Adrien d´Avernas

We use machine learning to examine the predictability of hold-to-maturity returns on single stock options. In sharp contrast to the implications of standard asset pricing theory, we find that the expected return of the underlying stock fails to forecast option returns, but does explain cross-sectional variation in option prices. Option trading strategies based on the underlying's expected stock return deliver anomalously low returns. These findings challenge canonical option pricing models and suggest that options are not a suitable instrument to harvest stock risk premia.

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.

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.

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.

We provide a parsimonious model and extensive empirical evidence supporting the idea that much of the variation in deposit-pricing behavior across large vs. 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 paper provides a general framework for analyzing 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 is costly for the platform’s equity (token) holders. Combined with collateral, decentralization can act as a substitute for commitment. 

How does the presence of “shadow banks” – non-bank, unregulated financial intermediaries – affect the ability of central banks to tackle a liquidity crisis? To address this question, we develop an asset pricing model with both bank and non-bank financial institutions. A crucial part of the model is that banks intermediate liquidity between the central bank and non-banks, but this intermediation stops during a financial crisis. Non-banks are then left without a lender-of-last-resort, and central bank liquidity operations with banks are not sufficient to mitigate the crisis. In our stylized model, opening liquidity facilities to non-banks and purchasing illiquid assets are then essential measures to tackle a liquidity crisis.

This paper investigates the efficiency of various monetary policy instruments to stabilize asset prices in a liquidity crisis. We propose a macro-finance model featuring both traditional and shadow banks subject to funding risk. When banks are well capitalized, they have access to money markets and efficiently mitigate funding shocks. When aggregate bank capital is low, a vicious cycle arises between declining asset prices and funding risks. The central bank can partially counter these dynamics. Increasing the supply of reserves reduces liquidity risk in the traditional banking sector, but fails to reach the shadow banking sector. When the shadow banking sector is large, as in the US in 2008, the central bank can further stabilize asset prices by directly purchasing illiquid securities.

Financial crises are particularly severe and lengthy when banks fail to recapitalize after bearing large losses. We present a model that explains the slow recovery of bank capital and economic activity. Banks provide intermediation in markets with information asymmetries. Large equity losses force banks to tighten intermediation, which exacerbates adverse selection. Adverse selection lowers bank profit margins which slows both the internal growth of equity and equity injections. This mechanism generates financial crises characterized by persistent low growth. The lack of equity injections during crises is a coordination failure that is solved when the decision to recapitalize banks is centralized.

Banks' ratio of the market value to book value of their equity was close to 1 until the 1990s, then more than doubled during the 1996-2007 period, and fell again to values close to 1 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 banks' market-to-book ratio is the sum of two components: franchise value and the value of 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.