In this thesis, we set out to model the market risk exposure for 251 stocks in the S&P 500 index, during a ten-year period between 2011-04-30 and 2021-03-31. The study brings to light a model not often mentioned in the scientific literature focused on market risk estimation, the linear mixed model. The linear mixed model makes it possible to model a time-varying market risk, as well as adding structure to the idiosyncratic risk, which is often assumed to be a stationary process. The results show that the mixed model is able to produce more accurate estimates for the market risk, compared to the baseline, which is here defined as a CAPM model. The success of the mixed model, which we in the study will refer to as the ADAPT model (adaptive APT), most certainly lies in its ability to create a hierarchical regression model. This makes it possible to not just view the set of observations as a single population, but let us group the observations into different clusters and in such a way makes it possible to construct a time-varying exposure. In the last part of the thesis, we highlight possible improvements for future works, which could make the estimation even more accurate and also more efficient.

The recent U.S. subprime mortgage crisis rapidly spread throughout the world and put the global financial system under extraordinary pressure. The main implication of the recent crisis is that complex banking regulations failed to adequately identify and limit riskiness of banking systems at both domestic and international levels. In spite of a large empirical literature on the causes and remedies of the recent crisis, there remains substantial uncertainty on (i) how risk measuring models performed during crisis, (ii) how systematic factors such as house prices affected the financial system, and (iii) how effectively government policy responses resolved the financial crisis. This thesis seeks to narrow this gap in the literature by offering three empirical essays. The first essay investigates the performance of alternative parametric VaR models in forecasting riskiness of international equity portfolios. Notably, alternative univariate VaR models are compared to multivariate conditional volatility models with special focus given to conditional correlation models. Conditional correlation models include the constant conditional correlation (CCC), dynamic conditional correlation (DCC), and asymmetric DCC (ADCC) models. Various criteria are then applied for backtesting VaR models and to evaluate their one-day-ahead forecasting ability in a wide range of countries and during different global financial conditions. It is found that most VaR models have satisfactory performance with small number of violations during pre-crisis period. However, the number of violations, mean deviation of violations, and maximum deviation of violations dramatically increase during crisis period. Furthermore, portfolio models incur lower number of violations compared to univariate models while DCC and ADCC models perform better than CCC models during crisis period. From risk management perspective, most single index models fail to pass Basel criteria for internal VaR models during crisis period, whereas empirical evidence on the choice between CCC, DCC, and ADCC models is mixed. The recent crisis also raised serious concerns about factors that can systematically destabilise the whole banking system. In particular, the collapse of house prices in the United States triggered the recent subprime mortgage crisis, which was associated with a sharp increase in the number of nonperforming loans and bank failures. This in turn demonstrates the key role that house prices play in systematically undermining the whole banking system. The second essay investigates the determinants of nonperforming loans (NPL) with a special focus on house price fluctuations as a key systematic factor. Using a panel of U.S. banking institutions from 1999 to 2012, the analysis is carried out across different loan categories, different types of banks, and different bank size. It is found that house price fluctuations have a significant impact on the evolution of nonperforming loans, while the magnitude of their impact varies across loan categories, institution types, and between large and small banks. Also, the impact of house price fluctuations on nonperforming loans is more pronounced during crisis period. The last essay of this thesis investigates the effectiveness of the U.S. government strategy to combat the crisis. As a comprehensive response to the recent financial crisis, the US government created the Troubled Asset Relief Program (TARP). The Capital Purchase Program (CPP) was launched as an initial program under the TARP. The CPP was designed to purchase preferred stocks or equity warrants from viable financial institutions. Using a large panel of the U.S. commercial banks over the period 2007Q1 to 2012Q4, survival analysis is used to investigate the impact of TARP funds on the likelihood of survival in the recipient banks. It is found that larger recipient banks are more likely to avoid regulatory closure, while receiving capital assistance does not effectively help banks to avoid technical failure. This implies that governmental capital assistance serves larger banks much better than their smaller counterparts. In addition, TARP recipients are more likely to be acquired, regardless of their size and financial health. In summary, the empirical findings reveal that capital infusions do not enhance the survival likelihood of the recipient banking institutions.

Thesis (MBA)--Stellenbosch University, 2004. / ENGLISH ABSTRACT: Risk managers and regulators are primarily concerned with ensuring that there is sufficient capital to withstand the effects of adverse movements in market prices. The accurate prediction of the maximum amount that a financial institution can expect to Jose over a specified period is essential to guard against catastrophic losses that can threaten the viability of an individual finn or the stability of entire markets. Value-at-risk (VaR) is a quantile-based measure of risk that is widely used for calculating the capital adequacy requirements of banks and other financial institutions. However, the current models for price risk tend to underestimate the risk of catastrophic losses because the entire return distribution is used to calculate the value-at-risk. By contrast, Extreme Value" Theory uses only the largest observations to model the tails of a distribution, which should provide a better fit for estimates of extreme quantiles and probabilities. The semi-parametric Hill (1975) estimator has often been used to fit the tails of financial returns, but its performance is heavily dependent on the number k" of order statistics used in the estimation process and the estimator can be very biased if this choice is suboptimal. Since k" depends on unknown properties of the tail, it has to be estimated from the sample. The first truly data-driven method for choosing an optimal number of order statistics adaptively was introduced by Beirlant, Dierckx. Goegebeur and Matthys (1999) and modified by Beirlanl. Dierckx and Stmca (2000) and Matthys and Beirlanl (2000b). Their methods are based on an exponential regression model developed independently by Beirlant et a/. (1999) and Feuerverger and Hall (1999) to reduce the bias found in the Hill estimator. The reduced bias of these adaptive estimators and the associated estimator for extreme quantiles developed by Matthys and Beirlant (2000b) makes these estimators attractive from a risk management point of view, but more work needs to be done on characterising their finite sample properties before they can be used in practice. In particular, it is crucially important to establish the smallest sample size that will yield reliable estimates of extreme quantiles and probabilities and to determine the widths and coverage probabilities of confidence intervals. This study project reviews the probability and statistical theory of univariate Extreme Value Theory from a financial risk management perspective. It is clear from a survey of the literature that the most worthwhile direction to pursue in terms of practical research will be intimately connected with developments in the fast-moving field of EVT with a future emphasis not only on fully evaluating the existing models, but indeed on creating even less biased and more precise models. Keywords and phrases: Extreme value index, Pareto-type distributions, maximum likelihood estimation, bias reduction, exponential regression model, market risk. / AFRIKAANSE OPSOMMING: Risikobestuurders en -reguleerders is hoofsaaklik gemoeid met die versekering dat genoegsame kapitaal beskikbaar is om die effek van ongunstige beweging in markpryse die hoof te kan bied. Die akkurate vooruitskatting van die maksimum verlies wat 'n finansiele instelling oor 'n spesifieke tydperk kan ly, is noodsaaklik as beskerming teen katastrofiese verliese wat die voortbestaan van 'n individuele firma, of die stabiliteit van die totale mark, mag bedreig. Waarde-op-Risiko (WoR) is 'n kwantiel gebaseerde maatstaaf van risiko wat algemeen vir die berekening van kapitaaltoereikendheid van banke en ander finansiele instellings benut word. Die huidige prys risikomodelle neig om die risiko van katastrofiese verliese te onderskat, omdat die totale opbrengs verspreiding gebruik word om WoR te bereken. In teenstelling benut die Ekstreme Waarde Teorie (EWT), slegs die grootste waarnemings om die eindverdelings te modelleer en is as sulks meer geskik om ekstreme kwantiele en waarskynlikhede te bepaal. Die semi-parametriese Hill (1975) skatter word gereeld gebruik om die stertgedeeltes van finansiele opbrengste te beraam, maar sy verrigting is swaar afhanklik van die getal k~ van rangstatistieke wat in die skattingsproses gebruik word en die skatting kan baie sydig wees indien die keuse suboptimaal is. Weens die afhanklikheid van kn van onbekende eienskappe van die stertgedeeltes, moet dit geskat word vanuit die steekproefdata. Die eerste data-gedrewe metode vir die keuse van die optimale rangordestatistieke, is deur Beiriant, Dierckx, Goegebeur en Matthys (1999) ontwikkel en aangepas deur Beirlant, Dierckx and Starica (2000), asook Matthys en Beirlant (2000b). Hul metodes is op 'n eksponensiele regressiemodel gebaseer, en is onafhanklik deur Beirlant et at. (1999), en Feuerverger en Hall (1999) ontwikkel met die doel om die sydigheid van die Hill skatter te verminder. Die verminderde sydigheid van hierdie adaptiewe skatters en die verwante skatter vir ekstreme kwantiele, ontwikkel deur Matthys en Beirlant (2000b), maak hierdie skatters aantreklik vanuit 'n risikobestuur oogpunt, maar meer werk word benodig met die karakterisering van hul eindige steekproefeienskappe, alvorens dit in die praktyk benut kan word. In besonder is dit van uiterste belang dat die kleinste steekproefgrootte bepaal sal word wat die betroubare skattings van ekstreme kwantiele en moontlikhede sal verseker, en wat ook benut kan word om betroubaarheidsintervalle op te ste!. Hierdie studie bied 'n oorsig van die moontlikhede en statistiese teorie van die eenveranderlike EWT vanuit 'n finansiele risikobestuur perspektief. Dit is duidelik vanuit die literatuurstudie dat die mees nuttige rigting om voort te gaan met praktiese navorsing, verband hou met die ontwikkeling in die vinnig ontwikkelende veld van EWT met toekomstige fokus, nie slegs op die volle evaluering van die bestaande modelle nie, maar ook op die ontwikkeling van minder sydige en meer akkurate modelle.

[pt] O gerenciamento de riscos de mercado é um assunto que já assume papel relevante e definitivo no ambiente das instituições financeiras. Mais recentemente o assunto vem ganhando espaço também no âmbito de instituições não financeiras. Dentre os benefícios advindos da implantação de sistemas de medição e gerenciamento de riscos de mercado no âmbito das instituições não financeiras, destacam-se como os mais diretos: o controle dos fluxos de caixa necessários ao cumprimento dos investimentos programados pela empresa, a redução da volatilidade desses fluxos e, conseqüentemente, da probabilidade de a empresa deixar de honrar compromissos futuros. Benefícios adicionais incluem o aumento da transparência aos investidores e a rápida assimilação de novas fontes de riscos de mercado pelos gestores. Considerando a existência deste espaço e a importância do tema para as empresas, este trabalho propõe a construção de um modelo teórico para mensuração do fluxo de caixa em risco e o aplica a uma única empresa pertencente ao setor de distribuição de energia elétrica no Brasil. Tal modelo deve ser capaz de informar a probabilidade dessa empresa não dispor de recursos para honrar seus compromissos em determinada data de pagamento futura, ou vértices do fluxo. / [en] In the last years, risk management assumed a relevant and definitive role in the environment of financial institutions. More recently however, the subject has also been gaining ground in the environment of non- financial institutions. Among the benefits arising from the introduction of risk management within the environment of non-financial institutions, those that stand out as being the most direct are the control of the cash flow necessary for the investments that have been programmed, reduction in the volatility of this cash flow, and consequently in the probability of the company failing to honor its future commitments. Additional benefits include an increase in transparency as far as investors are concerned, a rapid assimilation of new risk sources by the managers. Considering this gap and the theme`s importance to non-financial institutions, this work proposes a theoretical model, which aims to measure firm cash flow-at-risk. Afterwards, the proposed model is applied and tested in only one Brazilian distribution electric sector company. Such a model may be able to return the probability that a company faces a financial distress, for not being able to make due payments in the set dates.

[pt] O gerenciamento de Risco para empresas não financeiras é de grande importância, e vem recebendo cada vez mais relevância no mercado financeiro no país. O risco de mercado, ou seja, a volatilidade das variáveis as quais as companhias estão submetidas deve ser analisada com extrema atenção, dado que estas variáveis impactam de maneira significativa os fluxos de caixa e o valor destas. Esta pesquisa tem como objetivo estudar a utilização do Fluxo de Caixa em Risco (Cash Flow at Risk – CfaR), um instrumento para controle de risco de mercado que simula o valor em risco do fluxo de caixa futuro de uma companhia dentro de um intervalo de confiança predefinido. Faremos uma apresentação do modelo CfaR em uma empresa não financeira, em particular uma companhia do setor Sucroenergético para se estimar a probabilidade desta empresa não possuir a liquidez necessária para arcar com seus compromissos financeiros de curto prazo. / [en] Risk Management for non financial companies is of great importance and has been gaining even more significance in the country’s financial market. The Market Risk, that is, the volatility of the variables in which companies are exposed has to be analyzed with extreme attention, given that such variations may impact strongly the companies’ cash flows and its values. This paper has the aim to study the use of the Cash Flow at Risk – CfaR, a tool to control market risk and that simulates the Value at Risk of the future cash flow of a company at a predetermined confidence interval. The goal is to make a presentation of the CfaR model to a non financial company, particularly a company in the Sucroenergetic sector to estimate the probability of this specific enterprise not having the liquid assets necessary to honor its short term commitments.

O risco de mercado pode ser entendido como o risco de perdas em decorrência de oscilações em variáveis econômicas e financeiras, como taxas de juros, taxas de câmbio, preços de ações e de commodities. A adequada evidenciação dos aspectos relacionados ao risco de mercado tem assumido importância crescente no sistema financeiro, por diversos fatores, como as crises financeiras de amplitude global, o desenvolvimento dos derivativos, os colapsos empresariais decorrentes de deficiências na gestão de riscos, e as exigências de capital em função dos riscos incorridos pelas instituições. O objetivo da pesquisa é verificar e analisar o grau de evidenciação, por parte das instituições financeiras com atuação no Brasil, quanto às questões relativas ao risco de mercado. Para a avaliação das informações prestadas, são utilizadas como parâmetro as recomendações de evidenciação do Comitê de Supervisão Bancária da Basiléia, bem como informações sobre as práticas de divulgação de instituições financeiras no mercado internacional, a partir de levantamentos realizados pelo BIS (Bank for International Settlements). Assim, questiona-se se as instituições financeiras com atuação no Brasil têm apresentado aderência aos padrões internacionais de evidenciação na área de risco de mercado. Os resultados obtidos com a pesquisa empírica, que consistiu na análise de relatórios anuais (de 1997 a 2002) de bancos com atuação no Brasil, permitem concluir que a evidenciação bancária no mercado doméstico, apesar de mostrar indicadores de evolução na área de riscos de mercado, ainda apresenta um nível incipiente de transparência quando comparado com as práticas de divulgação de instituições do sistema financeiro internacional. Identifica-se, assim, a necessidade de melhoria do nível de disclosure bancário brasileiro. Para tanto, mais do que impor regras que tornem compulsórios determinados padrões de evidenciação, requer-se o desenvolvimento de todo o conjunto de precondições legais, institucionais e culturais relevantes para o amadurecimento do mercado de capitais doméstico e de seus sistemas de governança corporativa. / Market risk may be understood as the risk of losses resulting from fluctuation in economic and financial variables, such as interest rates, exchange rates, stock and commodities prices. The adequate disclosure of aspects related to market risk has assumed increasing importance in the financial system, because of several factors, such as global financial crises, development of derivatives, business collapses due to deficiencies in risk management, and regulatory capital requirements related to risks assumed by the institutions. The objective of this work is to verify and analyze the degree of disclosure, presented by financial institutions operating in Brazil, concerning market risk. For the assessment of the information rendered, a benchmark was constituted with the disclosure recommendations of the Basel Committee on Banking Supervision and information about the disclosure practices of financial institutions in the international financial market, based on surveys carried out by BIS (Bank for International Settlements). Thus, the question is whether the financial institutions operating in Brazil have been presenting adherence to international standards of disclosure in market risks area. The results obtained with the research, which consisted in the assessment of annual reports (from 1997 to 2002) of banks operating in Brazil, allow to conclude that banking disclosure in the domestic market, in spite of indicating an evolution in the market risks area, still presents an incipient level of transparency when compared with the disclosure practices of international financial institutions. It is identified a need for improving the disclosure level of Brazilian banks. For this, it is not enough to impose rules that make compulsory certain disclosure standards; it is necessary the development of legal, institutional and cultural preconditions that are required for the maturing of the domestic capital market and its corporate governance systems.

This project focuses on risk analysis of corporate bond portfolios. We separate the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called basis which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition, we also introduce a Fama-French multi-factor model to analyze factor significance to the corporate bond portfolio.

This project focuses on risk analysis of corporate bond portfolios. We divide the total risk of the portfolio into three parts, which are market risk, credit risk and liquidity risk. The market risk component is quantified by value-at-risk (VaR) which is determined by change in yield to maturity of the bond portfolio. For the credit risk component, we calculate default probabilities and losses in the event of default and then compute credit VaR. Next, we define a factor called `basis' which is the difference between the Credit Default Swap (CDS) spread and its corresponding corporate bond yield spread (z-spread or OAS). We quantify the liquidity risk by using the basis. In addition we also introduce a Fama-French multi-factor model to analyze the factor significance to the corporate bond portfolio.

Made available in DSpace on 2016-04-25T16:45:15Z (GMT). No. of bitstreams: 1 Lucimeire Caselato.pdf: 1588642 bytes, checksum: d6bd3b9c1673980b6b7a57fe3a40174d (MD5) Previous issue date: 2009-04-22 / Financial institutions are vulnerable to several risks, one of the most important risks is named market risk. The exposure to market risk can be defined as the probability of financial losses, and it can be characterized as the exposure of financial institution to a certain risk factor and the changes that occur in the asset prices because of market volatility. Trying to measure risk exposures, financial insitutions use a methodology named Value-at-Risk (VaR). Among the methodologies developed to measure financial risks there are, basically, three methodologies: parametric, historical simulation and Monte Carlo simulation. The objective of this research is to compare the performance between historical simulation methodology and parametric (or Delta-Normal) methodology, applied to three different portfolios. After mesuring VaR using the two different methodologies, it will be applied the backtest, to verify wich of the mentioned methodologies had the best performance to measure market risks / Uma instituição financeira está exposta à vários tipos de risco, sendo que um dos principais é o risco de mercado. A exposição ao risco de mercado pode ser entendida como a probabilidade de ocorrerem perdas financeiras, dados: a exposição financeira de uma instituição financeira em um determinado fator de risco e mudanças que ocorrem nos preços dos ativos devido às oscilações de mercado. Tentando mensurar a exposição risco de mercado, as instituições financeiras recorrem ao cálculo do VaR (Value at Risk). Existem, basicamente, três métodos de cálculo de VaR: VaR paramétrico (ou delta-normal), VaR por simulação histórica e VaR por Simulação de Monte Carlo. No presente estudo será comparada a eficiência de dois métodos utilizados para o cálculo do VaR: VaR paramétrico e VaR por simulação Histórica, em três carteiras pré-fixadas. Após o cálculo do VaR utilizando estas duas metodologias será aplicado o backtest, para verificar qual das duas metodologias mensurou de forma eficaz valor do risco de mercado

Nos últimos anos, o value-at-risk tem se tornado uma ferramenta amplamente utilizada nas principais instituições financeiras, inclusive no Brasil. Dentre suas vantagens, destaca-se a possibilidade de se resumir em um único número os riscos de mercado incorridos e incorporar neste valor tanto a exposição da instituição quanto a volatilidade do mercado. O objetivo principal deste estudo é verificar a eficácia dos modelos mais conhecidos de value-at-risk - RiskMetrics(TM) e Simulação Histórica - na mensuração dos riscos de mercado de carteiras de renda fixa compostas por instrumentos pré-fixados em reais. No âmbito da alocação de capital para atendimento aos órgãos de regulamentação, o estudo estende-se também ao modelo adotado pelo Banco Central do Brasil. No decorrer do estudo, discute-se ainda as vantagens e desvantagens apresentadas, bem como o impacto que as peculiaridades do mercado brasileiro exercem sobre as hipóteses assumidas em cada um dos modelos. / Value-at-Risk (VaR) has become the primary tool for the systematic measuring and monitoring of market risk in most financial institutions. VaR is a statistical measure that comprises not only the exposure but also the market volatility in a single number. The main purpose of this work is to evaluate the performance of the well-known value-at-risk models - RiskMetrics(TM) and Historical Simulation - in the Brazilian fixed-income market. In the scope of capital allocation related to banking regulation, this study also extends briefly to the model adopted by the Brazilian Central Bank. Additionally, the underlying assumptions of these models are analyzed in the Brazilian financial market context. Also, this study discusses the advantages and disadvantages presented by the RiskMetrics and the Historical Simulation models.