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APE-2: Time-varying liquidity and risk premia
The TIPS Liquidity Premium
1Aarhus University, Denmark; 2Federal Reserve Bank of San Francisco; 3Amazon
We introduce an arbitrage-free term structure model of nominal and real yields that accounts for liquidity risk in Treasury inﬂation-protected securities (TIPS). The novel feature of our model is to identify liquidity risk from individual TIPS prices by accounting for the tendency that TIPS, like most ﬁxed-income securities, go into buy-and-hold investors’ portfolios as time passes. We ﬁnd a sizable and countercyclical TIPS liquidity premium, which greatly helps our model in matching TIPS prices. Accounting for liquidity risk also improves the model’s ability to forecast inﬂation and match surveys of inﬂation expectations, although none of these series are included in the estimation.
The Time Variation in Risk Appetite and Uncertainty
1Columbia Business School, United States of America; 2Federal Reserve Board, United States of America; 3Boston College, Carroll School of Management, United States of America
We develop measures of time-varying risk aversion and economic uncertainty that are calculated from financial variables at high frequencies. We formulate a dynamic no-arbitrage asset pricing model for equities and corporate bonds. The joint dynamics among asset-specific cash flows, macroeconomic fundamentals and risk aversion feature heteroskedasticity and non-Gaussianity. Variance risk premiums on equity are very informative about risk aversion, whereas credit spreads and corporate bond volatility are highly correlated with economic uncertainty. Model-implied risk premiums outperform standard instruments for predicting excess returns on equity and corporate bonds. A financial proxy to our economic uncertainty predicts output growth significantly negatively.
Can unpredictable risk exposure be priced?
Tilburg University, Netherlands, The
We study the link between beta predictability and the price of risk. An investor who desires exposure to a certain risk factor needs to predict what next period’s beta will be. We use a simple model to show that an ambiguity averse agent’s demand is lower when betas are hard to predict, leading to a reduction in risk premiums. We test the implications for downside betas and VIX betas. We ﬁnd that they have economically and statistically small prices of risk once we account for the fact that an investor cannot observe ex-post realized betas when determining asset demand.
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Conference: EFA 2019
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