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Author: Spokoiny, Vladimir × End date: 2009 × Start date: 2000 × Type: report ×

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Now showing 1 - 10 of 11
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    Effiziente Methoden zur Bestimmung von Risikomaßen : Schlussbericht ; Projekt des BMBF-Förderprogramm "Neue Mathematische Verfahren in Industrie und Dienstleistungen"
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2004) Schoenmakers, John; Spokoiny, Vladimir; Reiß, Oliver; Zacherias-Langhans, Johan-Hinrich
    [no abstract available]
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    Inhomogeneous dependence modelling with time varying copulae
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2007) Giacomini, Enzo; Härdle, Wolfgang; Spokoiny, Vladimir
    Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for non-Gaussian dependence. The correct modelling of non-Gaussian dependences is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of portfolios and show their better performance over the RiskMetrics approach, a widely used methodology for VaR estimation.
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    Parameter estimation in time series analysis
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2009) Spokoiny, Vladimir
    The paper offers a novel unified approach to studying the accuracy of parameter estimation for a time series. Important features of the approach are: (1) The underlying model is not assumed to be parametric. (2) The imposed conditions on the model are very mild and can be easily checked in specific applications. (3) The considered time series need not to be ergodic or stationary. The approach is equally applicable to ergodic, unit root and explosive cases. (4) The parameter set can be unbounded and non-compact. (5) No conditions on parameter identifiability are required. (6) The established risk bounds are nonasymptotic and valid for large, moderate and small samples. (7) The results describe confidence and concentration sets rather than the accuracy of point estimation. The whole approach can be viewed as complementary to the classical one based on the asymptotic expansion of the log-likelihood. In particular, it claims a consistency of the considered estimate in a rather general sense, which usually is assumed to be fulfilled in the asymptotic analysis. In standard situations under ergodicity conditions, the usual rate results can be easily obtained as corollaries from the established risk bounds. The approach and the results are illustrated on a number of popular time series models including autoregressive, Generalized Linear time series, ARCH and GARCH models and meadian/quantile regression.
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    Locally time homogeneous time series modelling
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2008) Elagin, Mstislav; Spokoiny, Vladimir
    In this paper three locally adaptive estimation methods are applied to the problems of variance forecasting, value-at-risk analysis and volatility estimation within the context of nonstationary financial time series. A general procedure for the computation of critical values is given. Numerical results exhibit a very reasonable performance of the methods.
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    Exponential bounds for the minimum contrast with some applications
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2007) Golubev, Yuri; Spokoiny, Vladimir
    The paper studies parametric minimum contrast estimates under rather general conditions. The quality if estimation is measured by the rate function related to the contrast which allows for stating the results without specifying the particular parametric structure of the model. This approach permits also to go far beyond the classical i.i.d. case and to obtain nonasymptotic upper bounds for the risk. These bounds apply even for small or moderate samples. They also cover the case of misspecified parametric models. Another important feature of the approach is that it works well in the case when the parametric set can be unbounded and non-compact. In the case of a smooth contrast, the obtained exponential bounds do not rely on the covering numbers and can be easily computed. We also illustrate how these bound can be used for statistical inference: bounding the estimation risk, constructing the confidence sets for the underlying parameters, establishing the concentration properties of the minimum contrast estimate. The general results are specified to the case of a Gaussian contrast and of an i.i.d. sample. We also illustrate the approach by several popular examples including least squares and least absolute deviation contrasts and the problem of estimating the location of the change point. What we obtain in these examples slightly differs from usual asymptotic results known in the classical literature. This difference is due to the unboundness of the parameter set and a possible model misspecification.
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    In search on non-Gaussian components of a high-dimensional distribution
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2006) Blanchard, Gilles; Kawanabe, Motoaki; Sugiyama, Masashi; Spokoiny, Vladimir; Müller, Klaus-Robert
    Finding non-Gaussian components of high-dimensional data is an important preprocessing step for efficient information processing. This article proposes a new em linear method to identify the "non-Gaussian subspace'' within a very general semi-parametric framework. Our proposed method, called NGCA (Non-Gaussian Component Analysis), is essentially based on the fact that we can construct a linear operator which, to any arbitrary nonlinear (smooth) function, associates a vector which belongs to the low dimensional non-Gaussian target subspace up to an estimation error. By applying this operator to a family of different nonlinear functions, one obtains a family of different vectors lying in a vicinity of the target space. As a final step, the target space itself is estimated by applying PCA to this family of vectors. We show that this procedure is consistent in the sense that the estimaton error tends to zero at a parametric rate, uniformly over the family, Numerical examples demonstrate the usefulness of our method
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    Forward and reverse representations for Markov chains
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2006) Milstein, Grigori N.; Schoenmakers, John G.M.; Spokoiny, Vladimir
    In this paper we carry over the concept of reverse probabilistic representations developed in Milstein, Schoenmakers, Spokoiny (2004) for diffusion processes, to discrete time Markov chains. We outline the construction of reverse chains in several situations and apply this to processes which are connected with jump-diffusion models and finite state Markov chains. By combining forward and reverse representations we then construct transition density estimators for chains which have root-N accuracy in any dimension and consider some applications.
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    Regression methods in pricing American and Bermudan options using consumption processes
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2006) Belomestny, Denis; Milstein, Grigor N.; Spokoiny, Vladimir
    Here we develop methods for efficient pricing multidimensional discrete-time American and Bermudan options by using regression based algorithms together with a new approach towards constructing upper bounds for the price of the option. Applying sample space with payoffs at the optimal stopping times, we propose sequential estimates for continuation values, values of the consumption process, and stopping times on the sample paths. The approach admits constructing both low and upper bounds for the price by Monte Carlo simulations. The methods are illustrated by pricing Bermudan swaptions and snowballs in the Libor market model.
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    Diffusion tensor imaging : structural adaptive smoothing
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2007) Tabelow, Karsten; Polzehl, Jörg; Spokoiny, Vladimir; Voss, Henning U.
    Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking.
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    Robust risk management : accounting for nonstationarity and heavy tails
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2007) Chen, Ying; Spokoiny, Vladimir
    In the ideal Black-Scholes world, financial time series are assumed 1) stationary (time homogeneous) or can be modelled globally by a stationary process and 2) having conditionally normal distribution given the past. These two assumptions have been widely-used in many methods such as the RiskMetrics, one risk management method considered as industry standard. However these assumptions are unrealistic. The primary aim of the paper is to account for nonstationarity and heavy tails in time series by presenting a local exponential smoothing approach, by which the smoothing parameter is adaptively selected at every time point and the heavy-tailedness of the process is considered. A complete theory addresses both issues. In our study, we demonstrate the implementation of the proposed method in volatility estimation and risk management given simulated and real data. Numerical results show the proposed method delivers accurate and sensitive estimates.