Research Interests:

Econometrics, Financial Econometrics, High-Dimensional Statistics, Asset Pricing, Financial Economics.


  1. Inference for Option Panels in Pure-Jump Settings
    • with Torben G. Andersen, Nicola Fusari and Viktor Todorov, 2018. Accepted at Econometric Theory. (Article)
  2. Unified Inference for Nonlinear Factor Models from Panels with Fixed and Large Time Span
    • with Torben G. Andersen, Nicola Fusari and Viktor Todorov, 2018. Accepted at Journal of Econometrics. (Article)
  3. Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns
  4. A Local Stable Bootstrap for Power Variations of Pure-Jump Semimartingales and Activity Index Estimation
    • with Ulrich Hounyo, 2017, Journal of Econometrics, 198(1), pp. 10-28. (Article)
  5. Estimating the Quadratic Variation Spectrum of Noisy Asset Prices using Generalized Flat-top Realized Kernels
  6. Medium Band Least Squares Estimation of Fractional Cointegration in the Presence of Low-Frequency Contamination
    • with Bent Jesper Christensen, 2017, Journal of Econometrics, 197(2), pp. 218-244. (Article)
  7. Flat-Top Realized Kernel Estimation of Quadratic Covariation with Nonsynchronous and Noisy Asset Prices
    • 2016, Journal of Business & Economic Statistics, 34(1), pp. 1-22. (Lead Article)
  8. The Role of Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts
    • with Valeri Voev, 2013, Journal of Empirical Finance, 20, pp. 83-95. (Article)
  9. Unit roots, nonlinearities and structural breaks
    • with Niels Haldrup, Robinson Kruse, Timo Teräsvirta, 2013, In: N. Hazimshade and M. Thornton, Eds., Handbook of Empirical Macroeconomics. Handbook of Research Methods and Application Series, Edvard, Elgar Publishing Ltd.

Working Papers:

  1. Fredquency Dependent Risk
    • with Andreas Neuhierl.
    • First draft: October 2018. (Article)
    • Abstract: This paper provides a new nonparametric framework for studying the dynamics of the state vector and its associated risk prices. Specifically, in a general setting where the SDF decomposes into permanent and transitory components, we analyze their contribution to the unconditional asset return premium using frequency domain techniques. We show analytically that the co-spectrum between returns and the SDF only displays frequency dependencies through its transitory component, that is, through the state vector. Moreover, we demonstrate that state vector dynamics and its risk prices can be uncovered by studying (transformations of) the covariance between (portfolios of) asset returns. We introduce two new frequency risk measures. We apply our framework to study frequency risk in the full cross-section of US stocks, utilizing the market, value, size and momentum factors as baseline portfolios to construct the risk measures. Our analysis uncovers the existence of, at least, two significantly priced low-frequency risk factors, one of which commands a large positive risk premium of 6% per year. Moreover, we document, at least, one high-frequency component in the state vector that is significantly priced. Importantly, we show that these frequency dependent risk factors are unspanned by a battery of appraised risk factors and characteristics. Our analysis demonstrates that multiple state vector components with varying persistence and risk prices are needed to be consistent with the cross-section. Throughout, we contrast our findings with the implications of the long-run risk model, the dynamic disaster model as well as a regime-switching CCAPM, providing new analytical results for such models.
  2. Inference for Local Distributions at High Sampling Frequencies: A Bootstrap Approach
    • with Ulrich Hounyo.
    • First draft: April 2018. This version: October 2018. Submitted. (Article)
    • Abstract: We study inference for the local innovations of Itô semimartingales. Specifically, we construct a resampling procedure for the empirical CDF of high-frequency innovations that have been standardized using a nonparametric estimate of its stochastic scale (volatility) and truncated to rid the effect of "large" jumps. Our locally dependent wild bootstrap (LDWB) accommodate issues related to the stochastic scale and jumps as well as account for a special block-wise dependence structure induced by sampling errors. We show that the LDWB replicates first and second-order limit theory from the usual empirical process and the stochastic scale estimate, respectively, as well as an asymptotic bias. Moreover, we design the LDWB sufficiently general to establish asymptotic equivalence between it, and a nonparametric local block bootstrap, also introduced here, up to second-order distribution theory. Finally, we introduce LDWB-aided Kolmogorov-Smirnov tests for local Gaussianity as well as local von-Mises statistics, with and without bootstrap inference, and establish their asymptotic validity using second-order distribution theory. The finite sample performance of CLT and LDWB-aided local Gaussianity tests are assessed in a simulation study as well as two empirical applications. Whereas the CLT test is oversized, even in large samples, the size of the LDWB tests is accurate, even in small samples. The empirical analysis verifies this patter, in addition to providing new insights about the distributional properties of equity indices, commodities, exchange rates and popular macro finance variables.
  3. Consistent Inference for Predictive Regressions in Persistent VAR Economies
    • with Torben G. Andersen.
    • First draft: February 2018. Currently being revised. (Previous Draft)
    • Abstract: This paper studies the properties of standard predictive regressions in model economies, characterized through persistent vector autoregressive dynamics for the state variables and the associated series of interesting. In particular, we consider a setting where all, or a subset, of the variables may be fractionally integrated, and note that this induces a spurious regression problem. We then propose a new inference and testing procedure - the local spectrum (LCM) approach -- for the joint significance of the regressors, which is robust against the variables having different integration orders. The LCM procedure is based on (semi-)parametric fractional-filtering and band spectrum regression using a suitably selected set of frequency ordinates. We establish the asymptotic properties and explain how they differ and extend existing procedures. Using these new inference and testing techniques, we explore the implications of assuming VAR dynamics in predictive regressions for realized return variation. Standard least squares predictive regressions indicate that popular financial and macroeconomic variables carry valuable information about return volatility. In contrast, we find no significant evidence using our robust LCM procedure, indicating that prior conclusions may be premature. In fact, if anything, our results suggest the reverse causality, i.e., rising volatility predates adverse innovations to key macroeconomic variables. Simulations are employed to illustrate the relevance of the theoretical arguments for finite-sample inference.
  4. Dynamic Global Currency Hedging
    • with Bent Jesper Christensen.
    • First draft: January 2016. Revised: November 2018. (Article, Supplementary Appendix)
    • Abstract: This paper proposes a model for discrete-time hedging based on continuous-time movements in portfolio and foreign exchange rate returns. The vector of optimal currency exposures is given by the negative realized regression coefficients from a one-period conditional expectation of the intra-period quadratic covariation matrix for portfolio and exchange rate returns. The empirical results from an extensive hedging exercise for equity investments illustrate that currency exposures exhibit important time variation, leading to substantial volatility reductions, without sacrificing returns. A risk-averse investor is willing to pay several hundred basis points to switch from exisiting hedging methods to the proposed dynamic strategies.
  5. On the Informational Efficiency of Option-Implied and Time Series Forecasts of Realized Volatility
    • with Torben G. Andersen.
    • First draft: February 2014. Currently being revised. Draft available upon request.