Download or read book Time Varying Adaptive Copulae and Dynamic Semiparametic Factor Models with Applications in Finance written by Enzo Giacomini and published by . This book was released on 2009 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Contributions to Static and Time varying Copula based Modeling of Multivariate Association written by Martin Ruppert and published by BoD – Books on Demand. This book was released on 2012 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Putting a particular emphasis on nonparametric methods that rely on modern empirical process techniques, the author contributes to the theory of static and time-varying stochastic models for multivariate association based on the concept of copulas. These functions enable a profound understanding of multivariate association, which is pivotal for judging whether a large set of risky assets entails diversification effects or aggravates risk from an entrepreneurial point of view. Since serial dependence is a stylized fact of financial time series, an asymptotic theory for estimating the structure of association in this context is developed under weak assumptions. A new measure of multivariate association, based on a notion of distance to stochastic independence, is introduced. Asymptotic results as well as hypothesis tests are established which are directly applicable to important types of multivariate financial time series. To ensure that risk management properly captures the current structure of association, it is crucial to assess the constancy of the structure. Therefore, nonparametric tests for a constant copula with either a specified or unspecified change point (candidate) are derived. The thesis concludes with a study of characterizations of association between non-continuous random variables.

Download or read book Copulae and Multivariate Probability Distributions in Finance written by Alexandra Dias and published by Routledge. This book was released on 2013-08-21 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Portfolio theory and much of asset pricing, as well as many empirical applications, depend on the use of multivariate probability distributions to describe asset returns. Traditionally, this has meant the multivariate normal (or Gaussian) distribution. More recently, theoretical and empirical work in financial economics has employed the multivariate Student (and other) distributions which are members of the elliptically symmetric class. There is also a growing body of work which is based on skew-elliptical distributions. These probability models all exhibit the property that the marginal distributions differ only by location and scale parameters or are restrictive in other respects. Very often, such models are not supported by the empirical evidence that the marginal distributions of asset returns can differ markedly. Copula theory is a branch of statistics which provides powerful methods to overcome these shortcomings. This book provides a synthesis of the latest research in the area of copulae as applied to finance and related subjects such as insurance. Multivariate non-Gaussian dependence is a fact of life for many problems in financial econometrics. This book describes the state of the art in tools required to deal with these observed features of financial data. This book was originally published as a special issue of the European Journal of Finance.

Download or read book Dynamic Semiparametric Factor Models written by Szymon Borak and published by . This book was released on 2008 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Time Series in High Dimension the General Dynamic Factor Model written by Marc Hallin and published by World Scientific Publishing Company. This book was released on 2020-03-30 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.

Download or read book Time varying General Dynamic Factor Models and the Measurement of Financial Connectedness written by Matteo Barigozzi and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Multivariate GARCH and Dynamic Copula Models for Financial Time Series written by Martin Grziska and published by Pro BUSINESS. This book was released on 2015-02-05 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk measures. Furthermore, the different dependence models are used to analyze the integration of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model. The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no single parametric model dominates all other models for all portfolios and risk measures. The investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less integrated into the world economy than proprietary. Thus, secondary emerging markets are moresuitable to diversify a portfolio consisting of developed equity or bond indices than proprietary.

Download or read book Applications of Time varying parameter Models to Economics and Finance written by Peng Huang (Ph. D.) and published by . This book was released on 2006 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on applying time-varying-parameter models to the field of financial and monetary economics. The first two essays analyze the cross-sectional returns on the U.S. stock market by emphasizing the dynamics of risk loadings. The third essay studies the impact of a tight monetary policy on weak currencies during financial crises by examining the time-varying relationship between interest rates and exchange rates. Motivated by the pricing errors found in small size and low book-to-market ratio portfolios in the Fama-French three-factor model, the first essay proposes a time-varying four-factor model. As small size and low book-to-market ratio firms are more sensitive to the risk related to innovations in the discount rate, the model incorporates a new risk factor to capture the information about the discount-rate risk for which the Fama-French three factors cannot fully account. In addition, the investors' learning process mimicked by the Kalman filter procedure is used to model the evolution of risk loadings. The results indicate that the model outperforms the Fama-French three-factor model in explaining the cross-sectional returns by substantially reducing pricing errors. The second essay analyzes the risk-return relationship in a capital asset pricing model (CAPM) with a time-varying beta estimated by adaptive least squares (ALS) based on Kalman foundations. The results show the presence of a significant and positive risk-return relationship in the up market and the presence of a significant and negative risk-return relationship in the down market. In comparison with the model that assumes a constant beta, the CAMP with a time-varying beta reduces unexplained returns and improves the accuracy of the estimated risk-return relationship. The third essay investigates the use of interest rates as a monetary instrument to stabilize exchange rates in the Asian financial crisis. Since previous studies suggest that the interest-exchange rate relationship may vary within, or across, regimes, a time-varying-parameter model with generalized autoregressive conditional heteroskedastic (GARCH) disturbances is used to estimate the impact of raising interest rates on exchange rates. The empirical evidence shows that an increase in interest rates leads to currency depreciation during certain periods of financial crises.

Download or read book Copulae in Mathematical and Quantitative Finance written by Piotr Jaworski and published by Springer Science & Business Media. This book was released on 2013-06-18 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Copulas are mathematical objects that fully capture the dependence structure among random variables and hence offer great flexibility in building multivariate stochastic models. Since their introduction in the early 1950s, copulas have gained considerable popularity in several fields of applied mathematics, especially finance and insurance. Today, copulas represent a well-recognized tool for market and credit models, aggregation of risks, and portfolio selection. Historically, the Gaussian copula model has been one of the most common models in credit risk. However, the recent financial crisis has underlined its limitations and drawbacks. In fact, despite their simplicity, Gaussian copula models severely underestimate the risk of the occurrence of joint extreme events. Recent theoretical investigations have put new tools for detecting and estimating dependence and risk (like tail dependence, time-varying models, etc) in the spotlight. All such investigations need to be further developed and promoted, a goal this book pursues. The book includes surveys that provide an up-to-date account of essential aspects of copula models in quantitative finance, as well as the extended versions of talks selected from papers presented at the workshop in Cracow.

Download or read book The Adaptive Multi factor Model and the Financial Market written by Liao Zhu and published by . This book was released on 2020 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern evolvements of the technologies have been leading to a profound influence on the financial market. The introduction of constituents like Exchange-Traded Funds, and the wide-use of advanced technologies such as algorithmic trading, results in a boom of the data which provides more opportunities to reveal deeper insights. However, traditional statistical methods always suffer from the high-dimensional, high-correlation, and time-varying instinct of the financial data. In this dissertation, we focus on developing techniques to stress these difficulties. With the proposed methodologies, we can have more interpretable models, clearer explanations, and better predictions. We start from proposing a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities. Since the collection of basis assets is large and highly correlated, high-dimension methods are used. The AMF model along with the GIBS algorithm is shown to have significantly better fitting and prediction power than the Fama-French 5-factor model. Next, we do the time-invariance tests for the betas for both the AMF model and the FF5 in various time periods. We show that for nearly all time periods with length less than 6 years, the $\beta$ coefficients are time-invariant for the AMF model, but not the FF5 model. The $\beta$ coefficients are time-varying for both AMF and FF5 models for longer time periods. Therefore, using the dynamic AMF model with a decent rolling window (such as 5 years) is more powerful and stable than the FF5 model. We also successfully provide a new explanation of the well-known low-volatility anomaly which pervades in the finance literature for a long time. We use the Adaptive Multi-Factor (AMF) model estimated by the Groupwise Interpretable Basis Selection (GIBS) algorithm to find those basis assets significantly related to low and high volatility portfolios. These two portfolios load on very different factors, which indicates that volatility is not an independent risk, but that it is related to existing risk factors. The out-performance of the low-volatility portfolio is due to the (equilibrium) performance of these loaded risk factors. For completeness, we compare the AMF model with the traditional Fama-French 5-factor (FF5) model, documenting the superior performance of the AMF model.

Download or read book Applications of State Space Models in Finance written by Sascha Mergner and published by Universitätsverlag Göttingen. This book was released on 2009 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models play a key role in the estimation of time-varying sensitivities in financial markets. The objective of this book is to analyze the relative merits of modern time series techniques, such as Markov regime switching and the Kalman filter, to model structural changes in the context of widely used concepts in finance. The presented material will be useful for financial economists and practitioners who are interested in taking time-variation in the relationship between financial assets and key economic factors explicitly into account. The empirical part illustrates the application of the various methods under consideration. As a distinctive feature, it includes a comprehensive analysis of the ability of time-varying coefficient models to estimate and predict the conditional nature of systematic risks for European industry portfolios.

Download or read book Dynamic Factor Models written by Siem Jan Koopman and published by Emerald Group Publishing Limited. This book was released on 2016-01-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.

Download or read book Dynamic Semiparametric Factor Model in Applications to FMRI and Interest Rates written by Piotr Majer and published by . This book was released on 2014 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Linear Factor Models in Finance written by John Knight and published by Elsevier. This book was released on 2004-12-01 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: The determination of the values of stocks, bonds, options, futures, and derivatives is done by the scientific process of asset pricing, which has developed dramatically in the last few years due to advances in financial theory and econometrics. This book covers the science of asset pricing by concentrating on the most widely used modelling technique called: Linear Factor Modelling.Linear Factor Models covers an important area for Quantitative Analysts/Investment Managers who are developing Quantitative Investment Strategies. Linear factor models (LFM) are part of modern investment processes that include asset valuation, portfolio theory and applications, linear factor models and applications, dynamic asset allocation strategies, portfolio performance measurement, risk management, international perspectives, and the use of derivatives. The book develops the building blocks for one of the most important theories of asset pricing - Linear Factor Modelling. Within this framework, we can include other asset pricing theories such as the Capital Asset Pricing Model (CAPM), arbitrage pricing theory and various pricing formulae for derivatives and option prices. As a bare minimum, the reader of this book must have a working knowledge of basic calculus, simple optimisation and elementary statistics. In particular, the reader must be comfortable with the algebraic manipulation of means, variances (and covariances) of linear combination(s) of random variables. Some topics may require a greater mathematical sophistication.* Covers the latest methods in this area.* Combines actual quantitative finance experience with analytical research rigour* Written by both quantitative analysts and academics who work in this area

Download or read book Copulae in Mathematical and Quantitative Finance written by Piotr Jaworski and published by Springer. This book was released on 2013-06-20 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Copulas are mathematical objects that fully capture the dependence structure among random variables and hence offer great flexibility in building multivariate stochastic models. Since their introduction in the early 1950s, copulas have gained considerable popularity in several fields of applied mathematics, especially finance and insurance. Today, copulas represent a well-recognized tool for market and credit models, aggregation of risks, and portfolio selection. Historically, the Gaussian copula model has been one of the most common models in credit risk. However, the recent financial crisis has underlined its limitations and drawbacks. In fact, despite their simplicity, Gaussian copula models severely underestimate the risk of the occurrence of joint extreme events. Recent theoretical investigations have put new tools for detecting and estimating dependence and risk (like tail dependence, time-varying models, etc) in the spotlight. All such investigations need to be further developed and promoted, a goal this book pursues. The book includes surveys that provide an up-to-date account of essential aspects of copula models in quantitative finance, as well as the extended versions of talks selected from papers presented at the workshop in Cracow.

Download or read book Multi factor Models and Signal Processing Techniques written by Serges Darolles and published by John Wiley & Sons. This book was released on 2013-07-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With recent outbreaks of multiple large-scale financial crises, amplified by interconnected risk sources, a new paradigm of fund management has emerged. This new paradigm leverages “embedded” quantitative processes and methods to provide more transparent, adaptive, reliable and easily implemented “risk assessment-based” practices. This book surveys the most widely used factor models employed within the field of financial asset pricing. Through the concrete application of evaluating risks in the hedge fund industry, the authors demonstrate that signal processing techniques are an interesting alternative to the selection of factors (both fundamentals and statistical factors) and can provide more efficient estimation procedures, based on lq regularized Kalman filtering for instance. With numerous illustrative examples from stock markets, this book meets the needs of both finance practitioners and graduate students in science, econometrics and finance. Contents Foreword, Rama Cont. 1. Factor Models and General Definition. 2. Factor Selection. 3. Least Squares Estimation (LSE) and Kalman Filtering (KF) for Factor Modeling: A Geometrical Perspective. 4. A Regularized Kalman Filter (rgKF) for Spiky Data. Appendix: Some Probability Densities. About the Authors Serge Darolles is Professor of Finance at Paris-Dauphine University, Vice-President of QuantValley, co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His research interests include financial econometrics, liquidity and hedge fund analysis. He has written numerous articles, which have been published in academic journals. Patrick Duvaut is currently the Research Director of Telecom ParisTech, France. He is co-founder of QAMLab SAS, and member of the Quantitative Management Initiative (QMI) scientific committee. His fields of expertise encompass statistical signal processing, digital communications, embedded systems and QUANT finance. Emmanuelle Jay is co-founder and President of QAMLab SAS. She has worked at Aequam Capital as co-head of R&D since April 2011 and is member of the Quantitative Management Initiative (QMI) scientific committee. Her research interests include SP for finance, quantitative and statistical finance, and hedge fund analysis.

Download or read book Conditional Dependency of Financial Series written by Eric Jondeau and published by . This book was released on 2003 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop a new methodology to measure conditional dependency between time series each driven by complicated marginal distributions. We achieve this by using copula functions that link marginal distributions, and by expressing the parameter of the copula as a function of predetermined variables. The marginal model is an autoregressive version of Hansen's (1994) GARCH-type model with time-varying skewness and kurtosis. Here, we extend, to a dynamic setting, the research that focuses on asymmetries in correlation during extreme events. We show that, for many market indices, dependency increases subsequent to large extreme realizations. Furthermore, for several index pairs, this increase is stronger after crashes. Our model has many potential applications such as VaR measurement and portfolio allocation in non-gaussian environments.