71 research outputs found

    Computational resources for extremes

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    The necessity to quantify the risk caused by the high volatility of asset prices, large insurance claims or floods has lead to an increasing interest in extreme value analysis. Generalized Pareto and extreme value distributions are well suited to model data which are exceedances above a threshold or maxima. We describe two statistical software systems - XploRe and Xtremes - that support a user in performing an extreme value analysis. Within both systems, various estimators for the above distributions are provided. We give an overview of their application and mention visual tools to check the adequacy of a parametric modeling by means of non-parametric procedures. Both systems utilize a client/server architecture to provide access to their resources across a network. While the server version of XploRe supports an interactive Java client which can be used from a web browser, the Xtremes system implements a CORBA interface that exports statistical objects to a client program

    Semiparametric bootstrap approach to hypothesis tests and confidence intervals for the hurst coefficient

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    A major application of rescaled adjusted range analysis (RS analysis) is the study of price fluctuations in financial markets. There, the value of the Hurst constant, H, in a time series may be interpreted as an indicator of the irregularity of the price of a commodity, currency or similar quantity. Interval estimation and hypothesis testing for H are central to comparative quantitative Analysis. In this paper we propose a new bootstrap, or Monte Carlo, approach to such problems. Traditional bootstrap methods in this context file based on fitting a process chosen from a wide but relatively conventional range of discrete time series models, including autoregressions, moving averages, autoregressive moving averages and many more. By way of contrast we suggest simulation using a single type of continuous-time process, with its fractal dimension. We provide theoretical justification for this method, and explore its numerical properties and statistical performance by application 1,0 real data on commodity prices and exchange rates

    On Technical Bases and Surplus in Life Insurance

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    We revisit surplus on general life insurance contracts, represented by Markov models. We classify technical bases in terms of boundary conditions in Thiele's equation(s), allowing more general regulations than Scandinavian-style `first-order/second-order' regimes, and replacing the traditional retrospective policy value. We propose a `canonical' model with three technical bases (premium, valuation, accumulation) and show how each pair of bases defines premium loadings and surplus. Along with a `true' or `real-world' experience basis, this expands fundamental results of Ramlau-Hansen (1988a). We conclude with two applications: lapse-supported business; and the retrospectively-oriented regime proposed by M{\o}ller & Steffensen (2007).Comment: 33 pages, 4 Figure

    Fitting Multi-Population Mortality Models to Socio-Economic Groups

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    Longevity trend risk over limited time horizons

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    Semiparametric Diffusion Estimation and Application to a Stock Market Model

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    The analysis of diffusion process in financial models is crucially dependent on the form of the drift and diffusion coefficient functions. A methodology is proposed for estimating and testing coefficient functions for ergodic diffusions that are not directly observable. It is based on semiparametric and nonparametric estimates. The testing is performed via the wild bootstrap resampling technique. The method is illustrated on S&P 500 index.diffusion; identification; continuous-time financial models; semi-parametric methods; kernel smoothing; bootstrap

    Will genetic test results be monetized in life insurance?

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    If life insurers are not permitted to use genetic test results in underwriting, they may face adverse selection. It is sometimes claimed that applicants will choose abnormally high sums insured as a form of financial gamble, possibly financed by life settlement companies (LSCs). The latter possibility is given some credence by the recent experience of “stranger‐originated life insurance” (STOLI) in the United States. We examine these claims, and find them unconvincing for four reasons. First, apparently high mortality implies surprisingly high probabilities of surviving for decades, so the gamble faces long odds. Second, LSCs would have to adopt a different business model, involving much longer time horizons. Third, STOLI is being effectively dealt with by the U.S. courts. Fourth, the gamble would be predicated upon a deep understanding of the genetic epidemiology, which is evolving, subject to uncertain biases, and cannot predict the emergence of effective treatments

    Testing continuous time models in financial markets

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    Das Ziel der Dissertation ist die Entwicklung statistischer Testverfahren zur Überprüfung parametrischer Modelle für die Dynamik zeitstetiger Prozesse und die Anwendung der entwickelten Methoden auf Finanzmarktdaten. Besonderes Augenmerk wird auf die statistische Methodik und die Untersuchung der Testeigenschaften in endlichen Stichproben gelegt, da diese in empirischen Untersuchungen von entscheidener Bedeutung sind. Alle Kapitel der Dissertation umfassen eine empirische Analyse, in der die vorgestellten Tests auf Finanzmarktdaten angewandt werden.The aim of the thesis is to provide a wide range of statistical methods designed to test parametric assumptions about the evolution of continuous time processes in financial markets. The main focus is on the statistical methodology and the investigation of the properties of the proposed methods when applied to finite samples. The latter aspect is particularly important for empirical applications. All chapters include an empirical analysis of financial data using the developed methods

    Client/Server based Statistical Computing

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    We propose a client server architecture for statistical computing. The main feature of our approach is the possibility to connect various client programs via a TCP/IP connection to a powerful statistical engine. This offers the opportunity to include the statistical engine into a number of software packages and to empower the user of these packages to access a modern statistical programming environment. It also allows for the development of specialized client programs for particular tasks. TCP/IP permits a client/server connection with the client and server running on different hosts (remote host) as well as running both applications on the same computer (local host). To have a large flexibility we suggest adding a middleware program managing the communication between Server and Client. This avoids the need to implement TCP/IP communication methods on the server side. The paper provides an overview of the desired environment and illustrates the general structure by the implementation of the XploRe Quantlet Client and XploRe Quantlet Server
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