Research

Research Papers

Decentralized and Centralized Options Trading: A Risk Premia Perspective, with A. Andolfatto, S. Naik, 2024

On-Chain options refer to option contracts, traded directly on a decentralized exchange on the Ethereum blockchain. We report a novel set of stylized facts about the functioning of this so-called automated market making for options trading. We document the extent to which the On-Chain options differ from their Off-Chain counterparts traded on centralized exchanges. In particular, we identify a difference in implied volatilities between On-Chain and Off-Chain options, attributing it to factors like the complex On-Chain fee structure, trading volume, and net demand pressure.

Implied Impermanent Loss: A Cross-Sectional Analysis of Decentralized Liquidity Pools, with T. Li, S. Naik, A. Papanicolaou, 2024

We propose a continuous-time stochastic model to analyze the dynamics of the impermanent loss, as the variance of the relative price on the underlying tokens, in decentralized liquidity provision. We estimate the risk-neutral joint distribution of the tokens by minimizing the Hansen–Jagannathan bound, which we then use to value the impermanent loss and for calculating an implied correlation of the token pair. We explore how implied volatilities and correlations affect impermanent loss, revealing their role in explaining the cross-sectional returns of liquidity pools. We test our hypothesis on options data from a major centralized derivative exchange.

Yield Farming for Liquidity Provision, with T. Li, S. Naik, A. Papanicolaou, 2024

Yield farming in decentralized finance is the practice of providing liquidity in exchange for a share of transaction fees paid by liquidity takers. In this article, we analyze yield farming's risks and returns using on-chain data from major decentralized exchanges. We propose a mathematical model that incorporates stochasticity of returns, impermanent loss as a source of risk, and gas fees paid by liquidity providers. By calibrating the model to the data, we gain insights into the trade-off between future earnings and upfront gas fees, offering a valuable understanding of yield farming's economic dynamics.

Pandemic Tail Risk, with M. Breugem, R. Corvino, R. Marfe, 2024

This paper studies the measurement of forward-looking tail risk in US equity markets around the COVID-19 outbreak. We document that financial markets are informative about how pandemic risk has spread in the economy in advance of the actual outbreak. While the tail risk of the market index did not respond before the outbreak, investors identified less pandemic-resilient economic sectors whose tail risk boomed in advance of both the market drawdown and the implementation of social distancing provisions. This pattern is consistent across different methodologies for measuring forward-looking tail risk, using option contracts, and across various horizons.

Correlations, Value Factor Returns, and Growth Options, 2023

This paper shows theoretically and empirically that the average equity correlation is related to investment-specific technology (IST) shocks and to growth options. Average equity correlation forecasts returns on growth stocks and returns on the value factor. A production-based asset-pricing model motivates the findings and provides a novel explanation for the market return predictability by average equity correlation: Innovation, e.g., IST shocks, favors individual growth option accumulation and leads to lower average equity correlations. The expected average equity correlation is related to risks associated with the value premium and growth option dynamics and therefore serves as a leading procyclical state variable.

Investor Behavior under Prospect Theory: Evidence from Mutual Funds, with J. Guo, 2022

This paper studies the investment behavior of investors and fund managers within the mutual funds industry. We find that investors are biased in their fund purchase decisions in a way described by prospect theory: The prospect theory value i) predicts future fund flows, even though it is not related to the funds' future performance, ii) contains incremental information compared to existing historical performance measures, and iii) is mainly driven by the loss aversion property encapsulated in it. Fund managers are not subject to any behavioral bias identifiable by prospect theory when selecting stocks for their fund portfolio.

Expected correlation extracted jointly from index and stock options, predicts future market excess returns for horizons of up to 1 year, in- and out-of-sample. The predictive power is superior and incremental to that of risk measures based on the marginal distribution of the market, including (semi)variance risk premiums, and works through three channels: market variance, idiosyncratic risk and the crosssectional dispersion of systematic betas, with each one linked to economic fundamentals in its own way. Jointly, option-implied versions of market and idiosyncratic variances, and dispersion of market betas predict market returns even better than the implied correlation.

Correlation risk is an integral part of the portfolio variance risk, yet playing a separate role and having distinct patterns in predicting future market returns, portfolio risks and macroeconomic conditions. Expected correlations predict market returns for a year ahead, and contrary to the accepted view of correlation as crash risk state variable, this link works through the ability of correlations to predict long-term diversification, namely, average correlations and the lower bound of non-diversifiable market risk. Economy-wide implied correlation built exclusively from combining option prices of sector ETFs and the index jointly predicts future market returns and systematic diversification risk. Newly developed implied correlations and correlation risk premiums for economic sectors provide industry-related information and are also used to extract option-implied risk factors.

Work in Progress

Conference Organizations

Awards and Scholarships

Department Visit

Referee