Inline with the third pillar of the Basel accords, quantitative market risk measurements are investigate and evaluated comparing JP Morgan’s RiskMetrics and Bollerslev’s GARCH with the Peek over Threshold and Block Maxima approaches from the Extreme Value Theory framework. Value-at-Risk and Expected Shortfall (Conditional Value-at-Risk), with 95% and 99% confidence, is predicted for 25 years of the OMXS30. The study finds Bollerslev’s suggested t distribution to be a more appropriate distributional assumption, but no evidence to prefer the GARCH to the RiskMetrics. The more demanding Extreme Value Theory procedures trail behind as they are found wasteful of data and more difficult to backtest and therefore evaluate.

No description available.

Effects of cost and accuracy on the decision to request and to utilize expert advice were investigated in 2 experiments using a choice task. Experiments 1 and 2 found that experienced accuracy significantly predicted requesting expert advice. Participants in Experiment 2 used very inaccurate experts to rule out the expert's option. Cost affected requesting advice in Experiment 1 only when cost was able to exceed the amount that could be gained for a correct choice. Experiment 2 found a significant interaction between cost and experienced accuracy. Both experiments found requesting advice was the only significant predictor for changing answers. The results did not support an adherence to sunk costs in the decision to change answers.

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Estatística, 2015. / Submitted by Fernanda Percia França (fernandafranca@bce.unb.br) on 2016-03-22T19:46:38Z No. of bitstreams: 1 2015_YuriSampaioMaluf.pdf: 4291479 bytes, checksum: 4a9954a7905294836d257652f0ce1753 (MD5) / Approved for entry into archive by Marília Freitas(marilia@bce.unb.br) on 2016-05-26T16:30:44Z (GMT) No. of bitstreams: 1 2015_YuriSampaioMaluf.pdf: 4291479 bytes, checksum: 4a9954a7905294836d257652f0ce1753 (MD5) / Made available in DSpace on 2016-05-26T16:30:44Z (GMT). No. of bitstreams: 1 2015_YuriSampaioMaluf.pdf: 4291479 bytes, checksum: 4a9954a7905294836d257652f0ce1753 (MD5) / Neste trabalho, estudamos a formulação da distribuição de funções de variáveis aleatórias contínuas dependentes. O mecanismo de modelagem da dependência é feita via funções cópulas. Dentre os resultados obtidos formulamos a expressão geral da distribuição da soma de n variáveis aleatórias dependentes. Expandimos a abordagem para a distribuição de outras funções de variáveis aleatórias tais como o quociente, produto e uma combinação convexa. Por meio das R-Vines Cópulas, obtivermos também a expressão da soma de n variáveis aleatórias em que cada componente é governada por um processo GARCH. A partir deste resultado, calculamos o Value-at-Risk (VaR) e Expected Shortfalls (ES) da soma dessas variáveis. Em função desta estrutura, as medidas de risco passam a adquirir um comportamento dinâmico. Ao final do trabalho exibimos algumas ilustrações numéricas via simulação de Monte Carlo. Apresentamos também uma aplicação com dados reais provenientes de bolsas de valores da América Latina. / In this thesis, we studied the distribution of function of dependents continuous random variables. The modeling dependencies structures are made via copula functions. We obtain the general expression of the distribution of the sum of n dependents random variables. This approach is expanded for other functions such as ratio, product and a convex combination. Using R-Vines Copulas, we also derive an expression of the sum of n dependents random variables, being each component governed by AR-GARCH process. From these results, we assess the Value-at-Risk (VaR) and Expected Shortfalls (ES) of the sum of these variables. According to this structure, the VaR takes a dynamic behavior. At the end of this thesis, we show some numerical illustrations via Monte Carlo simulation. An application with real data from Latin American stock markets is also presented.

Dado que o investimento no mercado acionário envolve incerteza, devíamos esperar que seu retorno médio fosse relativamente superior a uma aplicação livre de risco para compensar o investidor pelo risco adicional que ele incorre quando aplica seus recursos em ações. Entretanto, não encontramos tal evidência quando analisamos o comportamento do mercado acionário brasileiro. Isto porque, considerando os retornos realizados médio dos últimos vinte anos, o prêmio histórico foi relativamente baixo. Assim, naturalmente surge à questão se tal estimativa corresponde a um valor razoável para inferirmos o futuro comportamento do mercado acionário. Para responder a esta questão, nossa metodologia constituiu em três etapas. Na primeira, revisamos a literatura em busca de técnicas de estimação do prêmio e selecionamos as abordagens baseado em artigos recentes, citações e disponibilidade de dados. Além disso, também realizamos algumas propostas de estimação. Em seguida, apresentamos os resultados das metodologias selecionadas para os anos recentes e observamos que as estimativas apresentaram certo grau de heterogeneidade. Na segunda etapa, testamos o desempenho dos modelos empíricos estimados usando testes de previsão fora da amostra. Os resultados apontaram que alguns modelos foram superiores ao prêmio histórico. Desta forma, encontramos evidências de que o prêmio histórico representa apenas mais uma fonte de informação para inferir o prêmio esperado e, se tomado sozinho, não constitui um procedimento de inferência razoável. Visto que cada modelo apresenta uma estratégia empírica para inferir o prêmio, todos deveriam representar uma fonte informacional sobre o prêmio futuro. Consequentemente, uma corrente da literatura recente destaca que a estratégia ótima pode ser agregar informações dos modelos individuais. Com este intuito, o último passo da metodologia foi combinar informações dos modelos que apresentaram melhor desempenho em relação ao prêmio histórico e verificar se tal procedimento aumentou a performance do poder preditivo dos modelos. Como resultado, verificamos que tal abordagem melhora e estabiliza a previsão do prêmio. / Given that investment in the stock market involves uncertainty, we should expect that the average return was relatively higher than a risk-free investment in order to compensate investors for the additional risk they incur. However, we find no such evidence when we analyze the Brazilian stock market behavior. This is because, considering the realized average returns of the past twenty years, the historic equity risk premium was relatively low. So, naturally, the question of whether such an estimate corresponds to a reasonable value to infer the future behavior of the stock market arises. To answer this question, our methodology consists of three stages. At first, we review the literature on risk premium estimation techniques and select the different approaches based on recent articles, quotes and availability of data. We also made some estimation proposals. We then proceed and present the results of the methodologies selected for the recent years and find that the estimates presented some degree of heterogeneity. On the second step, we test the performance of our estimates using out-of-sample predictive tests. The results showed that some models performed better than the historical premium. Thus, we find evidence that the historical premium is just another source of information to infer the expected award and, if taken alone, does not constitute a reasonable inference procedure. Since each model presents an empirical strategy to infer the premium, every one of them should represent an information source on the future premium. Consequently, a recent literature points out that the current optimal strategy may be to aggregate information from individual models. To this end, the last step of the methodology was to combine information of the models that performed better against the historical premium and verify that this procedure increased the power of the predictive performance of the models. As a result, we find that this approach improves and stabilizes the premium forecast.

Submitted by Nathália Faria da Silva (nathaliafsilva.ufv@gmail.com) on 2017-07-13T11:23:31Z No. of bitstreams: 1 texto completo.pdf: 1310330 bytes, checksum: 0b1fb40f1985db830fa723ed7b82aec9 (MD5) / Made available in DSpace on 2017-07-13T11:23:31Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1310330 bytes, checksum: 0b1fb40f1985db830fa723ed7b82aec9 (MD5) Previous issue date: 2001-11-13 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The objective of the first study was to obtain genetic parameters for sequential weights of beef cattle using RRM on data sets with missing and no missing traits, and to compare these estimates with those obtained by MTM. Growth curves of Nellore cattle were analyzed using body weights measured at ages ranging from 1 day (birth weight) to 733 days. Two data samples were created: one with 71,867 records from herds with missing traits and the other with 74,601 records from herds with no missing traits. Records preadjusted to a fixed age were analyzed by a multiple trait model (MTM), which included the effects of contemporary group, age of dam class, additive direct, additive maternal, and maternal permanent environment. Analyses were by restricted maximum likelihood (REML) with 5 traits at a time. The random regression model (RRM) included the effects of age of animal, contemporary group, age of dam class, additive direct, additive maternal, permanent environment, and maternal permanent environment. Legendre cubic polynomials were used to describe the random effects. Estimates of covariances by MTM were similar for both data sets, although those from the missing data set showed more variability from age to age. The estimates from RRM were similar to those from MTM only for the complete -trait case and showed large artifacts for the case of missing traits. Estimates of additive direct-maternal correlations under RRM for some ages approached -1.0, and most likely contained artifacts. If many traits are missing, the best approach to obtaining parameters for RRM would be conversion from smoothed MTM estimates. The purpose of the second study was estimation of parameters of models and data sets as in the first study by a Bayesian methodology – Gibbs sampling, and to make comparisons with their estimates by REML. Analyses were by a Bayesian method for all 9 traits. MTM estimated covariance components and genetic parameters for birth weight and sequential weights and RRM for all ages. Estimates of additive direct variance from herds with missing traits increased from birth weight through weight at 551 to 651 days with MTM. However, this component also increased for the sample with no missing traits after this age. Additive direct and residual estimated variance with RRM increased over all ages for both samples. For MTM, additive direct and maternal heritabilities were greater from the sample with herds with missing traits than those values from herds with no missing traits. The estimates from RRM were slightly lower than those from MTM for the sample with no missing traits; however, additive maternal heritabilities from MTM were greater than those using RRM. The estimated additive direct genetic correlations for each pair of traits were slightly higher for the first age (birth weight) using MTM than RRM. The range of additive maternal genetic correlations was lower than that for additive direct genetic correlations with MTM and RRM. Due to the fact that covariance components based on RRM were inflated for herds with missing traits, MTM should be used and converted to covariance functions. As well, for analyses with standard models where inferences on shapes of parameters are not important, analyses by REML may be more robust. The first goal of the third study was to implement the genetic evaluation of weights for a large population of beef cattle using the random regression model. The second goal was to compare these evaluations with those obtained from a multitrait evaluation. Expected progeny differences (EPD) were computed by two methods: a finite method using sparse factorization (SF) and interating (IT) by preconditioned conjugate gradient (PCG). The correlations between EPDs from MTM and RRM by SF and IT were ≤ .43 until the random regressions were orthogonalized. After orthogonalization high computing requirements of RRM were reduced by removing regressions corresponding to very low eigenvalues and by replacing the random error effects with weights. Correlations between EPDs from MTM and RRM for the additive direct effect were .87, .89, .89, .87, and .86 for W1 (weight at 60 days), W2 (weight at 252 days), W3 (weight at 243 days), W5 (weight at 426 days), and W7 (weight at 601 days), respectively. The corresponding correlations for the additive maternal effect were .85, .86, .88, .85 and .84, respectively. These low correlations were mostly due to differences in variances between the models and, to a lesser degree, due to better accounting for environmental effects and more data by RRM. The RRM applied to beef weights may be poorly conditioned numerically. / O objetivo do primeiro estudo foi estimar parâmetros para pesos seqüenciais de gado de corte, por meio de modelos de regressão aleatória (RRM), em características com informações perdidas e completas. Analisaram-se curvas de crescimento de gado Nelore mediante o uso de pesos corporais coletados, do nascer aos 733 dias de idade. Duas amostras foram geradas; a primeira era constituída de 71.867 medidas provenientes de rebanhos com informações perdidas, e a segunda, de 74.601 medidas oriundas de rebanhos com informações completas. Os pesos pré-ajustados a idades fixas foram analisados por meio de um modelo de características múltiplas (MTM), cinco características por vez, no qual foram incluídos efeitos de grupo contemporâneo, classe de idade da vaca, aditivo direto, aditivo materno e ambiente materno permanente. No modelo de regressão aleatória (RRM) foram incluídos efeitos de idade do animal, grupo contemporâneo, classe de idade da vaca, aditivo direto, ambiente permanente, aditivo materno e ambiente materno permanente. Polinômios cúbicos de Legendre foram utilizados na descrição dos efeitos aleatórios. Estimativas de covariâncias por meio de MTM foram similares em ambas as amostras, apesar de as obtidas da amostra com informações perdidas terem apresentado maior variabilidade entre as idades. As estimativas obtidas pelo RRM foram similares às obtidas pelo MTM somente para o caso de características completas e mostraram grande variabilidade para o caso de características com informações perdidas. Estimativas de correlações entre os efeitos aditivos direto e materno, por meio de RRM, foram iguais a -1.0, em algumas idades. Se várias informações forem perdidas, a melhor aproximação para obter parâmetros por meio de RRM seria a conversão das estimativas obtidas por meio de MTM. O segundo estudo objetivou estimar parâmetros por meio de modelos e características com informações perdidas e completas, à semelhança do primeiro estudo, mediante metodologia Bayesiana – Gibbs sampling, e efetivar comparações com as estimativas obtidas por meio da metodologia REML. As análises por meio do MTM foram para nove características. Estimaram-se componentes de covariâncias e parâmetros genéticos para específicos pontos seqüenciais, por meio do MTM; entretanto, por meio do RRM, tais estimativas foram obtidas para todas as idades. Estimativas de variâncias aditivas diretas para a amostra com informações perdidas aumentaram, do nascer à idade de 551 a 651 dias, pelo MTM, e em todas as idades, na amostra com informações completas. Estimativas de variâncias aditiva direta e residual, mediante RRM, aumentaram ao longo de todas as idades, em ambas as amostras. Pelo MTM, heritabilidades aditivas direta e materna foram maiores na amostra de rebanhos com informações perdidas do que na de rebanhos com informações completas. As estimativas obtidas pelo RRM foram ligeiramente menores do que aquelas obtidas pelo MTM na amostra com informações completas. Heritabilidades aditivas maternas pelo MTM foram maiores do que aquelas obtidas pelo RRM. As estimativas de correlações genéticas aditivas diretas foram levemente maiores para peso ao nascer, quando se utilizou MTM do que quando se empregou RRM. A amplitude das correlações genéticas aditivas maternas foi menor do que a do efeito genético aditivo direto, pelo MTM e pelo RRM. Tendo em vista que os componentes de covariância baseados em RRM são influenciados por informações perdidas, recomendam-se o MTM e a conversão destes componentes em funções de covariância. Além disso, nas análises com modelos-padrão em que inferências dos parâmetros não são importantes, o REML deve ser escolhido. Um terceiro trabalho objetivou a implementação de avaliação genética em bovinos de corte, utilizando modelo de regressão aleatória. Além disso, as avaliações foram comparadas com aquelas estimadas por meio de um modelo de características múltiplas. Dois métodos foram considerados nas análises: um método finito, FSPAKF90 (Factorization sparse matrix package), e o de iteração nos dados, PCG ( Preconditioned conjugate gradient). As correlações entre as diferenças esperadas nas progênies (DEP), estimadas pelo MTM e pelo RRM, foram muito baixas antes de se terem as regressões aleatórias ortogonais. Grande demanda computacional dos RRM foi reduzida pela remoção das regressões correspondentes a pequenas variâncias e também pela substituição dos efeitos aleatórios do erro por específica ponderação. Correlações entre DEPs, estimadas pelo MTM e pelo RRM para efeito aditivo direto, foram .87, .89, .89, .87 e .86 para W1 (peso aos 60 dias), W2 (peso aos 152 dias), W3 (peso aos 243 dias), W5 (peso aos 426 dias) e W7 (peso aos 601 dias), respectivamente. As correlações correspondentes, para efeito aditivo materno, foram .85, .86, .88, .85 e .84, respectivamente. Estimativas obtidas pelos RRM em informações ponderais de gado de corte podem não ser adequadas, em virtude das propriedades numéricas desses modelos. Em geral, baixas correlações são devidas a diferenças em variâncias entre modelos, número insuficiente de graus de liberdade para estimar os efeitos de ambiente e informações perdidas nos RRM.

The low-volatility anomaly can be described as the unexpected outperformance of low-volatility stocks compared to high-volatility stocks over the long-term. This dissertation investigates the low-volatility anomaly and its presence on the Johannesburg Stock Exchange (JSE). Possible reasons behind why low-volatility stocks consistently outperform their high volatility counterparts, as well as their own expected return, over the long-term are discussed. This includes analysing how financial risk is measured and whether this plays a role in obscuring the expected risk-return relationship, in addition to other fundamental factors impacting expected returns. It is found that the low-volatility anomaly is present on the JSE and that using a number of different risk metrics does not significantly change where a stock is ranked on the risk spectrum. Additionally, including an interest rate exposure factor, a value factor and a momentum factor lowers the unexpected portion (Alpha) of the returns of low volatility stocks, at the same time as narrowing the gap between the unexpected performance of the lowest and highest volatility stocks.

The purpose of this study is to investigate which groups of individuals are less likely to follow the authorities’ recommendations during the COVID-19 pandemic in Sweden. The thesis aims to explain the decision-making of these individuals during a pandemic based on the theory of Expected Utility. A linear probability model is estimated in addition to a logistic regression. The study finds that the estimated effect of gender and age are significantly different from zero when considering socioeconomic control variables. The results suggest that older individuals’ expected utility is higher when following the recommendations. For men as well as younger individuals, the theory needs an extended analysis including factors from a behavioural economics point of view. This paper is an addition to an increasing number of studies conducted on the COVID-19 pandemic. / Syftet med studien är att undersöka vilka individer som är mindre sannolika att följa de allmänna råd och rekommendationer som myndigheter uppmanar till under COVID-19 pandemin i Sverige samt att konkretisera potentiella bakgrunder till varför vissa individer väljer att inte följa de utifrån teorin om förväntad nytta. Tidigare studier har visat att män och yngre individer är mindre benägna att följa restriktioner under andra pandemier. Mot den bakgrunden formulerades uppsatsens hypoteser att män och yngre individer är mer sannolika att inte följa rekommendationer under pandemin i Sverige. En linjär sannolikhetsmodell och en logistisk regression estimerades, där ålder och kön var determinanter mot en binär utfallsvariabel definierad som 0 = följer rekommendationer och 1 = följer inte rekommendationer. Resultatet visade att kön och ålder uppvisade en effekt signifikant skild från noll även efter socioekonomiska kontroller. Utifrån förklaringsmodellen tyder resultatet på att äldre individer har en hög förväntad nytta av att följa rekommendationer. För män och yngre individer behöver teorin hämta stöd från beteendeekonomin. En fördjupande analys av individers riskpreferenser rekommenderas i framtiden för att ge tydliga rekommendationer till olika grupper i samhället.

Abstract Background: Tencent, as a listed company with strong comprehensive ability and also an early launch of equity incentive plan, is quite representative in its implementation of equity incentive plan.  Purpose: Tencent's equity incentive plan is the subject of this study, and a combination of theory and case studies is used to analyze Tencent's own development status and the current status of its equity incentive plan, and to further study the company's growth capability. Method: Qualitative analysis was conducted from both financial and non-financial indicators. In the study of financial indicators, Tencent's data from 2009-2019 was analyzed and verified that the degree of equity incentive has a significant correlation with the financial performance of the enterprise, and then specific indicators under the operating capacity, profitability, solvency and growth capacity of the enterprise were analyzed and the conclusion that equity incentive has a positive impact on the expected growth of the enterprise was drawn. Conclusion: Tencent's equity incentive has a positive effect on the future development of the company. The human resource department of the company can design a better incentive plan and more reasonable evaluation index to promote the effectiveness of the equity incentive policy of the company.

I analyzed the effects of Agriculture Risk Coverage (ARC) and Revenue Protection crop insurance (RP) on the RP coverage level by certainty equivalents and certainty equivalent returns. ARC is a commodity program that falls under Title I of the 2014 farm bill and triggers a payment for a participating producer once his actual revenue falls below a band of 76-86 percent of a calculated expected revenue. RP is a revenue-based crop insurance program that allows for a producer to sign up for one of eight different coverage levels ranging from 50-85 percent in 5 percent increments. This leads to the idea that in order to maximize his utility, a fully-informed, expected-utility maximizing producer should not choose to select full coverage RP but rather select the 75 percent RP and pair it with the ARC program. This analysis is conducted under the conceptual frameworks of expected-utility and cumulative prospect theory.