SI 2020 Asset Pricing

The market value of outstanding federal government debt in the U.S. exceeds the expected present discounted value of current and future primary surpluses by a multiple of U.S. GDP. When the pricing kernel fits U.S. equity and Treasury prices and the government surpluses are consistent with U.S. post-war data, a government debt valuation puzzle emerges. Since tax revenues are pro-cyclical while government spending is counter-cyclical, the tax revenue claim has a higher short-run discount rate and a lower value than the spending claim. Since revenue and spending are co-integrated with GDP, the long-run risk discount rates of both claims are much higher than the long Treasury yield. These forces imply a negative present value of U.S. government surpluses. Convenience yields for Treasurys are much larger than previously thought and/or U.S. Treasury markets have failed to enforce the no-bubble condition.

The Fed and the market often disagree about the path of interest rates. We develop a model that explains this disagreement and study its implications for monetary policy and asset prices. Our key assumption is that the Fed and the market disagree about expected aggregate demand. Moreover, agents learn from data but not from each other---they are opinionated and information is fully symmetric. Our main result shows that disagreements about future demand, together with learning, translate into disagreements about future interest rates. Our model further reveals that these disagreements shape optimal monetary policy, especially when they are entrenched. The market perceives monetary policy "mistakes" and the Fed partially accommodates the market's view to mitigate the financial market fallout from perceived "mistakes." We also show that differences in the speed at which the Fed and the market lean from the data---heterogeneous data sensitivity---matters for asset prices and interest rates. With heterogeneous data sensitivity, every macroeconomic shock has an embedded monetary policy "mistake" shock. When the Fed is more (less) data sensitive, the anticipation of these mistakes dampen (amplify) the impact of macroeconomic shocks on asset prices.

We analyze 29,000 entries in Federal Reserve governors’ calendars from 2007-2018 to understand how information flows from the Federal Reserve Board to stock markets. By studying which of 47 types of counterparties are more likely to be on governor calendars in crucial times for policy (days with high values of VIX), we document that interactions with Federal Reserve Bank presidents and the FOMC are viewed as important by governors. Consistent with this, we show that communication between Federal Reserve governors (the chair, vice-chair or other governors) and Federal Reserve Bank presidents are a central driver of the high stock returns in even weeks in FOMC cycle time documented by Cieslak, Morse, and Vissing-Jorgensen (2019). This result holds even after controlling for formal information releases and speeches. Of all the possible counterparties, it is the interactions of the Federal Reserve governors with their own insiders – Federal Reserve Bank presidents – that most strongly predict informal communications with markets. Since the times of governor-president interactions are not publicly known ahead of time, the results furthermore indicate that the FOMC cycle in stock returns is not a risk premium, but instead reflects unexpectedly positive policy news.

Firm-Level Exposure to Epidemic Diseases: Covid-19, SARS, and H1N1 Tarek Hassan, Stephan Hollander, Laurence van Lent, and Ahmed Tahoun

When Selling Becomes Viral: Disruptions in Debt Markets in the COVID-19 Crisis and the Fed’s Response Valentin Haddad, Alan Moreira, and Tyler Muir

We administer a newly-designed survey to a large panel of wealthy retail investors. The survey elicits beliefs that are important for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition, their log-in behavior, and their trading activity. We establish five facts in this data: (1) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (2) Belief changes do not predict when investors trade, but conditional on trading, they affect both the direction and the magnitude of trades. (3) Beliefs are mostly characterized by large and persistent individual heterogeneity; demographic characteristics explain only a small part of why some individuals are optimistic and some are pessimistic. (4) Expected cash flow growth and expected returns are positively related, both within and across investors. (5) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts provide useful guidance for the design of macro-finance models.

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 examine the relative importance of these two forces: the effect of interest rates on tail inequality depends on the average issuance rate and the leverage of entrepreneurs making it to the top of the wealth distribution. After measuring these moments in the data, we find that the secular decline in real interest rates has been a major contributor to the rise of top wealth inequality in the U.S.