Research on continuous-time econometrics and finance

Research on high-frequency econometrics and finance

Research on microstructure frictions

Research on low-frequency asset pricing

Research on nonlinear, nonstationary time series

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0DTE option pricing (with Nicola Fusari and Roberto Reno')  (SSRN draft)

The market for ultra short-term (zero days-to-expiry or 0DTE) options has grown exponentially over the last few years. In 2023, daily volume in 0DTEs reached over 45% of overall daily options volume. After briefly describing this exploding new market, we present a novel pricing formula designed to capture the shape of the 0DTE implied volatility surface both out-of-the-money (through discontinuities in the dynamics of the underlying) and at-the-money (through continuous volatility dynamics driven, in particular, by leverage and the volatility-of-volatility). The latter result hinges on an Edgeworth-like expansion of the conditional characteristic function of the continuous portion of the underlying’s price process. The expansion shifts probability mass from an otherwise locally Gaussian return density by adding time-varying skewness (through leverage) and time-varying kurtosis (through the volatility-of-volatility). The expansion is local in time and, therefore, naturally suited to price ultra short-tenor instruments, like 0DTEs. We document considerable price improvements as compared to state-of-the-art specifications. We also provide suggestive results on nearly instantaneous predictability (through leverage and the volatility-of-volatility) by estimating 0DTE return/variance risk premia.

Conditional spectral methods (with Yinan Su)  (SSRN draft)

We model predictive frequency-specific cycles. By employing suitable matrix representations, we express lead values of covariance-stationary multivariate time series in terms of conditionally orthonormal frequency-specific basis. The representations yield conditionally orthogonal decompositions of these lead values. They also provide decompositions of the conditional variances and betas in terms of conditional frequency-specific variances and betas capturing predictive variability and co-variability over cycles of alternative lengths without spillovers across cycles. Making use of the proposed representations within the classical family of time-varying conditional volatility models, we document the role of time-varying volatility forecasts in generating orthogonal predictive frequency-specific cycles in returns. We conclude by providing suggestive evidence that the conditional variances of the predictive return cycles may be priced over short-to-medium horizons.

Spectral financial econometrics (with Andrea Tamoni)  (paper)

Econometric Theory, forthcoming

We survey the literature on spectral regression estimation. We present a cohesive framework designed to model dependence on frequency in the response of economic time series to changes in the explanatory variables. Our emphasis is on the statistical structure and on the economic interpretation of time-domain specifications needed to obtain horizon effects over frequencies, over scales, or upon aggregation. To this end, we articulate our discussion around the role played by lead-lag effects in the explanatory variables as drivers of differential information across horizons. We provide perspectives for future work throughout.

Spectral factor models (with Shomesh Chaudhuri, Andrew W. Lo and Andrea Tamoni) (paper)

Journal of Financial Economics, 2021, 142, 214-238.

We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed—without being forced—to vary across frequencies. Frequency-specific systematic risk is captured by a notion of spectral beta. We show that traditional factor models restrict the spectral betas to be constant across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to reveal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.

Structural stochastic volatility (with Nicola Fusari and Roberto Reno') (SSRN draft)

A novel closed-form pricing formula for short-maturity options is employed to jointly identify equity characteristics (spot volatility, spot leverage, and spot volatility of volatility) which have been the focus of large, but separate, strands of the literature. Interpreting equity as a call option on asset values, all equity characteristics should depend on structural sources of risk, such as the variance of the firm’s assets and the extent of the firm’s financial leverage. We confirm the implications of theory with data, thereby providing support for relations (like the link between spot leverage and the firm’s financial leverage) broadly considered empirically ambiguous.

Return predictability with endogenous growth (with Lorenzo Bretscher and Andrea Tamoni) (SSRN draft)

Journal of Financial Economics, forthcoming

The component of the volatility of total factor productivity (TFP) that is orthogonal to the dividend price ratio is shown to have long-run predictive ability for market returns. This finding implies that TFP volatility should also predict real cash flows and/or real interest rates: it is found to mainly predict real cash flows through inflation. A model with endogenous growth, Epstein-Zin preferences and price rigidities reconciles both TFP volatility-driven long-run predictability and its real implications. Within the model, we justify the cross-sectional pricing of TFP volatility risk in alternative asset classes as well as the similar (to that of TFP volatility) predictive ability of a suitable low-frequency notion of market volatility.

Systematic staleness (with Davide Pirino and Roberto Reno') (SSRN draft)

Journal of Econometrics, forthcoming

Asset prices are stale. We define a measure of systematic (market-wide) staleness as the percentage of small price adjustments over multiple assets. A notion of idiosyncratic (asset-specific) staleness is also established. For both systematic and idiosyncratic staleness, we provide a limit theory based on joint asymptotics relying on increasingly-frequent observations over a fixed time span and an increasing number of assets. Using systematic and idiosyncratic staleness as moment conditions, we introduce novel structural estimates of systematic and idiosyncratic measures of liquidity obtained from transaction prices only. The economic signal contained in the latter is assessed by virtue of suitable metrics.

Beta in the tails (with Roberto Reno') (paper)

Journal of Econometrics, 2022, 227, 134-150

Do hedge funds hedge? In negative states of the world, often not as much as they should. For several styles, we report larger market betas when market returns are low (i.e., “beta in the tails”). We justify this finding through a combination of negative-mean jumps in the market returns and large market jump betas: when moving to the left tail of the market return distribution jump dynamics dominate continuous dynamics and the overall systematic risk of the fund is driven by the higher systematic risk associated with return discontinuities. Methodologically, the separation of continuous and discontinuous dynamics is conducted by exploiting the informational content of the high-order infinitesimal cross-moments of hedge-fund and market returns.