Adrien d'Avernas

Banks’ ratio of market equity to book equity was close to one until the 1990s, then more than doubled during the 1996-2007 period, and fell again to values close to one after the 2008 financial crisis. Sarin and Summers (2016) and Chousakos and Gorton (2017) argue that the drop in banks market to book ratio since the crisis is due to a loss in bank franchise value or profitability. In this paper we argue that the market to book ratio is the sum of two components: franchise value, and government guarantees. We empirically decompose the ratio between these two components, and find that a large portion of the variation in this ratio over time is due to changes in the value of government guarantees.

Financial crises seem particularly severe and lengthy when banks fail to recapitalize after bearing large losses. We present a model that jointly explains the slow recovery of bank capital and economic activity. Banks provide intermediation in markets with informational asymmetries. Large equity losses reduce banks’ capacity to sustain further losses. Losing this capacity leads to reductions in intermediation that exacerbate adverse selection. Adverse selection, in turn, lowers profit margins for banks, which explains the banks failure to accumulate profits or attract equity injections. The model delivers financial crises that are infrequent events characterized by persistent low economic growth.

We propose a model of post-GFC money markets and monetary policy implementation. In our framework, capital regulation may deter banks from intermediating liquidity derived from holding reserves to shadow banks. Consequently, money markets can be segmented, and the scarcity of Treasury bills available to shadow banks is the main driver of short-term spreads. In this regime, open market operations have an inverse effect on net liquidity provision when swapping ample reserves for scarce T-bills or repos. Our model quantitatively accounts for post-2010 time series for repo rates, T-bill yields, and the Fed's reverse repo facility usage.

We provide a simple model of investment by a firm funded with debt and equity and robust empirical evidence to demonstrate that, once we control for the debt overhang problem with credit spreads, asset volatility is an unambiguously positive signal for investment, while equity volatility sends a mixed signal: Elevated volatility raises the option value of equity and increases investment for financially sound firms, but it exacerbates debt overhang and decreases investment for firms close to default. Our study provides a simple unified understanding of the structural and empirical relationships between investment, credit spreads, equity vs. asset volatility, leverage, and Tobin's q.

This paper provides a general model to assess the stability of stablecoins, cryptocurrencies pegged to a traditional currency. We study the problem of a monopolist platform that can earn seigniorage revenues from issuing stablecoins. We characterize stablecoin issuance-redemption and pegging dynamics under various degrees of commitment to policies. Even under full commitment, the stablecoin peg is vulnerable to large demand shocks. Backing stablecoins with collateral helps to stabilize the platform but does not provide commitment. Decentralization of issuance, combined with collateral, can act as a substitute for commitment.

This paper proposes a new model of monetary policy implementation to account for two key developments: (i) the introduction of intraday liquidity requirements and (ii) the decreasing relevance of the federal funds market in favor of repurchase agreement (repo) markets with nonbank participants. Our paper demonstrates how liquidity requirements prevent banks from arbitraging between the fed funds and repo markets and generate large repo spikes. We propose a simple calibration for excess intraday reserves. Consistent with our theory, this metric turned negative in the summer of 2019, at the time US repo markets experienced a spike of 400 basis points.

This paper presents a dynamic asset pricing model of Treasury bonds with banks subject to both capital and liquidity requirements. Capital requirements and households’ preference for money-like assets push non-banks to be the primary holders of Treasuries, thereby exposing Treasury yields to funding shocks originating in repo markets. When holding sufficient reserves, banks mitigate those shocks by lending in repo when the funding supply tightens. When reserves are scarce, banks stop lending. Repo rates and Treasury yields spike up to reflect those funding imbalances. Our model highlights the key role of both sides of the central bank's balance sheet as well as agents' anticipation about the duration of shocks and policy intervention in explaining observed Treasury market disruptions.

The deposit business differs at large versus small banks. We provide a parsimonious model and extensive empirical evidence supporting the idea that much of the variation in deposit-pricing behavior between large and small banks reflects differences in ``preferences and technologies.'' Large banks offer superior liquidity services but lower deposit rates, and locate where customers value their services. In addition to receiving a lower level of deposit rates on average, customers of large banks exhibit lower demand elasticities with respect to deposit rate spreads. As a result, despite the fact that the locations of large-bank branches have demographics typically associated with greater financial sophistication, large-bank customers earn lower average deposit rates. Our explanation for deposit pricing behavior challenges the idea that deposit pricing is mainly driven by pricing power derived from the large observed degree of concentration in the banking industry.

This work proposes a framework to study the risk-benefit trade-off of quantitative easing (QE) for the consolidated government, integrating the central bank and treasury department. In a simple model with distortionary taxes, nominal frictions, and a zero lower bound, we characterize the optimal size of a QE program as equalizing the marginal benefit from stimulating output to the marginal cost of induced rollover risk for taxpayers. A conservative quantification of this trade-off suggests that QE programs in the US made a positive net present contribution to welfare.

This paper investigates how the presence of shadow banks affects the ability of central banks to offset a liquidity crisis. We propose an asset pricing model with heterogeneous banks subject to funding risk. While traditional banks have direct access to central bank operations, shadow banks rely on the intermediation of liquidity from traditional banks. In a crisis, this intermediation stops due to lack of collateral and shadow banks are left without lender-of-last-resort. Traditional instruments are not sufficient to fully mitigate the crisis. Opening liquidity facilities to shadow banks and purchasing illiquid assets is then necessary to further boost asset prices and tackle the crisis.

We propose an algorithm capable of solving a general class of continuous-time asset pricing models, including heterogeneous agent models, in a fast and standardized way. These models require the solution of a Hamilton-Jacobi-Bellman equation for each agent coupled with a system of algebraic equations. We rely on a finite difference algorithm and show how using a Stern-Brocot Tree decomposition as advocated by Bonnans, Ottenwaelter, and Zidani (2004) allows for fast and stable convergence in settings with up to two endogenous and stochastic state variables. We provide an open source software package, PyMacroFin, that includes an object-oriented interface for model definition and solution for any model in this general class.

We employ machine learning techniques to examine the relationship between expected equity returns and option returns. While any option pricing model predicts a strong link between expected option returns and underlying stock returns, empirical analyses so far are limited due to difficulties in recovering forward-looking equity returns. Recent advances in the asset pricing literature via machine learning, however, offer new avenues to explore this relationship. Leveraging on these techniques we extract a prediction of expected equity returns and uncover an economic puzzle: While we can recover the link in simulation studies, we fail to recover it using the history of available option returns. We rule out that this is due to noise and explore market segmentation as an alternative explanation.

In this paper, I provide a structural approach to quantify the forces that govern the joint dynamics of five financial indicators: (i) default risk, (ii) corporate bond credit spreads, (iii) aggregate and (iv) idiosyncratic equity volatility, and (v) corporate bond bid-ask spreads. I build a dynamic structural model and estimate fundamental shocks using a large firm-level database on credit spreads, equity prices, accounting statements, and bond recovery ratios in the U.S. from 1973 to 2014. The model accurately accounts for the historical levels and dynamics of the financial indicators, both over time and in the cross-section. A structural decomposition reveals that the joint dynamics of these financial indicators is driven by fluctuations in firms’ asset values and firms’ aggregate asset volatility. I find that the informational content of the financial indicators for predicting economic activity is captured by fluctuations in firms’ aggregate asset volatility. All together, my results suggest that fluctuations in firms’ aggregate asset volatility are key for the transmission channel that links the fundamental drivers of financial indicators to the real economy.