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Session Overview
APT-1: Networks and Learning
Thursday, 24/Aug/2017:
10:30am - 12:00pm

Session Chair: Irina Zviadadze, Stockholm School of Economics
Location: O135

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Distress Propagation in Networks and Asset Pricing

Andrea Buraschi1, Claudio Tebaldi2

1Imperial College Business School; 2Bocconi University

Discussant: Bernard Herskovic (UCLA)

This paper studies asset pricing an exchange economy incorporating a network. The model allows firms to be interconnected so that seemingly firm-specific distress shocks can propagate to generate aggregate fluctuations that are priced. The first contribution of the paper is to show that a simple network dynamics may give rise to a long term critical regime characterized by cascades of shocks. Cascades are intermittent bursts of clustered distress transitions that have been documented among the empirical stylized properties of the distress propagation. In addition the model generates three testable empirical implications that are supported by the data: (i) the negative relation between stock expected returns and single firm distress probability documented by Campbell Hilsher Szilagy (2008); (ii) the positive relation between expected returns and a network based measure of firm exposure to a cascade of distress shocks; (iii) the positive relation between the size and value premia and the network distress premium.

EFA2017-1719-APT-1-Buraschi-Distress Propagation in Networks and Asset Pricing.pdf

Equilibrium Asset Pricing in Directed Networks

Nicole Branger1, Patrick Konermann2, Christoph Meinerding3, Christian Schlag4

1University of Muenster; 2BI Norwegian Business School; 3Deutsche Bundesbank; 4Goethe University Frankfurt

Discussant: Nina Boyarchenko (Federal Reserve Bank of New York)

The direction of links in cash flow networks affects the cross-section of return volatilities, market prices of risk, and Sharpe ratios. We propose a flexible and tractable model for general equilibrium asset pricing in weighted, directed networks and establish shock propagation capacity as a measure for directedness. Our model predicts: The higher the shock propagation capacity, the lower the return volatility, the higher the market price of jump risk, and the higher the Sharpe ratio. As an illustration we estimate the empirical network from industry cash flow data using variance decompositions and find support for these predictions.

EFA2017-1768-APT-1-Branger-Equilibrium Asset Pricing in Directed Networks.pdf

Asset Pricing with Learning

Daniel Andrei1, Michael Hasler2, Alexandre Jeanneret3

1UCLA; 2University of Toronto; 3HEC Montréal

Discussant: Andrey Ermolov (Fordham University)

We develop a dynamic pure-exchange economy that nests two different types of learning about future economic conditions: learning about the level of expected economic growth and learning about its degree of persistence. Learning about persistence (i.e., about long-run risk) generates strong countercyclical fluctuations in equilibrium risk premia, equity volatility, and the Sharpe ratio. In contrast, these moments remain constant when investors learn exclusively about the level of expected economic growth or in an economy with perfect information. We provide strong empirical support for the case of learning about long-run risk.

EFA2017-325-APT-1-Andrei-Asset Pricing with Learning.pdf

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