Selected Work

We propose two methods based on the functional principal component analysis (FPCA) to estimate smooth derivatives for a sample of observed curves with a multidimensional domain. We apply the eigendecomposition to a) the dual covariance matrix of the derivatives; b) the dual covariance matrix of the observed curves, and take derivatives of their eigenfunctions. To handle noisy and discrete observations, we rely on local polynomial regression. We show that if the curves are contained in a finite-dimensional function space, the second method performs better asymptotically. We apply our methodology in simulations and an empirical study of option implied state price density surfaces. Using call data for the DAX 30 stock index between 2002 and 2011, we identify three components that are interpreted as volatility, skewness and tail factors, and we find evidence of term structure variation.
Statistica Sinica, 2018: 28, 2469-2496
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Supported by several recent investigations, the empirical pricing kernel (PK) puzzle might be considered as a stylized fact. Based on an economic model with reference-dependent preferences for the financial investors, we emphasize a microeconomic view that explains the puzzle via state-dependent aggregate preferences. We also investigate how the shape of the PK estimated from option and stock market index returns changes in relation to the volatility risk premium.
Review of Finance, 2017: 21 (1), 269-298
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Several empirical studies reported that pricing kernels exhibit a common pattern across different markets. The main interest in pricing kernels lies in validating the presence of the peaks and their variability in location among curves. Motivated by this observation we investigate the problem of estimating pricing kernels based on the shape invariant model, a semi-parametric approach used for multiple curves with shape-related nonlinear variation. This approach allows us to capture the common features contained in the shape of the functions and at the same time characterize the nonlinear variability with a few interpretable parameters. These parameters provide an informative summary of the curves and can be used to make a further analysis with macroeconomic variables. Implied risk aversion function and utility function can also be derived. The method is demonstrated with the European options and returns values of the German stock index DAX.
Journal of Financial Econometrics, 2013: 11 (2), 370-399
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Publication List

Peer-Reviewed Articles

  • Functional Principal Component Analysis for Derivatives of Multivariate Curves
    Statistica Sinica, 2018: 28, 2469-2496

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  • Reference-Dependent Preferences and the Empirical Pricing Kernel Puzzle
    Review of Finance, 2017: 21 (1), 269-298

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  • Shape Invariant Modeling of Pricing Kernels and Risk Aversion
    Journal of Financial Econometrics, 2013: 11 (2), 370-399

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Working Papers

  • Dynamic Analysis of Multivariate Time Series Using Wavelet Dependence Graphs
    Working Paper, 2017

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  • Option Implied Stock Return Distribution
    Working Paper, 2016

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Book Chapters

  • Nonparametric Estimation of Risk-Neutral Densities
    In Handbook of Computational Finance, Jin-Chuan Duan, James E. Gentle, and Wolfgang Härdle (eds). Springer Verlag, 2011, 277-305

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  • Parametric Estimation of Risk Neutral Density Functions
    In Handbook of Computational Finance, Jin-Chuan Duan, James E. Gentle, and Wolfgang Härdle (eds), Springer Verlag, 2011, 253-275

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