1,720,959 research outputs found
Non-parametric estimators for volatility under market microstructure noise
Get PDFDie Bedeutung der Volatilität nimmt in der Finanzwirtschaft immer mehr zu. Bei der Bepreisung von Optionen im Black-Scholes-Modell ist die Volatilität der einzige nicht messbare Parameter. Daher muss man sich mit Volatilitätsschätzern begnügen. Zu gegebenen Hochfrequenz-Börsendaten ist der naheliegendste Schätzer - die Quadratsumme der Differenz der Trade Prices - die Realized Variance. Das Problem dabei ist, dass dieser Schätzer unter Market Microstructure Noise divergiert. Dies ist ein Effekt, der durch Unstimmigkeiten wie Bid-Ask Bounces oder Messfehler verursacht wird. Dieses Problem kann behoben werden, indem die Samplingfrequenz reduziert wird, wobei dabei teilweise bis zu 99 Prozent der Daten verworfen werden müssen. Da dies nicht optimal ist, wurden eine Vielzahl an Erweiterungen und neue Ansätze für Volatilitätsschätzer entwickelt. Einerseits wird durch Einbeziehung der Autokovarianzen höherer Ordnung und anderseits mittels Kernel-, Fouriermethode oder durch Linearkombinationen über mehrere Skalen versucht, konsistente Schätzer für die Volatilität zu erhalten. Die Frage nach dem in der Praxis am besten geeigneten Volatilitätsschätzer ist nicht einfach zu beantworten. Jeder einzelne Schätzer hat seine Vor- und Nachteile. Konvergenzrate, Rechenaufwand und asymptotisches Verhalten müssen gegeneinander abgewogen werden. Besonders wichtig dabei sind die richtige Implementierung und die bestmögliche Wahl der optimalen Freiheitsparameter. Es bedarf auf jeden Fall einer genauen Analyse der Daten und einer großen Menge an Know-how, um vernünftige Schätzwerte für die Volatilität zu erhalten.In finance the importance of volatility is increasing. When pricing options in the Black-Scholes-Model, volatility is the only parameter that is not measurable. Therefore we need volatility estimators. The obvious estimator for a given time series of high-frequency financial data is the Realized Variance, the sum of squared returns between trades. Unfortunately this estimator leads to divergence in the presence of Market Microstructure Noise, an effect which appears in cause of frictions like bid-ask Bounces or measurement errors. This problem can be solved by reducing the sampling frequency. If sampling sparsely at the optimal frequency, one is throwing away a large amount of data, in particular up to 99 per cent. Due to the fact that sampling sparsely is not optimal, there was need for new classes of volatility estimators. First, estimators that include autocovariances higher order have been developed. Second, methods like the Fourier method, the class of Kernel estimators, and a linear combination over multiple scales emerged. The question concerning the most appropriate volatility estimator is not an easy one. Every estimator itself has his advantages and disadvantages. Convergence rate, computational effort and asymptotic behaviour have to be balanced out. The correct implementation and the right choice of the parameters of freedom is extremely important. It requires a good data analysis and a big amount of know-how to obtain reasonable estimated values for volatility
Multivariate ordinal models in credit risk: Three essays
Get PDFThis dissertation deals with the development, implementation and application of a multivariate statistical framework for credit risk modeling, which is able to incorporate both, default (or failure) information and credit ratings.
Credit risk is the risk of a loss arising from a failure (or default) of a counterparty to meet its contractual obligations (e.g., McNeil et al., 2015). The modeling of credit risk in banks and insurance companies has received considerable attention from academics and practitioners over the last decades. From a regulatory point of view, the Basel Committee on Banking Supervision provides a sophisticated foundation for the assessment of credit risk (Basel I, 1988; Basel II, 2004; Basel III, 2011). According to this regulatory framework, credit risk management and the development of appropriate credit risk models have a crucial relevance for banks and insurance companies, influencing their capital requirements. The financial crisis of 2007-2009 has made the prediction of bankruptcies as well as the understanding of the drivers of creditworthiness an even more urgent matter. Credit rating agencies provide in their credit ratings a forward-looking opinion about the creditworthiness of firms and sovereigns. Even though external credit ratings from the big three players in the credit rating market (Standard and Poor’s (S&P), Moody’s and Fitch) where criticized in the aftermath of the financial crisis, they seem to remain the most common and widely used credit risk measure (Hilscher and Wilson, 2017). Alternatively to credit ratings, internal statistical models based on historical defaults, accounting and market information are often applied when modeling credit risk. Such internal credit risk models serve as a widely-used alternative to credit ratings. Among others Lipton et al. (2012) and Löffler (2013) argue that credit rating agencies react slowly to credit events and are outperformed by failure prediction models in terms of prediction accuracy. Nevertheless in scenarios where defaults are scarce credit ratings serve as an important measure of credit risk and present an alternative to statistical models.
The thesis consists of three research articles. The first paper is concerned with a multivariate extension of ordinal regression models. The model class of multivariate ordinal regression models is motivated by the fact that correlated ordinal data arises naturally when modeling credit ratings.
Existing model specifications are extended in several directions. E.g., we allow for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. Furthermore, in addition to an underlying multivariate normal distribution (multivariate probit link), a multivariate logistic distribution (multivariate logit link) is considered. Moreover, missing observations in the response variables can be dealt with by the model. An estimation algorithm based on composite maximum likelihood methods is implemented and the quality of the estimates is investigated by means of a comprehensive simulation study. The proposed model allows to obtain insights into the rating behaviour of the big three credit rating agencies.
The second research article aims at making the algorithm for the estimation of multivariate ordinal regression models developed in the first paper accessible for the statistical community. A flexible modeling framework for multiple ordinal measurements on the same subject is set up and
implemented in the form of an R package (R Core Team, 2019). The mvord package (Hirk et al., 2019b) is freely available on the “Comprehensive R Archive Network” (CRAN) and enhances the available statistical software for analyzing correlated ordinal data. The flexible and user-friendly model design allows practitioners and researchers, who deal with correlated ordinal data in various areas of application, for different error structures to capture the dependence among the multiple observations. In addition, flexible constraints on the regression coefficients and on the threshold
parameters can be set.
The third paper uses the framework developed and implemented in the first two research articles
to propose a novel multivariate credit risk model, where default or failure information together with rating or expert information are jointly modeled. The proposed credit risk model uses financial variables typically used for bankruptcy predictions to provide probabilities of default conditional on the credit ratings from one or more credit rating agencies. The model is able to account for
missing default and credit rating information. An empirical analysis on a data set of US firms over the period from 1985 to 2014 is conducted. Our findings suggest that the proposed joint modeling framework gives superior prediction accuracy and discriminatory power compared to state-of-the-art failure prediction models and shadow rating approaches
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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