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Davide La Vecchia
Former Member
Title
Prof. Ph.D.
Last Name
La Vecchia
First name
Davide
Phone
+41 71 227 2476
Now showing
1 - 6 of 6
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PublicationStable Asymptotics for M-estimatorsWe review some first- and higher-order asymptotic techniques for M- estimators and we study their stability in the presence of data contaminations. We show that the estimating function (psi) and its derivative with respect to the parameter (grad psi) play a central role. We discuss in detail the first-order Gaussian density approximation, saddlepoint density approximation, saddlepoint test, tail area approximation via Lugannani-Rice formula, and empirical saddlepoint density approximation (a technique related to the empirical likelihood method). For all these asymptotics, we show that a bounded (in the Euclidean norm) psi and a bounded (e.g., in the Frobenius norm) grad psi yield stable inference in the presence of data contamination. We motivate and illustrate our findings by theoretical and numerical examples about the benchmark case of one-dimensional location model.Type: journal articleJournal: International statistical reviewVolume: 84Issue: 2DOI: 10.1111/insr.12102
Scopus© Citations 2 -
PublicationRobust heart rate variability analysis by generalized entropy minimizationTypical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion func- tion based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston's Beth Israel Hospital data, an important benchmark data set in HRV analysis.Type: journal articleJournal: Computational Statistics & Data AnalysisVolume: 82Issue: February 2015
Scopus© Citations 6 -
PublicationRealizing smiles: Options pricing with realized volatilityWe develop a discrete-time stochastic volatility option pricing model exploiting the information contained in the Realized Volatility (RV), which is used as a proxy of the unobservable log-return volatility. We model the RV dynamics by a simple and effective long-memory process, whose parameters can be easily estimated using historical data. Assuming an exponentially affine stochastic discount factor, we obtain a fully analytic change of measure. An empirical analysis of Standard and Poor's 500 index options illustrates that our model outperforms competing time-varying and stochastic volatility option pricing models.Type: journal articleJournal: Journal of Financial EconomicsVolume: 107Issue: 2
Scopus© Citations 75 -
PublicationOn robust estimation via pseudo-additive informationWe consider a robust parameter estimator minimizing an empirical approximation to the q-entropy and show its relationship to minimization of power divergences through a simple parameter transformation. The estimator balances robustness and efficiency through a tuning constant q and avoids kernel density smoothing. We derive an upper bound to the estimator mean squared error under a contaminated reference model and use it as a min-max criterion for selecting q.Type: journal articleJournal: BiometrikaVolume: 99Issue: 1
Scopus© Citations 18 -
PublicationHigher-order infinitesimal robustnessUsing the von Mises expansion, we study the higher-order infinitesimal robustness of a general M-functional and characterize its second-order properties. We show that second-order robustness is equivalent to the boundedness of both the estimator's estimating function and its derivative with respect to the parameter. It implies, at the same time, (i) variance-robustness and (ii) robustness of higher-order saddlepoint approximations to the estimator's finite sam- ple density. The proposed construction of second-order robust M-estimators is fairly general and potentially useful in a variety of relevant settings. Besides the theoretical contributions, we discuss the main computational issues and provide an algorithm for the implementation of second-order robust M-estimators. Finally, we illustrate our theory by Monte Carlo simulation and in a real-data estimation of the maximal losses of Nikkei 225 index returns. Our findings indicate that second-order robust estimators can improve on other widely-applied robust esti- mators, in terms of efficiency and robustness, for moderate to small sample sizes and in the presence of deviations from ideal parametric models.Type: journal articleJournal: Journal of the American Statistical Association : JASAVolume: 107Issue: 500
Scopus© Citations 11 -
PublicationInfinitesimal Robustness for DiffusionsWe develop infinitesimally robust statistical procedures for the general diffusion processes. We first prove the existence and uniqueness of the times-series influence function of conditionally unbiased M-estimators for ergodic and stationary diffusions, under weak conditions on the (martingale) estimating function used. We then characterize the robustness of M-estimators for diffusions and derive a class of conditionally unbiased optimal robust estimators. To compute these estimators, we propose a general algorithm, which exploits approximation methods for diffusions in the computation of the robust estimating function. Monte Carlo simulation shows a good performance of our robust estimators and an application to the robust estimation of the exchange rate dynamics within a target zone illustrates the methodology in a real-data application.Type: journal articleJournal: Journal of the American Statistical AssociationVolume: 490Issue: 105