Como a volatilidade é a única variável não observada nas fórmulas padrão de apreçamento de opções, o mercado financeiro utiliza amplamente o conceito de volatilidade implícita, isto é, a volatilidade que ao ser aplicada na fórmula de apreçamento resulte no preço correto (observado) das opções negociadas. Por isso, entender como as volatilidades implícitas das diversas opções de dólar negociadas na BM&F, o objeto de nosso estudo, variam ao longo do tempo e como estas se relacionam é importante para a análise de risco de carteiras de opções de dólar/real bem como para o apreçamento de derivativos cambiais exóticos ou pouco líquidos. A proposta de nosso estudo é, portanto, verificar se as observações da literatura técnica em diversos mercados também são válidas para as opções de dólar negociadas na BM&F: que as volatilidades implícitas não são constantes e que há uma relação entre as variações das volatilidades implícitas e as variações do valor do ativo objeto. Para alcançar este objetivo, aplicaremos a análise de componentes principais em nosso estudo. Com esta metodologia, reduziremos as variáveis aleatórias que representam o processo das volatilidades implícitas em um número menor de variáveis ortogonais, facilitando a análise dos dados obtidos. / Volatility is the only unobserved variable in the standard option pricing formulas and hence implied volatility is a concept widely adopted by the financial market, meaning the volatility which would make the formula yield the options real market price. Therefore, understanding how the implied volatility of the options on dollar traded at BM&F, the subject of our study, vary over time is important for risk analysis over dollar option books and for pricing of exotic or illiquid derivatives Our works proposal is to verify if the observations made by the technical literature over several markets could also be applied to the options on dollar traded at BM&F: implied volatilities do vary over time and there is a relation between this variation and the variation of the underlying asset price. In order to fulfill these goals, we will apply principal component analysis in our study. This methodology will help us analyze the data by reducing the number of variables that represent the implied volatility process into a few orthogonal variables.

We will compare two different forecasting models with the forecasting model that was used in March 2014 by The Swedish Social Insurance Agency ("Försäkringskassan" in Swedish or "FK") in this degree project. The models are used for forecasting the number of cases. The two models that will be compared with the model used by FK are the Seasonal Exponential Smoothing model (SES) and Auto-Regressive Integrated Moving Average (ARIMA) model. The models will be used to predict case volumes for two types of benefits: General Child Allowance “Barnbidrag” or (BB_ABB), and Pregnancy Benefit “Graviditetspenning” (GP_ANS). The results compare the forecast errors at the short time horizon (22) months and at the long-time horizon (70) months for the different types of models. Forecast error is the difference between the actual and the forecast value of case numbers received every month. The ARIMA model used in this degree project for GP_ANS had forecast errors on short and long horizons that are lower than the forecasting model that was used by FK in March 2014. However, the absolute forecast error is lower in the actual used model than in the ARIMA and SES models for pregnancy benefit cases. The results also show that for BB_ABB the forecast errors were large in all models, but it was the lowest in the actual used model (even the absolute forecast error). This shows that random error due to laws, rules, and community changes is almost impossible to predict. Therefore, it is not feasible to predict the time series with tested models in the long-term. However, that mainly depends on what FK considers as accepted forecast errors and how those forecasts will be used. It is important to mention that the implementation of ARIMA differs across different software. The best model in the used software in this degree project SAS (Statistical Analysis System) is not necessarily the best in other software.

Collateralization in over-the-counter (OTC) derivatives markets has grown rapidly over the past decade, and even faster in the past few years, due to the impact of the recent financial crisis and the particularly important attention to the counterparty credit risk in derivatives contracts. The addition of collateralization to such contracts significantly reduces the counterparty credit risk and allows to offset liabilities in case of default. We study the problem of valuation of OTC derivatives with payoff in a single currency and with single underlying asset for the cases of zero, partial, and perfect collateralization. We assume the derivative is traded between two default-free counterparties and analyze the impact of collateralization on the fair present value of the derivative. We establish a uniform generalized derivative pricing framework for the three cases of collateralization and show how different approaches to pricing turn out to be consistent. We then generalize the results to include multi-asset and cross-currency arguments, where the underlying and the derivative are in some domestic currency, but the collateral is posted in a foreign currency. We show that the results for the single currency, multi-asset case are consistent with those obtained for the single currency, single asset case.

No description available.

It is well established that stock market volatility has a memory of the past, moreover it is found that volatility correlations are long ranged. As a consequence, volatility cannot be characterized by a single correlation time in general. Recent empirical work suggests that the volatility correlation functions of various assets actually decay as a power law. Moreover it is well established that the distribution functions for the returns do not obey a Gaussian distribution, but follow more the type of distributions that incorporate what are commonly known as fat–tailed distributions. As a result, if one is to model the evolution of the stock price, stock market or any financial derivative, then standard Brownian motion models are inaccurate. One must take into account the results obtained from empirical studies and work with models that include realistic features observed on the market. In this thesis we show that it is possible to derive the path integral for a non-Gaussian option pricing model that can capture fat–tails. However we find that the path integral technique can only be used on a very small set of problems, as a number of situations of interest are shown to be intractable. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1378473 / Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2010

It is well established that stock market volatility has a memory of the past, moreover it is found that volatility correlations are long ranged. As a consequence, volatility cannot be characterized by a single correlation time in general. Recent empirical work suggests that the volatility correlation functions of various assets actually decay as a power law. Moreover it is well established that the distribution functions for the returns do not obey a Gaussian distribution, but follow more the type of distributions that incorporate what are commonly known as fat–tailed distributions. As a result, if one is to model the evolution of the stock price, stock market or any financial derivative, then standard Brownian motion models are inaccurate. One must take into account the results obtained from empirical studies and work with models that include realistic features observed on the market. In this thesis we show that it is possible to derive the path integral for a non-Gaussian option pricing model that can capture fat–tails. However we find that the path integral technique can only be used on a very small set of problems, as a number of situations of interest are shown to be intractable. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1378473 / Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2010

NJ, Univ., Dep. of Operations Research and Financial Engineering, Diss.--Princeton, 2005. / Kopie, ersch. im Verl. UMI, Ann Arbor, Mich. - Enth. 3 Beitr.

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished

Neste trabalho é apresentado um sistema autônomo de gestão de carteiras que utiliza Redes Neurais Artificiais para monitoramento do mercado e o modelo de Black-Litterman para otimização da alocação de patrimônio. O sistema analisa as dez ações mais negociadas do índice Bovespa, com redes neurais dedicadas a cada ação, e prevê estimativas de variações de preços para um dia no futuro a partir de indicadores da análise técnica. As estimativas das redes são então inseridas em um otimizador de carteiras, que utiliza o modelo de Black-Litterman, para compor carteiras diárias que empregam a estratégia Long and Short. Os resultados obtidos são comparados a um segundo sistema de trading autônomo, sem o emprego da otimização de carteiras. Foram observados resultados com ótimo índice de Sharpe em comparação ao Benchmark. Buscou-se, assim, contribuir com evidências a favor da utilização de modelos de inferência bayesiana utilizados junto à técnicas quantitativas para a gestão de patrimônio. / This work presents an autonomous portfolio management system which uses a Neural Network approach for monitoring the market and the Black-Litterman model for portfolio composition. The ten most traded assets from the Bovespa Index are analyzed, with dedicated neural networks, which suggests future return estimates using technical indicators as input. Those estimates are inserted in the Black-Litterman model which propose daily portfolio composition using long & short positions. The results are compared to a second autonomous trading system without the Black-Litterman approach. The results show great performance compared to the Benchmark, specially the risk and return relation, captured by the Sharpe Index. The work sought to bring positive evidences for the use of Bayesian Inference techniques in quantitative portfolio management.

Submitted by Maria Cristina (library@lncc.br) on 2015-07-27T15:48:13Z No. of bitstreams: 1 AJS1.pdf: 2780468 bytes, checksum: 3ef8d2a715329dfd670784063f307adb (MD5) / Approved for entry into archive by Maria Cristina (library@lncc.br) on 2015-07-27T18:05:09Z (GMT) No. of bitstreams: 1 AJS1.pdf: 2780468 bytes, checksum: 3ef8d2a715329dfd670784063f307adb (MD5) / Made available in DSpace on 2015-07-27T18:07:28Z (GMT). No. of bitstreams: 1 AJS1.pdf: 2780468 bytes, checksum: 3ef8d2a715329dfd670784063f307adb (MD5) Previous issue date: 2015-06-02 / Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq / Propomos um método numérico de diferenças finitas para substituir os esquemas clássicos utilizados para solucionar EDPs em engenharia financeira. A motivação para desenvolvê-lo advém da perda de precisão na tentativa de estabilizar a solução via up-wind no termo convectivo bem como o fato de que oscilações espúrias ocorrem quando a volatilidade é baixa, o que é comumente observado nos mercados de taxas de juros. Ao contrário dos esquemas clássicos, nosso método cobre todo o espectro de volatilidade da dinâmica das taxas de juros. Nós comparamos resultados analíticos e numéricos precificando e realizando o hedge de uma variedade de contratos financeiros de renda fixa para mostrar que o método que desenvolvemos é confiável e altamente competitivo. O método se adapta bem a derivativos exóticos de taxas de juros, incluindo um derivativo dependente da trajetória denominado Opção IDI (índice brasileiro de depósito interbancário). O método dá ênfase à abordagem realística da capitalização discreta do índice em detrimento da capitalização contínua explorada frequentemente na literatura. / We propose a second order accurate numerical finite difference method to replace the classical schemes used to solving PDEs in financial engineering. The motivation for doing so stems from the accuracy loss while trying to stabilize the solution via the up-wind trick in the convective term as well as the fact that spurious oscillation solutions occur when volatilities are low. This is actually the range that we commonly observe in the interest rate markets. Unlike the classical schemes, our method covers the whole spectrum of volatilities in the interest rate dynamics. We compare the analytical and numerical results by both pricing and hedging a variety of fixed income financial contracts to show that the method we developed is reliable and highly competitive. The method adapts well to exotic interest rate derivative securities, including a path-dependent derivative named IDI (the Brazilian Interbank Deposit Rate Index) option. The method highlights the use of the realistic discretely compounding interest rate scheme, in detriment of the continuously compounding case often exploited in the literature.