Construction of asymmetric copulas and its application in two-dimensional reliability modelling

Copulas offer a useful tool in modelling the dependence among random variables. In the literature, most of the existing copulas are symmetric while data collected from the real world may exhibit asymmetric nature. This necessitates developing asymmetric copulas that can model such data. In the meantime, existing methods of modelling two-dimensional reliability data are not able to capture the tail dependence that exists between the pair of age and usage, which are the two dimensions designated to describe product life. This paper proposes two new methods of constructing asymmetric copulas, discusses the properties of the new copulas, and applies the method to fit two-dimensional reliability data that are collected from the real world

Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

This paper aims at investigating the dynamic dependence and extreme risk comovement of oil price and exchange rates in seven oil-importing and seven oil-exporting countries. For this purpose, we use six representative time- varying copula models and four types of tail dependences to assess the downside and upside conditional value-at-risk measures (CoVaRs). Our findings indicate that the dependence of crude oil returns and exchange rates is negative for most pairs, i.e., the rise (fall) in oil prices was accompanied by the appreciation (depreciation) of foreign currency against the US dollar. The oil price – exchange rate dependences in oil exporters are slightly larger than in oil importers, even though the dependence is weak in general. More interestingly, we find strong evidence of significant risk comovement between crude oil returns and exchange rates through the analysis of downside and upside CoVaRs. This comovement particularly showed asymmetric effects

Essays on Machine Learning in Risk Management, Option Pricing, and Insurance Economics

Dealing with uncertainty is at the heart of financial risk management and asset pricing. This cumulative dissertation consists of four independent research papers that study various aspects of uncertainty, from estimation and model risk over the volatility risk premium to the measurement of unobservable variables. In the first paper, a non-parametric estimator of conditional quantiles is proposed that builds on methods from the machine learning literature. The so-called leveraging estimator is discussed in detail and analyzed in an extensive simulation study. Subsequently, the estimator is used to quantify the estimation risk of Value-at-Risk and Expected Shortfall models. The results suggest that there are significant differences in the estimation risk of various GARCH-type models while in general estimation risk for the Expected Shortfall is higher than for the Value-at-Risk. In the second paper, the leveraging estimator is applied to realized and implied volatility estimates of US stock options to empirically test if the volatility risk premium is priced in the cross-section of option returns. A trading strategy that is long (short) in a portfolio with low (high) implied volatility conditional on the realized volatility yields average monthly returns that are economically and statistically significant. The third paper investigates the model risk of multivariate Value-at-Risk and Expected Shortfall models in a comprehensive empirical study on copula GARCH models. The paper finds that model risk is economically significant, especially high during periods of financial turmoil, and mainly due to the choice of the copula. In the fourth paper, the relation between digitalization and the market value of US insurers is analyzed. Therefore, a text-based measure of digitalization building on the Latent Dirichlet Allocation is proposed. It is shown that a rise in digitalization efforts is associated with an increase in market valuations.:1 Introduction 1.1 Motivation 1.2 Conditional quantile estimation via leveraging optimal quantization 1.3 Cross-section of option returns and the volatility risk premium 1.4 Marginals versus copulas: Which account for more model risk in multivariate risk forecasting? 1.5 Estimating the relation between digitalization and the market value of insurers 2 Conditional Quantile Estimation via Leveraging Optimal Quantization 2.1 Introduction 2.2 Optimal quantization 2.3 Conditional quantiles through leveraging optimal quantization 2.4 The hyperparameters N, λ, and γ 2.5 Simulation study 2.6 Empirical application 2.7 Conclusion 3 Cross-Section of Option Returns and the Volatility Risk Premium 3.1 Introduction 3.2 Capturing the volatility risk premium 3.3 Empirical study 3.4 Robustness checks 3.5 Conclusion 4 Marginals Versus Copulas: Which Account for More Model Risk in Multivariate Risk Forecasting? 4.1 Introduction 4.2 Market risk models and model risk 4.3 Data 4.4 Analysis of model risk 4.5 Model risk for models in the model confidence set 4.6 Model risk and backtesting 4.7 Conclusion 5 Estimating the Relation Between Digitalization and the Market Value of Insurers 5.1 Introduction 5.2 Measuring digitalization using LDA 5.3 Financial data & empirical strategy 5.4 Estimation results 5.5 Conclusio

Estimativas de Horizontes de Tempo para aplicação de DEA em seleção de Carteira de Ações e para manutenção da Carteira Selecionada.

A Análise Envoltória de Dados tem sido aplicada na seleção de carteiras de ações com bons resultados. Entretanto, existem lacunas nesta aplicação, quanto a estimativas de horizontes de tempo de coleta de dados para a seleção e de tempo de manutenção da carteira selecionada. A pesquisa propõe um modelo de programação matemática binária de minimização de erros quadrados para estimar estes horizontes, que é sua principal contribuição. A validação dos resultados do modelo ocorre pela simulação dos índices de retorno anuais estimados da carteira que utiliza ambos os horizontes estimados e de outras carteiras que não os utilizam, para posteriores comparações por testes de hipótese. A simulação de resultados mostra que a carteira com ambos os horizontes estimados tem os índices superiores, em média 6,99% ao ano, a todas as carteiras formadas para comparações. Os testes de hipótese confirmam a superioridade dos resultados dos índices do modelo proposto em níveis estatisticamente significativos. Isso nas comparações com carteiras que não utilizam nenhum dos horizontes estimados. As mesmas comparações com carteiras que utilizam ao menos um dos horizontes estimados indicam que os índices da carteira com ambos os horizontes continuam superiores, entretanto, com redução do percentual de significância estatística de superioridade neste caso

Risk in Costruction Industry: The Management and Financial Impact

Disertační práce se zaměřuje na finanční dopady rizik ve stavebnictví, protože je to jeden ze způsobů, jak lze riziko vyjádřit a také sledovat, což umožňuje jejich efektivní řízení. Na základě teoretických východisek a zvolené metodologie je provedena analýza stavebního trhu České republiky v širších souvislostech a jsou zde identifikovány možné zdroje vnějších rizik, které jsou dále zpracovány a popsány. Cílem disertační práce je nalezení hlavních rizik a posouzení jejich finančních dopadů, které ohrožují společnosti v odvětví stavebnictví, jenž je velmi specifickým místem pro podnikání. Tato rizika budou sestavena do katalogu riziku, pomocí kterého je možné řídit dopady jednotlivých rizik v oblasti stavebnictví. Pro další práci s identifikovanými riziky jsou vybrány dvě konkrétní společnosti, které poskytly svoje data (finanční výkazy, stavy jednotlivých účtů, počty zakázek, jejich objem a druh, informace o zaměstnancích, atd.) a také klíčové ukazatele, které využívají. Výsledkem práce je analýza stavebnictví a sestavení katalogu rizik. V práci je jasně definován postup sestavení katalogu, který pak mohou využít společnosti, pohybující se na stavebním trhu vzhledem k opakujícím se činnostem a rizikům, která je provázejí. V tomto katalogu jsou jednotlivá rizika seřazena dle kategorií, je stanovena pravděpodobnost, se kterou se mohou vyskytnout, je stanoven finanční dopad pro společnost a je uvedeno, jakým způsobem lze riziko řídit, tedy způsoby jeho snížení, případně eliminace, a také náklady, které je nutné vynaložit. Tyto náklady je nutné vnímat ve smyslu finančního dopadu tak, aby nedocházelo k zajištění rizik, která by neměla tak vysoký finanční dopad.The present dissertation studies financial impacts of risks in the construction industry, providing one of possible ways of the risks expression and monitoring, through which they can be efficiently managed. Theoretical assumptions and selected methodology provided basis for an overall analysis of the Czech Republic construction market, identifying possible external risks sources including their processing and description. The present dissertation aims at finding major risks and assessing their financial impacts that pose threats to companies active in the construction sector, indeed a specific sector for doing business. These risks have been compiled in a Risk Catalogue intended as a guide for selected risks management. To elaborate on the risks identified, two specific companies have were selected, having provided their data (financial reports, accounts statements, number of projects contracted including their volume and type, personnel information, etc.), as well as key indicators they have been using. The dissertation outcome presents construction sector analysis and Risk Register compilation. The work clearly defines the procedure of the Register compilation, possibly to be used by construction companies with respect to recurrent activities and the risks involved. The Catalogue sorts risks in categories and defines their possible occurrence as well as their financial impact on the company. Furthermore, it presents their possible management, i.e. how they can be minimized or even eliminated, as well as necessary costs involved. Indeed, the costs have to be considered in the sense of financial impacts so that it does not result in covering risks with rather low financial impact.

Essays on univariate and multivariate modeling of financial market risks

This dissertation explores risk management with regard to the univariate and multivariate modeling of financial market risks by applying three different scientific research methods: the deductive approach, the design and implementation of a model as well as the inductive analysis of simulation results. The introduction in chapter one is followed by a theory-based approach in the second chapter with the objective to investigate implications of mean reversion in asset prices with respect to the optimal hedging strategy in an expected utility framework. Chapters three and four are motivated by the growing criticism of elliptical models in risk management and focus on improvements of dynamic vine copula models. Both chapters start with the presentation of a modified model approach, continue with its implementation in an experimental framework, and conclude with an inductive analysis of the simulation results. The third chapter deals with smooth nonparametric Bernstein vine copula models which are shown to be a crucial extension in terms of reducing model risk and also well suited for the task of approximating the true dependence structure of multivariate data sets. The follow-up study presented in chapter four deals with the so-called mixture pair-copula-constructions. The models accuracy is demonstrated by performing both a simulation and an empirical study on the in-sample and out-of-sample Value-at-Risk forecasting. Moreover the modeling approach helps risk managers to save on regulatory risk capital. The final chapter five pushes forward into a new area of research relating to financial markets. A statistical modeling framework for specifying, estimating, and testing time series of investor attention measured by Google Search Data is provided. Empirical evidence of strong non-linear and asymmetric dependence in the attention investors give to companies is documented. Furthermore, the existence of extreme dependence between stock returns and the corresponding Google Search Data is shown. Finally, a striking similarity in the joint distributions of a multivariate bank stock portfolio and the corresponding portfolio of Google Search Data is presented