambiguity aversion, and other departures from full information rational expectations play a role in business cycles.

Jul 16 | 9:45 am to 10:30 am

Knight Meets Sharpe: Capital Asset Pricing under Ambiguity

Presented by: Yehuda Izhakian (Baruch College)

This paper extends the mean-variance preferences to mean-variance-ambiguity preferences by relaxing the assumption that probabilities are known, and instead assuming that probabilities are uncertain. The optimal portfolio is identified in general equilibrium, demonstrating that the twofund separation theorem is preserved. Thereby, introducing ambiguity into the capital asset pricing model demonstrates that the ambiguity premium corresponds to systematic ambiguity, which is distinguished from the systematic risk. Using the closed-form measurable beta ambiguity, performance measures are generalized to account for ambiguity alongside risk. Use of this model can be extended to other applications including investment decisions and valuations. The introduced asset pricing model is empirically applicable and provides insight into empirical asset pricing anomalies.

Jul 16 | 10:30 am to 10:45 am


Jul 16 | 10:45 am to 11:30 am

The Pre-FOMC Announcement Drift and Private Information: Kyle Meets Macro-Finance

Presented by: Chao Ying (University of Minnesota)

This paper proposes and tests the private information explanation to account for the pre-FOMC announcement drift. During the same window as the pre-FOMC drift, the market uncertainty (measured by the VIX index) decreases significantly and systematically. To understand the above features of the financial markets, I integrate Kyle’s (1985) model into a standard consumption-based asset pricing framework where the market makers are compensated for the risk of holding assets. Observing aggregate order flow, they update the beliefs about the marginal utility-weighted asset value, which resolves uncertainty gradually and results in an upward drift in market prices. I demonstrate that there is a strictly positive pre-FOMC drift if and only if the market makers require risk compensation.

Jul 16 | 11:30 am to 12:15 pm

Counterparty Risk: Implications for Network Linkages and Asset Prices

Presented by: Gill Segal (University of North Carolina)
Co-Author(s): Fotis Grigoris (University of North Carolina) and Yunzhi Hu (University of North Carolina)

This paper studies the relation between trade credit, risk, and the dynamics of production network linkages. We find that firms that extend more trade credit earn 7% p.a. lower risk premia, and maintain longer relationships with their customers. We also document that suppliers with longerduration links to their customers command lower expected returns. We quantitatively explain these facts using a production-based model. Trade credit helps to hedge customers against liquidity risks, thereby reducing suppliers’ exposures to costs incurred in finding new customers. Overall, trade credit is informative about the lifespan of supplier-customer links, the production network’s density, and macroeconomic risk.

Jul 16 | 12:15 pm to 12:30 pm


Jul 16 | 12:30 pm to 1:15 pm

Prospect Theory and Stock MARKET Anomalies

Presented by: Lawrence Jin (Caltech)
Co-Author(s): Nicholas Barberis (Yale University) and Baolian Wang (University of Florida)

We present a new model of asset prices in which investors evaluate risk according to prospect theory and examine its ability to explain 22 prominent stock market anomalies. The model incorporates all the elements of prospect theory, takes account of investors’ prior gains and losses, and makes quantitative predictions about an asset’s average return based on empirical estimates of its volatility, skewness, and past capital gain. We find that the model is helpful for thinking about a majority of the 22 anomalies.

Jul 16 | 1:15 pm to 2:00 pm

Non-Parametric Distributions, Uncertainty, and Asset Prices

Presented by: Viktor Tsyrennikov (Promontory)
Co-Author(s): Olena Rarytska (Cornell University) and Heidi Verheggen (University of Pennsylvania)

This paper studies asset pricing under non-parametric dividend distributions and Hansen and Sargent’s robust preferences, and it shows how to perform asset-pricing calculation efficiently in this case.

If the dividend process is non-normal, robust agents right-skew the whole distribution and this helps improve the asset pricing implications materially. At the same time, a given belief distortion is easier to detect when the actual distribution is not gaussian. Hence, there is little difference between using a normal and non-parametric distribution for any given level of distortion detectability. That is, asset pricing is mostly unaffected by assumptions about the underlying distribution of dividends. However, there are implications for stress-testing: skewness and kurtosis take precedence over mean stressing.

Jul 17 | 9:00 am to 9:45 am

The Risks of Safe Assets

Presented by: Yang Liu (University of Hong Kong)
Co-Author(s): Lukas Schmid (University of Southern California) and Amir Yaron (University of Pennsylvania & NBER)

US government bonds exhibit characteristics often attributed to safe assets. A long literature documents signicant convenience yields in scarce US Treasuries, suggesting that rising Treasury supply and government debt comes with a declining liquidity premium. We empirically document and theoretically identify a novel fiscal cost through a dual role for government debt. Through a liquidity channel an increase in government debt improves liquidity and lowers liquidity premia by facilitating debt rollover, thereby reducing credit spreads. Through an uncertainty channel, rising government debt creates policy uncertainty, raising default risk premia. We interpret and quantitatively evaluate these two channels through the lens of a general equilibrium asset pricing model with liquidity and credit risk with a rich scal sector. The calibrated model generates quantitatively realistic liquidity spreads and default risk premia, and suggests that rising government debt reduces liquidity premia, but crowds out corporate debt nancing and investment, and creates endogenous tax volatility, reflected in higher credit spreads, risk premia, and consumption volatility. Our model implies that these effects are exacerbated in times of scal stress. Therefore, increasing safe asset supply can be risky, and come at a signicant fiscal cost.

Jul 17 | 9:45 am to 10:30 am

Disagreement, Information Quality and Asset Prices

Presented by: Fernando Zapatero (Boston University)
Co-Author(s): Costas Xiouros (BI Norwegian Business School)

We solve analytically a pure exchange general equilibrium model with a continuum of agents that agree to disagree on how they interpret information. Disagreement fluctuates with information quality and the disagreement model is estimated using data on professional forecasts. We nd that fluctuations in information quality generate about half the stock price volatility in the data, help explain the equity premium, and explain empirical relations between the forecast dispersion and asset prices. Constant information quality cannot account for the variation in forecast dispersion and in this case, disagreement has almost no eect on the stock return volatility.

Jul 17 | 10:30 am to 10:45 am


Jul 17 | 10:45 am to 11:30 am

A Q-Theory of Inequality

Presented by: Emilien Gouin-Bonenfant (Columbia University)
Co-Author(s): Matthieu Gomez (Columbia University)

We study the effect of interest rates on top wealth inequality. While lower rates decrease the average growth rate of existing fortunes, they increase the growth rate of new fortunes by making it cheaper to raise capital. We develop a sufficient statistic approach to express the effect of interest rates on the Pareto exponent of the wealth distribution: it depends on the average equity issuance and leverage of individuals reaching the top. Quantitatively, we find that the secular decline in real interest rates has been a major contributor to the rise in top wealth inequality in the U.S.

Jul 17 | 11:30 am to 12:15 pm

Government Debt and Bank Leverage Cycle: An Analysis of Public and Intermediated Liquidity

Presented by: Ye Li (Ohio State University)

Financial intermediaries issue the majority of liquid securities, and nonfinancial firms have become net savers, holding intermediaries’ debt as cash. This paper shows that intermediaries’ liquidity creation stimulates growth – firms hold their debt for unhedgeable investment needs – but also breeds instability through procyclical intermediary leverage. Introducing government debt as a competing source of liquidity is a double-edged sword: firms hold more liquidity in every state of the world, but by squeezing intermediaries’ profits and amplifying their leverage cycle, public liquidity increases the frequency and duration of intermediation crises, raising the likelihood of states with less liquidity supplied by intermediaries. The latter force dominates and the overall impact of public liquidity is negative, when public liquidity cannot satiate firms’ liquidity demand and intermediaries are still needed as the marginal liquidity suppliers.

Jul 17 | 12:15 pm to 12:30 pm


Jul 17 | 12:30 pm to 1:15 pm

SimFinMkts: A Possible Tool for Financial Markets Research

Presented by: Kenneth Judd (Hoover Institution)