Selected Work

We present two methods based on functional principal component analysis (FPCA) for the estimation of smooth derivatives of a sample of random functions, which are observed in a more than one-dimensional domain. We apply eigenvalue decomposition to a) the dual covariance matrix of the derivatives, and b) the dual covariance matrix of the observed curves. To handle noisy data from discrete observations, we rely on local polynomial regressions. If curves are contained in a finite-dimensional function space, the second method performs better asymptotically. We apply our methodology in a simulation and empirical study, in which we estimate state price density (SPD) surfaces from call option prices. We identify three main components, which can be interpreted as volatility, skewness and tail factors. We also find evidence for term structure variation.
Discussion Paper. Submitted to Statistica Sinica, 2016

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

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

Publication List

Peer-Reviewed Articles

  • 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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  • Functional Principal Component Analysis for Derivatives of Multivariate Curves
    Discussion Paper. Submitted to Statistica Sinica, 2016

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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, pp 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, pp 253-275

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