Relevant bibliographies by topics / Copula-based dependence

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Copula-based dependence'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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Journal articles on the topic "Copula-based dependence"

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Yang, Jingping, Zhijin Chen, Fang Wang, and Ruodu Wang. "COMPOSITE BERNSTEIN COPULAS." ASTIN Bulletin 45, no. 2 (March 11, 2015): 445–75. http://dx.doi.org/10.1017/asb.2015.1.

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AbstractCopula function has been widely used in insurance and finance for modeling inter-dependency between risks. Inspired by the Bernstein copula put forward by Sancetta and Satchell (2004, Econometric Theory, 20, 535–562), we introduce a new class of multivariate copulas, the composite Bernstein copula, generated from a composition of two copulas. This new class of copula functions is able to capture tail dependence, and it has a reproduction property for the three important dependency structures: comonotonicity, countermonotonicity and independence. We introduce an estimation procedure based on the empirical composite Bernstein copula which incorporates both prior information and data into the estimation. Simulation studies and an empirical study on financial data illustrate the advantages of the empirical composite Bernstein copula estimation method, especially in capturing tail dependence.
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Vaz de Melo Mendes, Beatriz, and Cecília Aíube. "Copula based models for serial dependence." International Journal of Managerial Finance 7, no. 1 (February 22, 2011): 68–82. http://dx.doi.org/10.1108/17439131111109008.

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Lee, Eun-Joo, Noah Klumpe, Jonathan Vlk, and Seung-Hwan Lee. "Modeling Conditional Dependence of Stock Returns Using a Copula-based GARCH Model." International Journal of Statistics and Probability 6, no. 2 (February 13, 2017): 32. http://dx.doi.org/10.5539/ijsp.v6n2p32.

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Investigating dependence structures of stocks that are related to one another should be an important consideration in managing a stock portfolio, among other investment strategies. To capture various dependence features, we employ copula to overcome the limitations of traditional linear correlations. Financial time series data is typically characterized by volatility clustering of returns that influences an estimate of a stock’s future price. To deal with the volatility and dependence of stock returns, this paper provides procedures of combining a copula with a GARCH model which leads to the construction of a multivariate distribution. Using the copula-based GARCH approach that describes the tail dependences of stock returns, we carry out Monte Carlo simulations to predict a company’s movements in the stock market. The procedures are illustrated in two technology stocks, Apple and Samsung.
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Czado, Claudia, and Thomas Nagler. "Vine Copula Based Modeling." Annual Review of Statistics and Its Application 9, no. 1 (March 7, 2022): 453–77. http://dx.doi.org/10.1146/annurev-statistics-040220-101153.

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With the availability of massive multivariate data comes a need to develop flexible multivariate distribution classes. The copula approach allows marginal models to be constructed for each variable separately and joined with a dependence structure characterized by a copula. The class of multivariate copulas was limited for a long time to elliptical (including the Gaussian and t-copula) and Archimedean families (such as Clayton and Gumbel copulas). Both classes are rather restrictive with regard to symmetry and tail dependence properties. The class of vine copulas overcomes these limitations by building a multivariate model using only bivariate building blocks. This gives rise to highly flexible models that still allow for computationally tractable estimation and model selection procedures. These features made vine copula models quite popular among applied researchers in numerous areas of science. This article reviews the basic ideas underlying these models, presents estimation and model selection approaches, and discusses current developments and future directions.
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Zhou, Jin Yu, Kui Zhou Sun, and Xiu Lian Li. "Reliability Modeling for Symmetric Structure Systems Based on Copulas." Advanced Materials Research 118-120 (June 2010): 319–26. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.319.

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As a new tool of statistical analysis, Copula is introduced to build reliability model for structural system consisting of identical components, by which the complex feature of failure dependence can be depicted. Aiming at symmetric structure systems, typical failure-dependence mechanism of components is discussed firstly. Considering the failure-dependence mechanism, modeling steps based on Gauss Copula and Archimedean Copulas are put forward, in which the twin stress, components strength are chosen as the basic variables and the safety margins are chosen as the analytic variables. Compared with Gauss Copula, Archimedean Copulas have powerful capability of describing the failure-dependence mechanism owing to the adjustable parameters can be determined according to the rank correlation coefficient and the information about the critical failure point. Archimedean Copula-based reliability models are applicable to non-normal situations. A numerical example is given to show that the new method is reasonable and feasible. Copula-based reliability models can give a new path for reliability analysis of complex systems with failure-dependence.
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El Hannoun, Wafaa, Salah-Eddine El Adlouni, and Abdelhak Zoglat. "Vine-Copula-Based Quantile Regression for Cascade Reservoirs Management." Water 13, no. 7 (March 31, 2021): 964. http://dx.doi.org/10.3390/w13070964.

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This paper features an application of Regular Vine (R-vine) copulas, a recently developed statistical tool to assess composite risk. Copula-based dependence modelling is a popular tool in conditional risk assessment, but is usually applied to pairs of variables. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using a wide variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. This study emphasises the use of R-vine copulas in an analysis of the co-dependencies of five reservoirs in the cascade of the Saint-John River basin in Eastern Canada. The developed R-vine copulas lead to the joint and conditional return periods of maximum volumes, for hydrologic design and cascade reservoir management in the basin. The main attraction of this approach to risk modelling is the flexibility in the choice of distributions used to model heavy-tailed marginals and co-dependencies.
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Song, Shuai, Jing Liu, Yongjiu Qian, Fang Zhang, and Gang Wu. "Dependence analysis on the seismic demands of typical components of a concrete continuous girder bridge with the copula technique." Advances in Structural Engineering 21, no. 12 (February 14, 2018): 1826–39. http://dx.doi.org/10.1177/1369433218757234.

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The seismic reliability of a bridge system is significantly affected by the dependence among typical bridge components. This study demonstrates the process of using a copula technique to describe the nonlinear dependence among component seismic demands isolated from their marginal probability distributions. A suite of 100 bridge-ground motion samples were developed with the Latin hypercube sampling approach and bin approach. Based on the incremental dynamic analysis, the tail dependence among component seismic demands at different intensity levels was analyzed with the best-fitting copula function selected by the minimum distance method. In the longitudinal direction, the dependence increased first and then decreased with the ground motion intensity, while the dependence slightly decreased in the transverse direction. At low-intensity levels, the upper tail dependence among components was strong in both directions. At high-intensity levels, the upper and lower tail dependences were weak in the longitudinal direction, while the upper and lower tail dependences were strong in the transverse direction. Compared to the linear correlation coefficient, the copula technique provides an efficient way to describe the tail dependence among component seismic demands and can be used extensively in the seismic reliability analysis of the bridge system.
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Klüppelberg, Claudia, Stephan Haug, and Gabriel Kuhn. "Copula structure analysis based on extreme dependence." Statistics and Its Interface 8, no. 1 (2015): 93–107. http://dx.doi.org/10.4310/sii.2015.v8.n1.a9.

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Ozdemir, Onur, Thomas G. Allen, Sora Choi, Thakshila Wimalajeewa, and Pramod K. Varshney. "Copula Based Classifier Fusion Under Statistical Dependence." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 11 (November 1, 2018): 2740–48. http://dx.doi.org/10.1109/tpami.2017.2774300.

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Liebscher, Eckhard. "Copula-Based Dependence Measures For Piecewise Monotonicity." Dependence Modeling 5, no. 1 (August 28, 2017): 198–220. http://dx.doi.org/10.1515/demo-2017-0012.

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Abstract The aim of the present paper is to develop and examine association coefficients which can be helpfully applied in the framework of regression analysis. The construction of the coeffiecients is connected with the well-known Spearman coeffiecient and extensions of it (see Liebscher [5]). The proposed coeffiecient measures the discrepancy between the data points and a function which is strictly increasing on one interval and strictly decreasing in the remaining domain.We prove statements about the asymptotic behaviour of the estimated coeffiecient (convergence rate, asymptotic normality).
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Dissertations / Theses on the topic "Copula-based dependence"

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Pappada', Roberta. "Copula-based measures of tail dependence with applications." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3424539.

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With the advent of globalization and the recent financial turmoil, the interest for the analysis of dependencies between financial time series has significantly increased. Risk measures such as value-at-risk are heavily affected by the joint extreme comovements of associated risk factors. This thesis suggests some copula-based statistical tools which can be useful in order to have more insights into the nature of the association between random variables in the tail of their distributions. Preliminarily, an overview of important definitions and properties in copula theory is given, and some known measures of tail dependence based on the notion of tail dependence coefficients and rank correlations are introduced. A first proposal consists of a graphical tool based on the so-called tail concentration function, in order to distinguish different families of copulas in a 2D configuration. This can be used as a copula selection tool in practical fitting problems, when one wants to choose one or more copulas to model the dependence structure in the data, highlighting the information contained in the tail. The thesis mainly deals with financial time series applications, where copula functions and the related concepts of tail copula and tail dependence coefficients are used to characterize the dependence structure of asset returns. Classical cluster analysis tools are revisited by introducing suitable copula-based tail dependence measures, which are exploited in the identification of similarities or dissimilarities between the variables of interest and, in particular, between financial time series. Such an approach is designed to investigate the joint behaviour of pairs of time series when they are taking on extremely low values. Either the asymptotic and the finite behaviour are assessed. The proposed methodology is based on a suitable copula-based time series model(GARCH-copula model), in order to model the marginal behaviour of each time series separately from the dependence pattern. Moreover, non-parametric estimation procedures are adopted for describing the pairwise dependencies, thus avoiding any model assumption. Simulation studies are conducted in order to check the performances of the proposed procedures and applications to financial data are presented showing their practical implementation. The information coming from the output of the introduced clustering techniques can be exploited for automatic portfolio selection procedures in order to hedge the risk of a portfolio, by taking into account the occurrence of joint losses. A two-stage portfolio diversification strategy is proposed and empirical analysis are provided. Results show how the suggested approach to the clustering of financial time series can be used by an investor to have more insights into the relationships among different assets in crisis periods. Moreover, the application to portfolio selection framework suggests a cautious usage of standard procedures that may not work when the markets are expected to experience periods of high volatility.
Con l'avvento della globalizzazione e la recente crisi finanziaria, l'interesse verso l'analisi delle relazioni tra serie storiche finanziarie è notevolmente aumentato. Misure di rischio come il value-at-risk sono fortemente influenzate dai movimenti estremi congiunti dei fattori di rischio associati. Nella presente tesi si suggeriscono alcuni strumenti statistici basati sulla nozione di copula, che possono essere utili al fine di ottenere informazioni sulla natura dell'associazione tra variabili casuali nella coda delle loro distribuzioni. Preliminarmente, vengono introdotte definizioni e proprietà fondamentali della teoria delle copule, e discusse alcune note misure di dipendenza basate sul concetto di coefficienti di dipendenza nella coda e correlazioni fra i ranghi. Una prima proposta consiste in uno strumento grafico basato sulla cosiddetta funzione di concentrazione di coda per distinguere tra diverse famiglie di copule in una configurazione bidimensionale. Questo strumento può essere impiegato in problemi pratici, quando si vuole scegliere tra una o più copule per modellizzare la struttura di dipendenza nei dati, evidenziando le informazioni contenute nella coda. La tesi prende in considerazione diverse applicazioni nell'analisi di serie storiche finanziarie, in cui le funzioni copula e i relativi concetti di copule di coda e coefficienti di dipendenza nelle code vengono impiegati per caratterizzare la struttura di dipendenza dei rendimenti finanziari. Gli strumenti standard per l'Analisi dei Gruppi (Cluster Analysis) vengono rivisitati attraverso l'introduzione di opportune misure di dipendenza, che permettano di identificare similarità o dissimilarità tra le quantità di interesse, nello specifico rappresentate da serie finanziarie. Tale approccio ha lo scopo di studiare il comportamento congiunto di coppie di serie finanziarie nel momento in cui esse assumono valori estremamente bassi. Vengono valutate sia la dipendenza asintotica che il comportamento finito. La metodologia proposta utilizza un modello per serie storiche basato sulle copule (GARCH-copula model), che consente di modellizzare il comportamento marginale di ogni serie temporale separatamente dalla struttura di dipendenza. Inoltre, vengono adottate procedure di stima non parametriche in relazione alla struttura di dipendenza, evitando così qualunque assunzione sul modello. Vengono condotti degli studi di simulazione per testare le procedure proposte e diverse applicazioni a dati finanziari mostrano la loro implementazione pratica. Il risultato delle tecniche introdotte precedentemente può essere utilizzato in procedure di selezione automatica di portafoglio al fine di coprire il rischio dovuto al verificarsi di perdite congiunte. Viene proposta una strategia di diversificazione di portafoglio in due fasi e illustrate le analisi empiriche. L'approccio suggerito per il raggruppamento di serie finanziarie può essere utile ad un investitore per avere una visione più approfondita delle correlazioni tra mercati finanziari in periodi di crisi. Inoltre, l'applicazione nell’ambito della selezione di portafogli suggerisce un uso prudente delle procedure standard che potrebbero non essere appropriate quando si prevede che i mercati possano attraversare periodi di alta volatilità.
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Longla, Martial. "Modeling dependence and limit theorems for Copula-based Markov chains." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367944672.

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Di, Lascio Francesca Marta Lilja <1979&gt. "Analyzing the dependence structure of microarray data: a copula–based approach." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/670/1/Tesi_Di_Lascio_Francesca_Marta_Lilja.pdf.

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The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.
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Di, Lascio Francesca Marta Lilja <1979&gt. "Analyzing the dependence structure of microarray data: a copula–based approach." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/670/.

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The main aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in clustering has not been still investigated. The first part of this work contains the review of the literature of clustering methods, copula functions and microarray experiments. The attention focuses on the K–means (Hartigan, 1975; Hartigan and Wong, 1979), the hierarchical (Everitt, 1974) and the model–based (Fraley and Raftery, 1998, 1999, 2000, 2007) clustering techniques because their performance is compared. Then, the probabilistic interpretation of the Sklar’s theorem (Sklar’s, 1959), the estimation methods for copulas like the Inference for Margins (Joe and Xu, 1996) and the Archimedean and Elliptical copula families are presented. In the end, applications of clustering methods and copulas to the genetic and microarray experiments are highlighted. The second part contains the original contribution proposed. A simulation study is performed in order to evaluate the performance of the K–means and the hierarchical bottom–up clustering methods in identifying clusters according to the dependence structure of the data generating process. Different simulations are performed by varying different conditions (e.g., the kind of margins (distinct, overlapping and nested) and the value of the dependence parameter ) and the results are evaluated by means of different measures of performance. In light of the simulation results and of the limits of the two investigated clustering methods, a new clustering algorithm based on copula functions (‘CoClust’ in brief) is proposed. The basic idea, the iterative procedure of the CoClust and the description of the written R functions with their output are given. The CoClust algorithm is tested on simulated data (by varying the number of clusters, the copula models, the dependence parameter value and the degree of overlap of margins) and is compared with the performance of model–based clustering by using different measures of performance, like the percentage of well–identified number of clusters and the not rejection percentage of H0 on . It is shown that the CoClust algorithm allows to overcome all observed limits of the other investigated clustering techniques and is able to identify clusters according to the dependence structure of the data independently of the degree of overlap of margins and the strength of the dependence. The CoClust uses a criterion based on the maximized log–likelihood function of the copula and can virtually account for any possible dependence relationship between observations. Many peculiar characteristics are shown for the CoClust, e.g. its capability of identifying the true number of clusters and the fact that it does not require a starting classification. Finally, the CoClust algorithm is applied to the real microarray data of Hedenfalk et al. (2001) both to the gene expressions observed in three different cancer samples and to the columns (tumor samples) of the whole data matrix.
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TKACH, KATERYNA. "Essays on multidimensional poverty measurement and the dependence among well-being dimensions." Doctoral thesis, Università degli studi dell'Insubria, 2019. http://hdl.handle.net/10281/317984.

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Evaluating the welfare of nations is high on the research agenda of the economists, practitioners and policy-makers. The literature contributions of the last decades triggered a multivariate perception of the well-being, which is suggested to go beyond the GDP, and created a need for more complex approaches to evaluate the welfare as well as poverty. The first essay investigates the approaches to multivariate poverty measurement and focuses on the composite index approach and the steps involved in it. An important aspect of the multivariate perspective in well-being is the dependence among the underlying indicators. There is growing evidence in the literature that well-being dimensions are interrelated. This dependence among attributes matters for multidimensional poverty measurement, since income is no longer the only indicator to be considered. However, the reviewed approaches to multivariate poverty measurement do not commonly capture this interdependence. The second essay suggests a copula function as a flexible tool to estimate the dependence among welfare variables. Moreover, it proposes to incorporate the evaluated dependence in the composite indicator. The trade-off among attributes, which is established via the weighting of dimensions, is identified as a possible channel to include the interdependence in the composite indicator. The third essay of this dissertation defines bivariate and multivariate copula-based measures of dependence and applies them using the recent data from the EU-SILC. The results suggest that key dimensions of well-being, i.e. income, education and health, are positively interdependent. In addition, the strength of pairwise and multivariate dependence reinforced in the post-crisis period in some European countries. Finally, the last essay proposes a new class of the copula-based multidimensional poverty indices by innovating over the weighting approach. The weighting scheme proposed in this dissertation incorporates the estimated copula-based dependence and contains necessary normative controls to be chosen by the practitioner. The findings of the last essay suggest that the overall poverty is driven not only by the individual shortfalls, but also by the degree of interdependence among well-being indicators. Considering the proposed copula-based weighting scheme and the proposal of the new class of copula-based poverty indices, this dissertation contributes to the multivariate poverty measurement by suggesting the channel to enclose the dependence structure in the composite indicators. The proposed copula-based methodology will advance the multidimensional poverty analysis and the poverty-reducing policy, which can be designed to address the problem of interdependence of individual achievements.
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Spanhel, Fabian [Verfasser], and Stefan [Akademischer Betreuer] Mittnik. "A copula-based approach to model serial dependence in financial time series / Fabian Spanhel ; Betreuer: Stefan Mittnik." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2015. http://d-nb.info/1131551893/34.

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[Verfasser], Suroso, and András [Akademischer Betreuer] Bárdossy. "Asymmetric dependence based spatial copula models : empirical investigations and consequences on precipitation fields / Suroso ; Betreuer: András Bárdossy." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2017. http://d-nb.info/1139709720/34.

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Zhang, Fan. "Management of foreign reserves : an approach based on vine-copula, regime-switching dependence and Bayesian opinion pooling." Thesis, Durham University, 2014. http://etheses.dur.ac.uk/10847/.

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This research is constructed to address the issue of structure management for colossal foreign exchange reserves holders, such as China and other emerging economies. Contrary to the discussion of optimal quantity on the reserve level, structure management considers the ideal applications of the national wealth, specifically the compositions in the reserves' financial investments. Two perspectives are considered for the safety and liquidity tranche of the foreign reserves, and another one for the return tranche. The thwo perspectives are further developed into three chapters of this thesis and they form a comprehensive set of analyses for the structure management. First, the optimal currency composition for huge foreign reserves in the safety and liquidity tranche is investigated. The asymmetry fat-tails and complex dependence structure in distributions of currency returns are examined for their vital role in the portfolio risk appraisal. In a D-vine copula approach, it is shown that under the disappointment aversion effect, the central bank in our model can achieve sizeable gains in economic value by switching from the mean-variance to copula modelling. It is also found that this approach will lead to an optimal currency composition that allows China to have more space for international currency diversification, while maintaining the leading position of the US dollar in the currency shares of China’s reserves. Next, the strategic asset allocation for China’s foreign reserves in the same safety tranche is studied using a risk-based approach. Four aspects of the risk management are investigated: investment universe, dependence structure, allocation strategies under risk minimization and trade-off between risks and returns. A regime-switching copula model is developed to investigate the dynamic dependence between assets. The optimal allocation is derived following two strategies: risk minimization and trade-off between risk and returns in utility maximization with disappointment avoidance. If the central bank focuses solely on risk minimization, the asymmetries in the asset return dependence encourage the flight to safety. However, if higher risks are allowed in exchange for higher returns, even if the exchange is very conservative, the asymmetries would discourage the flight to safety. Therefore, we suggest that China should mitigate its flight to safety after 2008 and increase holdings of short-term bank deposits, long-term treasury bonds and euro bonds. Finally, the strategic asset allocation problem for China's Sovereign Wealth Fund, the China Investment Corporation, is examined. This is considered to be the return tranche of China's foreign reserves. Bearing the responsibility to pursue higher returns for China's huge volume of foreign exchange reserves, the China Investment Corporation is endowed with a capable funding position. However, its emphasis on safety is still considered more serious than that of other institutional investors. A new method combining the merits of the shrinkage estimation, vine-copula structure, and Black-Litterman model, is proposed and tested to satisfy the revealed investment objectives. Empirical analysis suggests that there is more emphasis on emerging market economies rather than advanced economies when diversifying in fixed-income securities; whereas that emphasis is reversed on the equities side. In addition, using the commodity ETFs to represent the significance of gold in the portfolio, it is discovered that gold is a formidable competitor to the investment in equities.
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Damaseb, W. B. "Investigation on the efficient frontier based on CVaR under copula dependence structure with applications to South African JSE stocks." Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/4877.

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Includes bibliographical references.
We study the feasihility of using a coherent monetary risk measure, Conditional Value at Risk (CVaR) also known as Expected Shortfall (ES), to optimise a portfolio of South African stocks. Value at Risk (VaR) is not a sub-additive risk measure and therefore does not possess one of the four properties that all coherent risk measures must satisfy. Using copula to describe the dependence structure between the instruments in our portfolio, we implement and backtest a CVaR optimization algorithm and compare the backtested results to those obtained using parametric and non-parametric/Monte Carlo VaR. Finally we optimise the portfolio of stocks and generate an efficient frontier specifying CVaR as the risk measure instead of the portfolio variance traditionally used in Markowitz and CAPM models.
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Safari, Katesari Hadi. "BAYESIAN DYNAMIC FACTOR ANALYSIS AND COPULA-BASED MODELS FOR MIXED DATA." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/dissertations/1948.

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Available statistical methodologies focus more on accommodating continuous variables, however recently dealing with count data has received high interest in the statistical literature. In this dissertation, we propose some statistical approaches to investigate linear and nonlinear dependencies between two discrete random variables, or between a discrete and continuous random variables. Copula functions are powerful tools for modeling dependencies between random variables. We derive copula-based population version of Spearman’s rho when at least one of the marginal distribution is discrete. In each case, the functional relationship between Kendall’s tau and Spearman’s rho is obtained. The asymptotic distributions of the proposed estimators of these association measures are derived and their corresponding confidence intervals are constructed, and tests of independence are derived. Then, we propose a Bayesian copula factor autoregressive model for time series mixed data. This model assumes conditional independence and shares latent factors in both mixed-type response and multivariate predictor variables of the time series through a quadratic timeseries regression model. This model is able to reduce the dimensionality by accommodating latent factors in both response and predictor variables of the high-dimensional time series data. A semiparametric time series extended rank likelihood technique is applied to the marginal distributions to handle mixed-type predictors of the high-dimensional time series, which decreases the number of estimated parameters and provides an efficient computational algorithm. In order to update and compute the posterior distributions of the latent factors and other parameters of the models, we propose a naive Bayesian algorithm with Metropolis-Hasting and Forward Filtering Backward Sampling methods. We evaluate the performance of the proposed models and methods through simulation studies. Finally, each proposed model is applied to a real dataset.
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Books on the topic "Copula-based dependence"

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Emura, Takeshi, and Yi-Hau Chen. Analysis of Survival Data with Dependent Censoring: Copula-Based Approaches. Springer, 2018.

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Emura, Takeshi, Shigeyuki Matsui, Virginie Rondeau, and Yi-Hau Chen. Survival Analysis with Dependent Censoring and Correlated Endpoints: Copula-Based Approaches. Springer Singapore Pte. Limited, 2018.

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Book chapters on the topic "Copula-based dependence"

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Di Lascio, F. Marta L., Fabrizio Durante, and Roberta Pappadà. "Copula–based clustering methods." In Copulas and Dependence Models with Applications, 49–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64221-5_4.

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Erdely, Arturo. "Copula-based piecewise regression." In Copulas and Dependence Models with Applications, 69–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64221-5_5.

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Xu, Jia, and Longbing Cao. "Vine Copula-Based Asymmetry and Tail Dependence Modeling." In Advances in Knowledge Discovery and Data Mining, 285–97. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93034-3_23.

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Erkal Sonmez, Ozlen, and Alp Baray. "On Copula Based Serial Dependence in Statistical Process Control." In Lecture Notes in Management and Industrial Engineering, 127–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03317-0_11.

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Kim, Seongyong, and Daeyoung Kim. "Directional Dependence Analysis Using Skew-Normal Copula-Based Regression." In Statistics and Causality, 131–52. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch6.

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De Keyser, Steven, and Irène Gijbels. "Copula-Based Divergence Measures for Dependence Between Random Vectors." In Building Bridges between Soft and Statistical Methodologies for Data Science, 104–11. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15509-3_14.

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Puarattanaarunkorn, Ornanong, and Songsak Sriboonchitta. "Copula Based GARCH Dependence Model of Chinese and Korean Tourist Arrivals to Thailand: Implications for Risk Management." In Modeling Dependence in Econometrics, 343–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_22.

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Kiatmanaroch, Teera, and Songsak Sriboonchitta. "Relationship between Exchange Rates, Palm Oil Prices, and Crude Oil Prices: A Vine Copula Based GARCH Approach." In Modeling Dependence in Econometrics, 399–413. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_25.

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Pappadà, Roberta, Fabrizio Durante, and Nicola Torelli. "A Graphical Tool for Copula Selection Based on Tail Dependence." In Studies in Classification, Data Analysis, and Knowledge Organization, 211–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-55708-3_23.

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Puarattanaarunkorn, Ornanong, and Songsak Sriboonchitta. "Analyzing Relationship between Tourist Arrivals from China and India to Thailand Using Copula Based GARCH and Seasonal Pattern." In Modeling Dependence in Econometrics, 367–82. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03395-2_23.

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Conference papers on the topic "Copula-based dependence"

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Pitertsev, A. A., R. B. Sinitsyn, and F. J. Yanovsky. "Copula based dependence measure for polarimetric weather radar." In 2015 16th International Radar Symposium (IRS). IEEE, 2015. http://dx.doi.org/10.1109/irs.2015.7226408.

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Xu, Jia, Wei Wei, and Longbing Cao. "Copula-Based High Dimensional Cross-Market Dependence Modeling." In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2017. http://dx.doi.org/10.1109/dsaa.2017.67.

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Choi, Sora, Hao He, and Pramod K. Varshney. "Copula based dependence modeling for inference in RADAR systems." In 2015 IEEE Radar Conference. IEEE, 2015. http://dx.doi.org/10.1109/radarconf.2015.7411879.

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Zhang, Jun, and Ziping Du. "Clustering of financial time series based on temporal dependence copula." In International conference on Management Innovation and Information Technology. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/miit131872.

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Chen, Zhong-Zhe, Yu Liu, Hong-Zhong Huang, Xuehai Wu, and Liping He. "A Reliability Allocation Method Considering Failure Dependence." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12944.

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Traditional reliability allocation methods are based on the assumption that the subsystems of a system are independence in order to simplify the problem. However, this assumption could deviate from the engineering practice. To achieve the reliability requirement of the system, the subsystems must be allocated high reliability based on traditional reliability allocation approaches which neglect the dependence between subsystems. To solve this problem, an improved reliability allocation method is developed in this paper. Firstly, the failure dependence between subsystems of mechanical systems is characterized using Copula functions. Secondly, a reliability prediction model considering failure dependence is formulated based on Copula function, Furthermore, the improved reliability allocation method according to relative failure rate is proposed. Finally a numerical case is presented to illustrate the proposed approach. The optimal allocation result shows that the system can achieve reliability requirement without high reliability demand to some subsystems, which could reduce the unnecessary cost.
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Yundai, Xu, and Yuan Yue. "Analysis of Aggregated Wind Power Dependence Based on Optimal Vine Copula." In 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia). IEEE, 2019. http://dx.doi.org/10.1109/isgt-asia.2019.8881069.

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Yi, Wen-de. "Copula-Based Dependence Models of Two Markov Time Series of Order 1." In Eighth International Conference of Chinese Logistics and Transportation Professionals (ICCLTP). Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/40996(330)503.

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Lu, Qiuyu, Wei Hu, Yong Min, Fei Yuan, and Zonghe Gao. "Wind power uncertainty modeling considering spatial dependence based on Pair-copula theory." In 2014 IEEE Power & Energy Society General Meeting. IEEE, 2014. http://dx.doi.org/10.1109/pesgm.2014.6938902.

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Lu, Ning, Yan-Feng Li, Tudi Huang, and Hong-Zhong Huang. "Reliability Analysis for Aero Engine Gear Considering Dependence of Multiple Failure Modes Based on Copula." In 2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS). IEEE, 2022. http://dx.doi.org/10.1109/icrms55680.2022.9944575.

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Salimi, Ehsan, and Ali E. Abbas. "A simulation-based comparison of maximum entropy and copula methods for capturing non-linear probability dependence." In 2016 Winter Simulation Conference (WSC). IEEE, 2016. http://dx.doi.org/10.1109/wsc.2016.7822112.

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Reports on the topic "Copula-based dependence"

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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.
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