Session 13: New Frontiers in Asset Pricing

Date
Wed, Sep 6 2023, 9:00am - Fri, Sep 8 2023, 5:00pm PDT
Location
Herbert Hoover Memorial Building, Annenberg Conference Room, Room 160, 434 Galvez Mall, Stanford, CA 94305
Organized by
  • Kenneth Judd, Hoover Institution at Stanford University
  • Walter Pohl, Norwegian School of Economics
  • Karl Schmedders, IMD Lausanne
  • Ole Wilms, University of Hamburg & Tilburg University

This session is for asset pricing papers on the frontier of the discipline. Particular areas of focus are macrofinance, computation, machine learning, and climate finance. Possible topics include but are not limited to the following: asset pricing, investor heterogeneity, learning and ambiguity, new preference structures for pricing models, or using machine learning to understand the cross-section of returns. A particular area of interest is climate finance, where both climate change and the policy responses to climate change present new risks in asset pricing markets.  Topics of interest include asset pricing with heterogeneous agents and disaster risks, credit risk modeling for possibly stranded assets, the implications of integrated assessment models for financial risks, and methodological advances in solution methods for complex analyses of climate finance models. 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

Wednesday, September 6, 2023

Sep 6

8:30 am - 9:00 am PDT

Check-in & Breakfast

Sep 6

9:00 am - 9:45 am PDT

Who values democracy?

Presented by: Max Miller (Harvard University)

I show democratizations have a large, negative impact on asset valuations driven by a rise in redistribution risk. Across 90 countries over 200 years, risk premia are substantially elevated in democratizations, similar in magnitude to financial crises. Using a shift in Catholic church doctrine in support of democracy, I provide causal evidence that democratizations increase risk premia. Successful democratizations lead to substantial redistribution: the size of the public sector grows, income inequality falls, and the labor share of income rises. A model of asset prices and political regimes in which wealthy asset market participants face redistribution risk in democratizations can quantitatively explain these effects. The model also explains the negligible asset pricing response to autocratizations. Neither an increase in macroeconomic risk nor generic political risk can explain the results.

Sep 6

9:45 am - 10:00 am PDT

Break

Sep 6

10:00 am - 10:45 am PDT

Robust Stock Index Return Predictions Using Deep Learning

Presented by: Andreas Neuhierl (Washington University in St. Louis)
Co-author(s): Ravi Jagannathan (Northwestern University) and Yuan Liao (Rutgers University)

We introduce a conditional machine learning approach utilizing deep neural networks to forecast stock index returns. We aim to address the well-documented instability in predicting aggregate stock returns. To evaluate the consistency and robustness of our predictions, we consider the out-of-sample R2 (OSSR2) as a time series and conduct structural break tests and derive a model-free decomposition of the R2 process. Our forecasting model exhibits a higher mean OSSR2 and fewer breaks compared to popular models in the literature. We develop an asymptotic distribution theory to assess the uncertainty associated with our neural network predictions.

Sep 6

10:45 am - 11:00 am PDT

Break

Sep 6

11:00 am - 11:45 am PDT

Discrete Price, Discrete Quantity, and the Optimal Nominal Price of a Stock

Presented by: Mao Ye (Cornell University)
Co-author(s): Sida Li (Brandeis University)

Economists commonly assume that price and quantity are continuous variables, while in reality both are discrete. As U.S. regulations mandate a one-cent minimum tick size and a 100-share minimum lot size, we predict that less volatile and more active stocks will choose higher prices to make pricing more continuous and quantity more discrete. Despite heterogeneous optimal prices, all firms achieve their optimal prices when their bid–ask spreads equal two ticks, i.e., when frictions from discrete pricing equal those from discrete lots. Empirically, our theoretical model explains 57% of cross-sectional variations in stock prices and 81% of cross-sectional variations in bid–ask spreads. The adjustment toward optimal prices rationalizes 91% of stock splits and contributes 94 bps to split announcement returns. The median U.S. stock value would increase by 106 bps, and the total U.S. market capitalization would increase by $93.7 billion if all firms chose their optimal price.

Sep 6

11:45 am - 1:15 pm PDT

Lunch

Sep 6

1:15 pm - 2:00 pm PDT

Pollution Abatement Investment under Financial Frictions and Policy Uncertainty

Presented by: Chi-Yang Tsou (University of Manchester)
Co-author(s): Min Fang (University of Florida) and Po-Hsuan Hsu (National Tsing Hua University)

This paper examines how financial frictions and policy uncertainty jointly influence firms’ investments in pollution abatement. Our data analyses suggest that financially constrained firms are less likely to invest in pollution abatement and are more likely to release toxic pollutants, with this pattern intensified by policy uncertainty surrounding future environmental regulations, as measured by "close" gubernatorial elections or uncertainty revealed in firms' earnings conference calls. We then develop a general equilibrium model with heterogeneous firms, including both financially constrained and unconstrained firms, in which financially constrained firms face increased marginal costs of finance from pollution abatement. These costs are further amplified by policy uncertainty, reducing firms' incentives to prevent pollution. Therefore, the aggregate effect of environmental policies depends on the distribution of financial frictions and policy uncertainty.

Sep 6

2:00 pm - 2:15 pm PDT

Break

Sep 6

2:15 pm - 3:00 pm PDT

Can the Fed Control Inflation? Stock Market Implications

Presented by: Daniel Andrei (McGill University)
Co-author(s): Michael Hassler (University of Texas at Dallas)

This paper examines the stock market implications of uncertainty about the Fed’s inflation-curbing efforts. We develop a general equilibrium model where investors learn about the Fed’s ability to control inflation. Uncertainty about this ability heightens stock market volatility and the risk premium, particularly during pronounced monetary tightening and easing cycles. This effect is stronger during tightening when investors’ learning amplifies stock reactions to inflation surprises. Moreover, when the Fed loses its inflation control credibility, investors view inflation as more persistent, further increasing the volatility and risk premium. Empirical tests support our model’s predictions, highlighting the importance of investor learning about the Fed’s ability to control inflation in shaping financial markets.

Sep 6

3:00 pm - 3:15 pm PDT

Break

Sep 6

3:15 pm - 4:00 pm PDT

Macroeconomic Drivers and the Pricing of Uncertainty, Inflation, and Bonds

Presented by: Thomas M. Mertens (Federal Reserve Bank of San Francisco)
Co-author(s): Brandyn Bok (Federal Reserve Bank of San Francisco )and John C. Williams (Federal Reserve Bank of New York(

The correlation between uncertainty shocks, as measured by changes in the VIX, and changes in breakeven inflation rates declined and turned negative after the Great Recession. This estimated time-varying correlation is shown to be consistent with the predictions of a standard New Keynesian model with a lower bound on interest rates and a trend decline in the natural rate of interest. In one equilibrium of the model, higher uncertainty raises the probability of large shocks that leave the central bank constrained by the lower bound and unable to offset negative shocks, resulting in inflation shortfalls and lower average inflation rates

Thursday, September 7, 2023

Sep 7

8:30 am - 9:00 am PDT

Check-In & Breakfast

Sep 7

9:00 am - 9:45 am PDT

Intermediary Balance Sheets and the Treasury Yield Curve

Presented by: Wenhao Li (University of Southern California)
Co-author(s): Wenxin Du (University of Chicago) and Benjamin Hébert (Stanford University)

We document a regime change in the U.S. Treasury market post-Global Financial Crisis (GFC): dealers switched from net short to net long Treasury bonds. Consistent with this change, we derive “net-long” and “net-short” Treasury curves that account for dealers’ balance sheet costs, and show that actual Treasury yields moved from the net short curve pre-GFC to the net long curve post-GFC. This regime change helps explain negative swap spreads post-GFC and the co-movement among swap spreads, dealer Treasury positions, yield curve slope, and covered interest-parity violations, and implies changing effects for a wide range of monetary and regulatory policy interventions.

Sep 7

9:45 am - 10:00 am PDT

Break

Sep 7

10:00 am - 10:45 am PDT

Climate Defaults and Financial Adaptation

Presented by: Toan Phan (Federal Reserve Bank of Richmond)
Co-author(s): Felipe Schwartzman (Federal Reserve Bank of Richmond)

We analyze the relationship between climate-related disasters and sovereign debt crises using a model with capital accumulation, sovereign default, and disaster risk. We find that disaster risk and default risk together lead to slow post-disaster recovery and heightened borrowing costs. Calibrating the model to Mexico, we find that the increase in cyclone risk due to climate change leads to a welfare loss equivalent to a permanent 1% consumption drop. However, financial adaptation via catastrophe bonds and disaster insurance can reduce these losses by about 25%. Our study highlights the importance of financial frictions in analyzing climate change impacts.

Sep 7

10:45 am - 11:00 am PDT

Break

Sep 7

11:00 am - 11:45 am PDT

Investment under Up- and Downstream Uncertainty

Presented by: Fotis Grigoris (Indiana University)
Co-author(s): Gill Segal (University of North Carolina at Chapel Hill)

We study the transmission of uncertainty shocks in production networks and find that their impact on economic activity depends on their source in supply chains. A real-option frame-work with time-to-build predicts that only upstream uncertainty suppresses investment, since upstream (downstream) uncertainty from suppliers (customers) affects the shorter-run (longer-run). Consistently, production-network data show that upstream uncertainty propagates down-stream, affecting firm-level outcomes negatively. Conversely, downstream uncertainty propagates upstream more weakly but affects firm-level outcomes positively. At the macro-level, these two uncertainties oppositely predict aggregate growth and asset prices. Overall, downstream uncertainty has an expansionary effect, in contrast to other facets of uncertainty.

Sep 7

11:45 am - 1:15 pm PDT

Lunch

Sep 7

1:15 pm - 2:00 pm PDT

A Probabilistic Solution to High-Dimensional Continuous-Time Macro and Finance Models

Presented by: Ji Huang (The Chinese University of Hong Kong)

This paper introduces the probabilistic formulation of continuous-time economic models: forward stochastic differential equations (SDE) govern the dynamics of backward-looking variables, and backward SDEs capture that of forward-looking variables. Deep learning streamlines the search for the probabilistic solution, which is less sensitive to the “curse of dimensionality.” The paper proposes a straightforward algorithm and assesses its accuracy by considering a multiple-country model with an explicit solution under symmetric states. Combining with the finite volume method, the algorithm can obtain global dynamics of heterogeneous-agent models with aggregate shocks, in which agents consider the distribution of individual states as a state variable.

Sep 7

2:00 pm - 2:15 pm PDT

Break

Sep 7

2:15 pm - 3:00 pm PDT

Information-Driven Volatility

Presented by: Leyla Jianyu Han (Boston University)
Co-author(s): Hengjie Ai (University of Wisconsin-Madison) and Lai Xu (Syracuse University)

Standard asset pricing models with stochastic volatility predict a robust positive relationship between past realized volatility and future expected returns. Empirical work typically finds this relationship to be negative. We develop an asset pricing model where stock market volatility dynamics are driven by information. We show that under strong generalized risk sensitivity of preferences, information-driven volatility induces a negative correlation between past realized volatility and future expected returns. We provide empirical evidence for the unique implications of the information-driven volatility channel and demonstrate that our model can quantitatively replicate the evidence.

Sep 7

3:00 pm - 3:15 pm PDT

Break

Sep 7

3:15 pm - 4:00 pm PDT

The Cost of Intermediary Market Power for Distressed Borrowers

Presented by: Wenyu Wang (Indiana University)
Co-author(s): Winston Wei Dou (University of Pennsylvania) and Wei Wang (Queen’s University)

In the loan markets for distressed corporate borrowers, a few specialized lenders finance a large fraction of loans. Ultra-high yield spreads prevail even after removing the credit-and liquidity-risk component. Borrowers are in desperate need of financing but face limited funding options, while specialized lenders have repeated syndication relations with re-strained participation. We develop and estimate a dynamic game-theoretic model, accounting for strategic competition, endogenous collusion capacity, endogenous participation, and latent heterogeneity. Lender market power accounts for 74 - 92% of the risk-adjusted yield spreads, with a significant fraction attributable to collusion. Smaller borrowers are more susceptible to lender market power. Importantly, both specialized lenders and distressed borrowers would be worse off if collusion is completely prohibited, suggesting that vigorous antitrust policies can be efficiency retarding.

Friday, September 8, 2023

Sep 8

8:30 am - 9:00 am PDT

Check-In & Breakfast

Sep 8

9:00 am - 9:45 am PDT

Green Finance and Inequality

Presented by: Lea Felicitas Tschan (University of St. Gallen)
Co-author(s): Ola Mahmoud (University of St. Gallen)

This paper provides empirical evidence for a significant positive association between green finance and income inequality from a panel of 87 countries from 2004 to 2020. This relationship is strongest for countries with initially lower levels of income, low levels of financial development, and low levels of carbon emissions. We contrast green investments to general investments and find that the main contributor to the observed increase in inequality is green finance. We also find evidence that the effect on inequality persists for four years and thereafter abates. We argue that the association between green finance and inequality is at least partially driven by two mechanisms: technological change and investment emissions. Using a moderated mediation design, we show that technological change and investment emissions are partially mediating the relationship between green finance and top income inequality.

Sep 8

9:45 am - 10:00 am PDT

Break

Sep 8

10:00 am - 10:45 am PDT

Markup Shocks and Asset Prices

Presented by: Jincheng Tong (University of Toronto)
Co-author(s): Jun E. Li (University of Warwick) and Alexandre Corhay (University of Toronto)

We explore the asset pricing implications of shocks that allow firms to extract more rents from consumers. These markup shocks directly impact the representative household’s marginal utility and the firms’ cash flow. Using firm-level data, we construct a measure of aggregate markup shocks and show that the price of markup risk is negative, that is, a positive markup shock is associated with high marginal utility states. Markup shocks generate differences in risk premia due to their heterogeneous impact on firms. Firms with larger exposures to markup shocks are less risky and have lower expected returns. We rationalize these findings in a general equilibrium model with markup shocks.

Sep 8

10:45 am - 11:00 am PDT

Break

Sep 8

11:00 am - 11:45 am PDT

Wealth Dynamics and Asset Prices with Heterogeneous Beliefs under Smooth Ambiguity

Presented by: Hening Liu (University of Manchester)
Co-author(s): Bo Huang (University of Manchester)

We study a class of heterogeneous-agent endowment economies with long-run risks in which the persistent component in the aggregate consumption growth process is unobservable. Agents holding different beliefs regarding the persistent component engage in Bayesian learning. Agents are averse to uncertainty arising from state estimation and have generalized recursive smooth ambiguity preferences. By examining a two-agent model with an elasticity of intertemporal substitution above 1 and a reasonable risk aversion, we find that: 1) Under recursive preferences without ambiguity aversion, the consumption share of the agent with the correct belief dominates in the long run. 2) Smooth ambiguity, in conjunction with state uncertainty, generates an uncertainty sharing motive that leads to the long-run survival of both agents. 3)The time-varying welfare weights of agents and their posterior beliefs well explain the time variation of price-dividend ratios in the data. We also examine the long-run survival outcomes for alternative values of preference parameters as well as the case in which agents differ in their degrees of ambiguity aversion.

Sep 8

11:45 am - 1:15 pm PDT

Lunch

Sep 8

1:15 pm - 2:00 pm PDT

The Statistical Limit of Arbitrage

Presented by: Dacheng Xiu (University of Chicago)
Co-author(s): Rui Da (University of Chicago) and Stefan Nagel (University of Chicago)

When alphas are weak and rare, and arbitrageurs have to learn about alphas from historical data, there is a gap between the Sharpe ratio that is feasible for them to achieve and the infeasible Sharpe ratio that could be obtained with perfect knowledge of parameters in the return generating process. This statistical limit to arbitrage widens the bounds within which alphas can survive in equilibrium relative to the arbitrage pricing theory (APT) in which arbitrageurs are endowed with perfect knowledge. We derive the optimal Sharpe ratio achievable by any feasible arbitrage strategy and illustrate in a simple model how this Sharpe ratio varies with the strength and sparsity of alpha signals, which characterize the difficulty of arbitrageurs’ learning problem. Furthermore, we design an “all-weather” arbitrage strategy that achieves this optimal Sharpe ratio regardless of the conditions of alpha signals. Our empirical analysis of equity returns shows that this optimal strategy, along with other feasible strategies based on multiple-testing, LASSO, and Ridge methods, achieve a moderately low Sharpe ratio out of sample, in spite of a considerably higher infeasible Sharpe ratio. This is consistent with the absence of feasible near-arbitrage opportunities and the relevance of statistical limits to arbitrage.

Sep 8

2:00 pm - 2:15 pm PDT

Break

Sep 8

2:15 pm - 3:00 pm PDT

Consumption Activism, Demand Elasticity, and the Green Premium

Presented by: Lorenzo Garlappi (University of British Columbia)
Co-author(s): Xuhui Chen (University of British Columbia) and Ali Lazrak (University of British Columbia)

We examine the impact of responsible consumption on asset prices. We introduce a preference bias in favor of “green” goods and good-specific external habits in a consumption-based dynamic asset pricing model with a variety of goods. We show that when goods’ demand elasticity is high, brown assets hedge against consumption risk while green assets are riskier. As a result, the “green minus brown” (GMB) asset return spread increases with goods’ demand elasticity. Using change in product prices as a measure of goods’ demand elasticity and ESG scores as a measure of greenness, we find that in the cross-section of US stocks over the 2012–2022 period, the annual GMB return spread is 12% for firms with high demand elasticity, while it is 0.77% for firms with low demand elasticity. Moreover, consistent with our model, we find that the outperformance of green vs. brown stocks in the recent decade is concentrated among firms facing high-demand elasticity in product markets. In contrast, green firms underperform brown in low-demand elasticity markets. Our model highlights that goods’ demand elasticity is crucial for understanding the impact of the rising trend of responsible consumption on asset prices.