🔬 Research
📄 Published
- SSRN | DOI | Non-Standard Errors
Menkveld et al. (crowd-finance project with 200+ coauthors)
Journal of Finance, 2024, 79(3), 2339–2390.
Abstract
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
- SSRN | DOI | Dispersion of Beliefs Bounds: Sentimental Recovery
Altan Pazarbasi, Paul Schneider, and Grigory Vilkov
Management Science, 2024, 70(12), 8217–9119.
Abstract
We present a nonparametric method to recover a bound on ex ante dispersion of beliefs (DBB) from asset prices with minimal assumptions. DBB constrains the dispersion among all possible distributions in an economy, consistent with observed prices and subject to a good-deal bound. In model-based economies, DBB effectively tracks belief heterogeneity and serves as a diagnostic tool for evaluating model calibrations. Empirically, DBB relates to common proxies of belief dispersion, offering a real-time, market-implied disagreement measure. Our versatile approach applies to both complete and incomplete markets represented by any asset class.
- SSRN | DOI | Pricing Climate Change Exposure
Zacharias Sautner, Laurence van Lent, Grigory Vilkov, and Ruishen Zhang
Management Science, 2023, 69(12), 7151–7882.
Abstract
We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs.
- SSRN | DOI | Data | Firm-level Climate Change Exposure
Zacharias Sautner, Laurence van Lent, Grigory Vilkov, and Ruishen Zhang
Journal of Finance, 2023, 78(3), 1449–1498.Abstract
We develop a method that identifies the attention paid by earnings call participants to firms’ climate change exposures. The method adapts a machine learning keyword discovery algorithm and captures exposures related to opportunity, physical, and regulatory shocks associated with climate change. The measures are available for more than 10,000 firms from 34 countries between 2002 and 2020. We show that the measures are useful in predicting important real outcomes related to the net-zero transition, in particular, job creation in disruptive green technologies and green patenting, and that they contain information that is priced in options and equity markets.
- SSRN | DOI | Data | Generalized Bounds on the Conditional Expected Excess Return on Individual Stocks
Fousseni Chabi-Yo, Chukwuma Dim, and Grigory Vilkov
Management Science, 2023, 69(2), 922–939.
Abstract
We derive generalized bounds on conditional expected excess returns that can be computed from option prices. The generalized lower bound may serve as an expected excess return proxy for individual and basket-type assets, is conditionally tight, accounts for the entire risk-neutral distribution of returns, and outperforms existing variance-based models in out-of-sample predictions. Bounds calibrated to realized returns correspond to reasonable risk aversion and prudence. On average, expected stock returns given by the bounds decrease on even weeks of the Federal Open Market Committee cycle. Cross-sectional tests deliver a reasonable market risk premium.
- SSRN | DOI | Data | Carbon Tail Risk
Emirhan Ilhan, Zacharias Sautner, and Grigory Vilkov
Review of Financial Studies, 2021, 34(3), 1540–1571.
Abstract
Strong regulatory actions are needed to combat climate change, but climate policy uncertainty makes it difficult for investors to quantify the impact of future climate regulation. We show that such uncertainty is priced in the option market. The cost of option protection against downside tail risks is larger for firms with more carbon-intense business models. For carbon-intense firms, the cost of protection against downside tail risk is magnified at times when the public’s attention to climate change spikes, and it decreased after the election of climate change skeptic President Trump.
- SSRN | DOI | Asymmetric Volatility Risk: Evidence from Option Markets
Jens Jackwerth and Grigory Vilkov
Review of Finance, 2019, 23(4), 777–799.
Abstract
Asymmetric volatility concerns the relation of returns to future expected volatility. Much is known from option prices about the marginal risk-neutral distributions (RNDs) of S&P 500 returns and of relative changes in future expected volatility (VIX). While the bivariate RND cannot be inferred from the marginals, we propose a novel identification based on long-dated index options. We estimate the risk-neutral asymmetric volatility implied correlation (AVIC) and find it to be significantly lower than its realized counterpart. We interpret the economics of the asymmetric volatility correlation risk premium and use AVIC to predict returns, volatility, and risk-neutral quantities.
- SSRN | DOI | Non-Myopic Betas
Semyon Malamud and Grigory Vilkov
Journal of Financial Economics, 2018, 129(2), 357–381.
Abstract
An overlapping generations model with investors having heterogeneous investment horizons leads to a two-factor asset pricing model. The risk premiums are determined by the exposure to the market (myopic betas) and the future return on the efficient portfolio (non-myopic betas), which is identified nonparametrically from equilibrium. Non-myopic betas are priced in the cross-section of stocks, producing increasing and economically significant risk-return relation. In the model with funding constraints, low non-myopic beta stocks deliver higher risk-adjusted returns. Empirically, a betting against non-myopic beta portfolio generates superior performance relative to common factor models and is negatively correlated with the market betting against beta portfolio.
- SSRN | DOI | The Intended and Unintended Consequences of Financial-Market Regulations: A General Equilibrium Analysis
Adrian Buss, Bernard Dumas, Raman Uppal, and Grigory Vilkov
Journal of Monetary Economics, 2016, 81(C), 25–43.
Abstract
In a production economy with trade in financial markets motivated by the desire to share labor-income risk and to speculate, we show that speculation increases volatility of asset returns and investment growth, increases the equity risk premium, and reduces welfare. Regulatory measures, such as constraints on stock positions, borrowing constraints, and the Tobin tax have similar effects on financial and macroeconomic variables. However, borrowing constraints and the Tobin tax are more successful than constraints on stock positions at improving welfare because they substantially reduce speculative trading without impairing excessively risk-sharing trades.
- SSRN | DOI | Improving Portfolio Selection Using Option-Implied Volatility and Skewness
Victor DeMiguel, Yuliya Plyakha, Raman Uppal, Grigory Vilkov
Journal of Financial and Quantitative Analysis, 2013, 48(6), 1813–1845.
Abstract
Our objective in this paper is to examine whether one can use option-implied information to improve the selection of mean-variance portfolios with a large number of stocks, and to document which aspects of option-implied information are most useful to improve their out-of-sample performance. Portfolio performance is measured in terms of volatility, Sharpe ratio, and turnover. Our empirical evidence shows that using option-implied volatility helps to reduce portfolio volatility. Using option-implied correlation does not improve any of the metrics. Using option-implied volatility, risk premium, and skewness to adjust expected returns leads to a substantial improvement in the Sharpe ratio, even after prohibiting short sales and accounting for transaction costs.
- SSRN | DOI | Measuring Equity Risk with Option-Implied Correlations
Adrian Buss and Grigory Vilkov
Review of Financial Studies, 2012, 25(10), 3113–3140.
Abstract
We use forward-looking information from option prices to estimate option-implied correlations and to construct an option-implied predictor of factor betas. With our implied market betas, we find a monotonically increasing risk-return relation, not detectable with standard rolling-window betas, with the slope close to the market excess return. Our implied betas confirm a risk-return relation consistent with linear factor models because, when compared to other beta approaches: (i) they are better predictors of realized betas, and (ii) they exhibit smaller and less systematic prediction errors. The predictive power of our betas is not related to known relations between option-implied characteristics and returns.
- SSRN | DOI | The Price of Correlation Risk: Evidence from Equity Options
Joost Driessen, Pascal Maenhout and Grigory Vilkov
Journal of Finance, 2009, 64(3), 1377-1406.
Abstract
We study whether exposure to marketwide correlation shocks affects expected option returns, using data on S&P100 index options, options on all components, and stock returns. We find evidence of priced correlation risk based on prices of index and individual variance risk. A trading strategy exploiting priced correlation risk generates a high alpha and is attractive for CRRA investors without frictions. Correlation risk exposure explains the cross-section of index and individual option returns well. The correlation risk premium cannot be exploited with realistic trading frictions, providing a limits-to-arbitrage interpretation of our finding of a high price of correlation risk.
📘 In Books
- DOI | Equal or Value Weighting? Implications for Asset-Pricing Tests
Yuliya Plyakha, Raman Uppal, and Grigory Vilkov
In: Zopounidis C., Benkraiem R., Kalaitzoglou I. (eds) Financial Risk Management and Modeling. Springer, Cham.
Abstract
We show that an equal-weighted portfolio has a higher total return than a value-weighted portfolio. As one may expect, this is partly because the equal-weighted portfolio has higher exposure to value and size factors, but we show that a considerable part (42%) comes from rebalancing to maintain constant weights. We then demonstrate, through four applications, that inferences from asset-pricing tests are substantially different depending on whether one uses equal- or value-weighted portfolios. These four applications are tests of the: Capital Asset Pricing Model, spanning properties of the stochastic discount factor, relation between characteristics and returns, and pricing of idiosyncratic volatility.
🛠️ In Progress
Estimating Rough Volatility Models
Bjorn Eraker and Grigory Vilkov, 2025Quantile Dispersions
Jules v. Binsbergen and Grigory Vilkov, 2025
📑 Working Papers
SSRN | Factor Dispersions
Daniil Gerchik, Vittorio Ruffo, Lorenzo Schoenleber, Grigory Vilkov, 2024SSRN | 0DTEs: Trading, Gamma Risk and Volatility Propagation
Chukwuma Dim, Bjorn Eraker, Grigory Vilkov, 2024, R&R in RFSSSRN | ODTE Trading Rules
Grigory Vilkov, 2023SSRN | Climate Value and Values Discovery in Earning Calls, 2024
Zacharias Sautner, Laurence van Lent, Grigory Vilkov, and Ruishen ZhangSSRN | Investor Sophistication and Portfolio Dynamics
Adrian Buss, Raman Uppal, and Grigory Vilkov, 2022, R&R in RFSSSRN | Media Narratives and Price Informativeness
Chukwuma Dim, Francesco Sangiorgi, and Grigory Vilkov, 2023SSRN | Factor Investing, Learning from Prices, and Endogenous Uncertainty in Asset Markets
Chukwuma Dim, Francesco Sangiorgi, and Grigory Vilkov, 2020
🗃️ Permanent WPs
SSRN | The Implications of Financial Innovation for Capital Markets and Household Welfare
Adrian Buss, Raman Uppal, and Grigory Vilkov (2017)SSRN | Expected Correlation and Future Market Returns
Adrian Buss, Lorenzo Schoenleber, and Grigory Vilkov (2017, updated 12/2018)SSRN | Option-Implied Correlations, Factor Models, and Market Risk
Adrian Buss, Lorenzo Schoenleber, and Grigory Vilkov (2016, updated 02/2017)SSRN | Where Experience Matters: Asset Allocation and Asset Pricing with Opaque and Illiquid Assets
Adrian Buss, Raman Uppal, and Grigory Vilkov (2013–2014)SSRN | Asset Prices in General Equilibrium with Recursive Utility and Illiquidity Induced by Transactions Costs
Adrian Buss, Raman Uppal, and Grigory Vilkov (2013)SSRN | Option-Implied Correlations and the Price of Correlation Risk
Joost Driessen, Pascal Maenhout, and Grigory Vilkov (2005, updated 10/2012)SSRN | Option-Implied Information and Predictability of Extreme Returns
Grigory Vilkov and Yan Xiao (2012, updated 09/2012)SSRN | Why Does an Equal-Weighted Portfolio Outperform Value- and Price-Weighted Portfolios?
Yuliya Plyakha, Raman Uppal, and Grigory Vilkov (2011, updated 01/2012), winner of SPIVA Award 2011SSRN | Risk-Neutral Skewness: Return Predictability and Its Sources
Zahid Ur Rehman and Grigory Vilkov (2008, updated 03/2012)SSRN | The Dynamics of Risk-Neutral Implied Moments: Evidence from Individual Options
Alexandra Hansis, Christian Schlag, and Grigory Vilkov (2009)SSRN | CAPM with Option-Implied Betas: Another Rescue Attempt
Adrian Buss, Christian Schlag, and Grigory Vilkov (2009)SSRN | Portfolio Policies with Stock Options
Yuliya Plyakha and Grigory Vilkov (2008)SSRN | Hedging Options in the Presence of Microstructural Noise
David Horn, Eva Schneider, and Grigory Vilkov (2007)SSRN | Variance Risk Premium Demystified
Grigory Vilkov (2006)
🖥️ Computer Science / IT
Adaptive Parallel Computing for Large-Scale Distributed and Parallel Applications (2010)
Jaiganesh Balasubramanian, Alexander Mintz, Andrew Kaplan, Grigory Vilkov, et al.
DD4LCCI ’10 Proceedings. ACM Digital LibraryZircon Adaptive Software on SGI® Altix® UV 1000 for High Performance Data Analytics (2010)
Alexander Mintz, Douglas C. Schmidt, Grigory Vilkov, et al.
SGI White Paper Series