Tyler Muir

Intermediary asset pricing posits that financial institutions are important players in financial markets, and that their decisions shape asset prices beyond simply reflecting the preferences of the average household in the economy. We explain how this approach helps make sense of empirical patterns in the data: the excess volatility of asset prices, differences in price movements across asset classes, the cross section of expected returns within asset classes, and specific arbitrages and price dislocations. We also review how this view of price fluctuations has important implications for macroeconomic dynamics, international economics, and policy, with particular focus on the role of financial regulation and monetary policy.

Market macrostructure studies the broad organization of financial markets into key players and institutional features and how this organization affects the level and dynamics of asset prices. We present a simple model to discuss when, why, and how market macrostructure matters for asset prices, then review work on three specific examples: the rise of passive investing in the stock market, the increased role of central banks in bond markets through asset purchase programs, and the role of levered financial intermediaries in financial markets.

Financial intermediaries trade frequently in many markets using sophisticated models. Their marginal value of wealth should therefore provide a more informative stochastic discount factor (SDF) than that of a representative consumer. Guided by theory, we use shocks to the leverage of securities broker-dealers to construct an intermediary SDF. Our single-factor model prices size, book-to-market, momentum, and bond portfolios with an R-squared of 77% and an average annual pricing error of 1% -- performing as well as standard multifactor benchmarks designed to price these assets.

Poor financial health of intermediaries coincides with low asset prices and high risk premiums. Is this because intermediaries matter for asset prices, or simply because their health correlates with economy-wide risk aversion? In the first case, return predictability should be more pronounced for asset classes in which households are less active. We provide evidence supporting this prediction, suggesting that a quantitatively sizable fraction of risk premium variation in several large asset classes such as credit or MBS is due to intermediaries.

Balance sheet expansion of leveraged intermediaries negatively predicts returns of stocks, bonds, currencies, and housing. The predictability is stronger at shorter horizons, is robust to macroeconomic controls, and holds outside distress periods. Intermediaries in global financial centers predict international equity returns. A new data set on individual stock holdings of Japanese intermediaries since 1955 shows intermediaries affect returns of stocks directly held. Our results suggest a strong universal link between intermediaries and asset returns distinct from macroeconomic channels.

I analyze the behavior of risk premia in financial crises, wars, and recessions in an international panel spanning over 140 years and 14 countries. Expected returns increase substantially in financial crises, but not in the other episodes. Asset prices decline in all episodes, but the decline in financial crises is substantially larger than the decline in fundamentals so that expected returns going forward are large. By disentangling financial crises from other bad macroeconomic times, the results suggest that financial crises are particularly important to understanding why risk premia vary, and theories where asset prices are related to the health of the financial sector appear particularly promising.

At the announcement of a new policy, agents form a view of state-contingent policy actions and impact. We develop a method to estimate this state-contingent perception and implement it for many asset-purchase interventions worldwide. Expectations of larger support in bad states -- "policy puts" -- explain a large fraction of the announcements' impact. For example, when the Fed introduced purchases of corporate bonds in March 2020, markets expected five times more price support had conditions worsened relative to the median scenario. Perceived promises of additional support in bad states alter asset prices, risk, and the response to future announcements.

We argue that quantitative easing (QE) and tightening policies constitute a dynamic state-contingent plan instead of a succession of independent interventions. This view changes the main reason QE is effective by adding an insurance channel to the static effect of absorbing bond supply in a given period. QE purchases occur in bad economic states when the supply of government debt increases. Increasing long-term bond prices in bad economic states increases their safety, driving up their value and thus lowering ex-ante yields. We estimate that this insurance channel alone lowers long-term bond yields by 75-100 bps. Consistent with a state-contingent channel, implied volatilities of long-duration risk-free securities fall substantially on QE announcements. We calibrate a policy rule for asset purchases to their historical path and include it in a quantitative term structure model.

Asset purchase policies are a dynamic state-contingent plan instead of a succession of independent interventions. We apply this view to the experience of asset purchase programs in the Euro area. Our evidence suggests that the market expects interventions in large part based on sovereign spreads and the total supply of sovereign credit risk. In turn, this provides a powerful ex-ante mechanism to lower spreads in the Euro area: because interventions typically occur when spreads, debt issuance, and dislocations are high, purchases provide ex-ante insurance to bond investors by stabilizing future sovereign spreads. This increases demand and lowers credit risk premiums on sovereign bonds. We illustrate this channel using both low frequency data and event study analysis, including using option prices to assess the state-contingent nature of interventions.

We document extreme disruption in debt markets during the COVID-19 crisis: a severe price crash accompanied by significant dislocations at the safer end of the credit spectrum. Investment-grade corporate bonds traded at a discount to credit default swaps; exchange-traded funds traded at a discount to net asset value, more so for safer bonds. The Federal Reserve's announcement of corporate bond purchases caused these dislocations to disappear and prices to recover. The best explanation is an acute liquidity need for specific bond investors, such as mutual funds, leading them to liquidate large positions.

Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in factor volatilities are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions and crises yet still earns high average returns.

A long-term investor who ignores variation in volatility gives up the equivalent of 2.4% of wealth per year. This result holds for a wide range of parameters consistent with US stock market data, and it is robust to estimation uncertainty. We propose and test a new channel, the volatility composition channel, for how investment horizon interacts with volatility timing. Investors respond substantially less to volatility variations when mean reversion in returns increases disproportionately alongside volatility, particularly when this mean reversion occurs rapidly.

We provide evidence that agents have slow-moving beliefs about stock market volatility that lead to initial underreaction to volatility shocks followed by delayed overreaction. These dynamics are mirrored in the VIX and variance risk premiums, which reflect investor expectations about volatility, and are also supported in both surveys and firm-level option prices. We embed these expectations into an asset pricing model and find that the model can account for a number of stylized facts about market returns and return volatility that are difficult to reconcile, including a weak or even negative risk-return tradeoff.

Standard risk factors can be hedged with minimal reduction in average returns. Stocks with low factor-exposure have similar performance relative to stocks with high factor-exposure, hence a long-short portfolio hedges factor risk with little reduction in expected returns. This is true for both "macro" factors relating to the business cycle, and "reduced-form" factors such as value and momentum. Hedging macro factors also hedges business cycle risk and many other macro factors argued to be priced in the literature, and hedging "reduced-form" factors generates large alphas.

We document a persistent doubling of U.S. stock market volatility since the mid-20th century, which cannot be attributed to macroeconomic fundamentals or idiosyncratic firm shocks. Instead, we show that the increased volume and dominance of index trading -- via futures, ETFs, and extended trading hours -- has structurally raised aggregate stock market volatility. We exploit the introduction of E-mini S&P 500 futures and historical NYSE trading-hour adjustments as natural experiments to provide causal evidence that index-level trading amplifies market variance through trading volume. Our model of index demand shocks rationalizes these findings, predicting higher market-level volatility, reduced return autocorrelation, and an increased share of systematic risk.

We analyze the behavior of credit and output in financial crises using data on credit spreads and credit growth. Crises are marked by a sharp rise in credit spreads, signaling sudden shifts in expectations. The severity of a crisis can be predicted by the extent of credit losses and financial sector fragility. This interaction is a key feature of crises. Postcrisis recessions are typically severe and prolonged. Notably, precrisis spreads tend to drop to low levels while credit growth accelerates, indicating that credit supply expansions often precede crises.

Modern financial crises are difficult to explain because they do not always involve bank runs, or the bank runs occur late. For this reason, the first year of the Great Depression, 1930, has remained a puzzle. Industrial production dropped by 20.8 percent despite no nationwide bank run. Using cross-sectional variation in external finance dependence, we demonstrate that banks' decision to not use the discount window and instead cut back lending and invest in safe assets can account for the majority of this decline.

This paper examines the pricing of risk for both stocks and real estate during credit booms from 1870 to 2015 across 17 countries. During a credit boom, investors are willing to accept lower risk premiums despite the fact that the overall amount of risk in the economy is rising -- expected excess returns for equities and housing both fall while crash likelihood doubles. The most compelling explanation is that investors extrapolate past low-risk environments into the future, creating excessive optimism that prompts overleveraging and inflated asset prices.

We document the emergence of two distinct types of banks over the past decade: high-rate banks, which set deposit rates to match market rates, hold shorter-term assets, and earn a spread through higher credit risk in personal and business loans; and low-rate banks, which offer low, stable deposit rates, hold longer-term assets like MBS, and lend less to firms. This divergence shifts deposits toward high-rate banks during monetary tightening, reducing the banking sector's maturity transformation capacity and concentrating credit risk among high-rate banks. Technological advancements appear to drive this trend: high-rate banks operate primarily online and attract rate-sensitive, less-sticky depositors.

Banks' relationships with their depositors are valuable when depositors remain sticky, but this value evaporates if they leave. This tension makes banks fragile when interest rates increase and long-term asset values are depressed. In this scenario, if all of its depositors leave, the bank fails, which justifies depositors' departure in the first place. Such failures can happen even when banks only invest in liquid assets and when deposits are insured, and they are more likely for banks with the most valuable relationships. This fragility leads to sharp changes in the exposure of bank values to interest rate risk: insensitive most of the time but highly responsive when asset losses are about to catch up with them.

We use the experience of the U.S. National Banking Era to evaluate the most important bank regulation to emerge from the financial crisis, the Bank for International Settlement's liquidity coverage ratio (LCR) which requires that short-term bank debt be backed one-for-one with U.S. Treasuries. The experience of the National Banking Era, which also required that bank short-term debt be backed by Treasury debt one-for-one, suggests that the LCR is unlikely to reduce financial fragility and may increase it.

US data display aggregate external financing and savings waves. Firms can allocate costly external finance to productive capital, or to liquid assets with low physical returns. If firms raise costly external finance and accumulate liquidity, either the cost of external finance is relatively low, or the total return to liquidity accumulation, including its shadow value as future internal funds, is particularly high. We formalize this intuition by estimating a dynamic model of firms' financing and savings decisions, and use our model along with firm level data to construct an empirical estimate of the average cost of external finance from 1980-2014.