17 search hits
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Decision Support in Financial Markets Using System Dynamics
(2010)
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Björn-Christopher Witte
Christian Suchan
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Trading Strategies with Partial Access to the Derivatives Market
(2010)
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Matthias Muck
- This research analyzes tradingstrategies with derivatives when there are several assets and risk factors. We investigate portfolio improvement if investors have full and partialaccess to the derivativesmarkets, i.e. situations in which derivatives are written on some but not all stocks or risk factors traded on the market. The focus is on markets with jump risk. In these markets the choice of optimal exposures to jump and diffusion risk is linked. In a numerical application we study the potential benefit from adding derivatives to the market. It turns out that e.g. diffusion correlation and volatility or jump sizes may have a significant impact on the benefit of a new derivative product even if market prices of risk remain unchanged. Given the structure of risk investors may have different preferences for making risk factors tradable. Utility gains provided by new derivatives may be both increasing or decreasing depending on the type of contract added.
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The political click : political participation through e-petitions in Germany
(2010)
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Andreas Jungherr
Pascal Jürgens
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Picking the Right One in a Transient World
(2010)
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Dan Schultz
Andreas Jungherr
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New methods for generating significance levels from multiply-imputed data
(2010)
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Christine Licht
- Missing data are a ubiquitous problem in statistical analyses that has become an important research field in applied statistics. A highly useful technique to handle missing values in many settings is multiple imputation, that was first proposed by Rubin (1977, 1978) and extended in Rubin (1987). Due to the ongoing improvement in computer power in the last 10 years, multiple imputation has become a well known and often used tool in statistical analyses. However, obtaining significance levels from multiply-imputed data is still a problem, because the application of multiple imputation requires normally distributed or t-distributed complete-data estimators. Today there are basically three methods that extend the suggestions given in Rubin (1987). First, Li, Raghunathan, and Rubin (1991) proposed a procedure, in which significance levels are created by computing a modified Wald-test statistic that is then referred to an F-distribution. This procedure is essentially calibrated, but it requires access to the completed-data estimates and their variance-covariance matrices that may not be available with standard software. Second, Meng and Rubin (1992) proposed a complete-data two-stage-likelihood-ratio-test-based procedure that requires access to the code for the calculation of the log-likelihood-ratio statistics. Common statistical software does not provide access to the code in their standard analyses routines. Third, Li, Meng, Raghunathan, and Rubin (1991) developed an improved version of a method in Rubin (1987) that only requires the chi-square-statistics from a usual complete-data Wald-test. This method is only approximately calibrated and has a substantial loss of power compared to the previous two. There thus exist several procedures to generate significance levels in general from multiply-imputed data, but none of them has satisfactory applicability. Since many statistical analyses are based on hypothesis tests, especially on the Wald-test in regression analyses, it is very important to find a method that retains the advantages and overcomes the disadvantages of the existing procedures. Developing such a method was the aim of the present thesis. After providing a short introduction to multiple imputation theory and the existing methods to generate significance levels from multiply-imputed data, we present a new procedure that is based on a z-transformation. We analytically show that the z-transformation works for a one-sided z-test. By simulation we show that our new z-transformation is working well for all one-dimensional tests, because they can be linked to an F-test or to a z-test, respectively. Despite the success of this new z-transformation procedure in several practical settings, problems arise when multi-dimensional tests are performed. We develop and discuss a possible procedure to fix these problems. Based on a comprehensive simulation study we discover an interesting general statistical problem: Using a chi-square-distribution rather than an F-distribution, can lead to a not negligible error for small sample sizes, especially with larger dimensions of the estimator. This problem seems to be unnoticed until now. In addition, we show the influence of the sample size for generating accurate significance levels from multiply imputed data. We present an adjusted procedure, the componentwise-moment-based method, to easily calculate correct significance levels from multiply-imputed data under some assumptions. This procedure is related to one of the three existing methods and uses the small-sample degrees of freedom given in Barnard and Rubin (1999) componentwise. We examine this new method and the already existing procedures in detail and compare them with each other by an extensive simulation study with 55296 different situations given by the factorial design of our simulation study. We also compare the results with former simulation studies of Li, Raghunathan, Meng, and Rubin (1991, 1992), where they simulated draws from the theoretically calculated distributions of the test statistics. To analyze the behaviour of the four methods we perform an ANOVA to identify the most important factors for the distribution of the multiple imputation p-values. Afterwards we examine the rejection rates of the four methods with their originally proposed degrees of freedom and some further "method and degrees-of-freedom"-combinations and compare them with the particular nominal levels. We summarize the results of the simulation study in detail. Based on these results we give some practical advices for the data collector, for the imputer and for the data analyst, about how to calculate correct significance levels from multiply-imputed data. Finally, we give an overview of challenging tasks left for future research.
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Risk Management in Cross-Border Electricity Markets in Europe - Analysis and Valuation of Derivative Instruments
(2010)
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Jan Marckhoff
- The thesis thoroughly discusses the valuation of derivatives in the European cross-border electricity market. It covers two distinct electricity markets in Europe and discusses the two most important types of derivatives within the European cross-border electricity market. Moreover, this thesis introduces the relevant risk factors associated with cross-border markets and estimates these risk-factors based on the major derivative instruments. Finally, it delivers a profound analysis of the valuation of these contracts. In the first part of the thesis, we analyze the ex-post risk premia of CfDs traded at Nord Pool. It is shown that CfDs contain significant risk premia that substantially vary in both sign and magnitude across market areas. We then investigate the development of these risk premia over time-to-maturity and identify their main economic drivers. Results show a strong coherency between ex-post risk premia and time-to-maturity. Although not significant for CfDs, this relation is highly significant for implied area and system forwards, the two constituents of CfDs. In addition, we identify a strong relationship between risk premia and the variance and skewness of the underlying spot prices and also find a significant impact of hydropower on spot prices and risk premia in the Nordic market. In the second part, this thesis discusses the valuation of hourly PTRs for the German-Dutch interconnector. We propose a spike-diffusion model and estimate its physical and risk-neutral parameters using the method of Markov Chain Monte Carlo (MCMC). Using those parameters, we compare the empirical and risk-neutral densities for the underlying price spreads. Our results show first of all that the spike-diffusion model adequately describes the underlying PTR prices especially during calm hours. Second, the estimated parameters show that during calm hours PTRs are traded at a discount, whereas market participants are willing to pay a premium for PTRs during turbulent hours. The premium implicit in those PTRs can be explained by either increased hedging demand or speculation of market participants. Furthermore, we find evidence for seasonality in the residuals of hourly and monthly PTR option prices. For monthly PTRs and hourly PTRs during turbulent hours, this seasonality is strongly related to jumps in the underlying spread. This result, in contrast to prior work, is the first based on not only spot but also option prices and offers further insights in the valuation of derivatives in cross-border as well as national electricity markets.
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Schema-Guided Inductive Functional Programming through Automatic Detection of Type Morphisms
(2010)
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Martin Hofmann
- Inductive functional programming systems can be characterised by two diametric approaches: Either they apply exhaustive program enumeration which uses input/output examples (IO) as test cases, or they perform an analytical, data-driven structural generalisation of the IO examples. Enumerative approaches ignore the structural information provided with the IO examples, but use type information to guide and restrict the search. They use higher-order functions which capture recursion schemes during their enumeration, but apply them randomly in a uninformed manner. Analytical approaches on the other side heavily exploit this structural information, but have ignored the benefits of a strong type system so far and use only recursion schemes either fixed and built in, or selected by an expert user. In category theory universal constructs, such as natural transformations or type morphisms, describe recursion schemes which can be defined on any inductively defined data type. They can be characterised by specific universal properties. Those type morphisms and related concepts provide a categorical approach to functional programming, which is often called categorical programming. This work shows how categorical programming can be applied to Inductive Programming and how universal constructs, such as catamorphisms, paramorphisms, and type functors, can be used as recursive program schemes for inductive functional programming. The use of program schemes for Inductive Programming is not new. The special appeal and novelty of this work is that, contrary to previous approaches, the program schemes are neither fixed, nor selected by an expert user: The applicability of those recursion schemes can be automatically detected in the given IO examples of a target function by checking the universal properties of the corresponding type morphisms. Applying this to the analytical system Igor2, both the capabilities and the expressiveness can be extended without a decrease in efficiency. An extension of the analytical functional inductive programming system Igor2 is proposed and its algorithms described. An empirical evaluation demonstrates the improvements with respect to efficiency and effectiveness that can be achieved by the use of type morphisms for Igor2 due to a reduction in search space complexity.
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A Combined Analytical and Search-Based Approach to the Inductive Synthesis of Functional Programs
(2010)
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Emanuel Kitzelmann
- This thesis is concerned with the inductive synthesis of recursive declarative programs and in particular with the analytical inductive synthesis of functional programs. Program synthesis addresses the problem of (semi-)automatically generating computer programs from specifications. In inductive program synthesis, recursive programs are constructed by generalizing over incomplete specifications such as finite sets of input/output examples (I/O examples). Classical methods for induction of functional programs are analytical, that is, a recursive function definition is derived by detecting and generalizing recurrent patterns between the given I/O examples. Most recent methods, on the other side, are generate-and-test based, that is, they repeatedly generate programs independently from the provided I/O examples until a program is found that correctly computes the examples. Analytical methods are much faster than generate-and-test methods, because they do not rely on search in a program space. Therefore, however, the schemas that generatable programs conform to, must be much more restricted. This thesis at first provides a comprehensive overview of current approaches and methods to inductive program synthesis. Then we present a new algorithm to the inductive synthesis of functional programs that generalizes the analytical approach and combines it with search in a program space. Thereby, the strong restrictions of analytical methods can be resolved for the most part. At the same time, applying analytical techniques allows for pruning large parts of the problem space so that solutions can often be found faster than with generate-and-test methods. By means of several experiments with an implementation of the described algorithm, we demonstrate its capabilities.
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Analyzing the Interoperability of WS-Security and WS-ReliableMessaging Implementations
(2010)
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Johannes Schwalb
Andreas Schönberger
- Since their invention as lightweight integration technology about a decade ago, Web Services have matured significantly. Today, major middleware solution vendors as well as industry communities like RosettaNet are propagating Web services even for exchanging business-critical data and implementing inter-organizational business processes. Core enablers for using Web services in this domain are stateful interactions using the Web Services Business Process Execution Language (WS-BPEL) as well as advanced communication features like security and reliability using the WS-Security and WS-ReliableMessaging standard specifications. However, advanced communication features come at the price of complexity which challenges interoperability across different Web services stack implementations. Interoperability, in turn, is a predominant requirement for an integration technology such as Web services, in particular if inter-organizational business processes are supposed to be implemented on top of that technology. This paper approaches the problem of testing the interoperability of the so-called WS-* standards, advanced Web services communication features that are typically defined as SOAP extensions and configured using WS-Policy. Being essential to business process integration, WS-Security and WS-ReliableMessaging are selected as representatives of this group and the two major Java-based Web services stack implementations Metro and Axis2 are tested for interoperability. We operationalize the notion of interoperability for testing WS-* standards, suppose an approach for deriving test cases from WS-* specifications as well as a method for performing the test cases, and we provide a comprehensive interoperability review of the two selected Web services stack implementations.
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Multiple Imputation of Censored Variables
(2010)
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Thomas Büttner
- Censoring of variables is a common problem with microdata. This situation often arises with wage and income variables due to manifold reasons. The data may not be available due to difficulties during the data collection process, it may be artificially censored to ensure confidentiality, or it may just not be reliable because of high wage earners tending above average not to answer income questions. An important example for this problem is the German IAB Employment Sample (IABS), which is based on administrative data from the social security systems. Here, right-censoring of wages occurs due to the contribution limit in the German social security system. If earnings are to be analyzed from right-censored or top-coded data, standard models cannot be applied. We treat this problem as a missing data problem and use multiple imputation approaches to impute the censored wages by draws of a random variable from a truncated distribution, based on Markov chain Monte Carlo techniques. In this dissertation thesis new single and multiple imputation methods allowing for heteroscedasticity are suggested. Whereas one goal of the thesis is to present new imputation approaches that are applicable for right-censored wages, a main objective is also to confirm the validity of multiple imputation approaches for right-censored wages in general and to show the superiority of the new multiple imputation approach considering heteroscedasticity in a wide range of situations. To assess the validity of this approach, we also develop alternative approaches using uncensored wage information from a survey (German Structure of Earnings Survey, GSES). Simulation studies are performed to compare the different imputation approaches under different situations and to show the superiority of the new approach working without external information. Additionally, analyses that were done with the IABS are replicated to demonstrate the validity of imputed wage data.