Estudos recentes relacionados ao meio ambiente vêm ganhando grande destaque em todo o mundo devido ao fato dos níveis de poluição e a destruição das reservas naturais terem aumentado de maneira alarmante nos últimos anos. As grandes cidades são as que mais sofrem com a poluição e aqui serão estudados os níveis de poluição do ar em duas cidades em particular, a Cidade do México e São Paulo. A Cidade do México apresenta sérios problemas com os níveis de ozônio e São Paulo é a cidade brasileira com os maiores problemas relacionados à poluição. Entre os diferentes modelos considerados para analisar dados de poluição do ar, pode-se considerar o uso de modelos de séries temporais para modelar as médias diárias ou semanais de poluição. Nessa direção pode-se usar modelos de volatilidade estocástica. Essa família de modelos estatísticos tem sido extensivamente usada para analisar séries temporais financeiras, porém não se observa muitas aplicações em dados ambientais e de saúde. Modelos de volatilidade estocástica bivariados e multivariados, sob a aproximação Bayesiana, foram considerados para analisar os dados, especialmente usando métodos MCMC (Monte Carlo em Cadeias de Markov) para obter os sumários a posteriori de interesse, pois pode-se ter muitas dificuldades usando métodos clássicos de inferência estatística / Recent studies related to environmental has been considered in all world due to increasing levels of pollution and of natural resources destruction especially, in the last years. The largest cities in the world are the ones been mostly affected by pollution and in this work we consider the analysis of air pollution data of two important cities: Mexico City and São Paulo. The Mexico City presents serious problems of ozone levels and São Paulo is the Brazilian city with the largest problems related to air pollution. Among the different models which could be used to analyze air pollution data, we consider the use of time series modeling to the weekly or daily levels of pollution. In this way, we consider the use of volatility stochastic models. This family of models has been well explored with financial data but not well explored to analyze environmental and health data. Bivariate and multivariate stochastic models under the Bayesian approach were considered to analyze the data, especially using MCMC (Markov Chain Monte Carlo) methods to obtain the posterior summary of interest, since we usually have big difficulties using standard classical inference methods

We analyze the relation between volatility spillovers and jumps in financial markets. For this, we compared the volatility spillover index proposed by Diebold and Yilmaz (2009) with a global volatility component, estimated through a multivariate stochastic volatility model with jumps in the mean and in the conditional volatility. This model allows a direct dating of events that alter the global volatility structure, based on a permanent/transitory decomposition in the structure of returns and volatilities, and also the estimation of market risk measures. We conclude that the multivariate stochastic volatility model solves some limitations in the spillover index and can be a useful tool in measuring and managing risk in global financial markets. / Analisamos a relação existente entre spillovers e saltos na volatilidade nos mercados financeiros. Para isso, comparamos o índice de spillover de volatilidade proposto por Diebold and Yilmaz (2009), com um componente de volatilidade global, estimado através de um modelo multivariado de volatilidade estocástica com saltos na média e na volatilidade condicional. Este modelo permite uma datação direta dos eventos que alteram a estrutura de volatilidade global, baseando-se na decomposição das estruturas de retorno e volatilidade entre efeitos permanentes/transitórios, como também a estimação de medidas de risco de mercado. Concluímos que este modelo resolve algumas das limitações do índice de spillover além de fornecer um método prático para mensurar e administrar o risco nos mercados financeiros globais.

This diploma thesis deals with problem of option pricing with stochastic volatility. At first, the Black-Scholes model is derived and then its biases are discussed. We explain shortly the concept of volatility. Further, we introduce three pricing models with stochastic volatility- Hull-White model, Heston model and Stein-Stein model. At the end, these models are reviewed.

Estudos recentes relacionados ao meio ambiente vêm ganhando grande destaque em todo o mundo devido ao fato dos níveis de poluição e a destruição das reservas naturais terem aumentado de maneira alarmante nos últimos anos. As grandes cidades são as que mais sofrem com a poluição e aqui serão estudados os níveis de poluição do ar em duas cidades em particular, a Cidade do México e São Paulo. A Cidade do México apresenta sérios problemas com os níveis de ozônio e São Paulo é a cidade brasileira com os maiores problemas relacionados à poluição. Entre os diferentes modelos considerados para analisar dados de poluição do ar, pode-se considerar o uso de modelos de séries temporais para modelar as médias diárias ou semanais de poluição. Nessa direção pode-se usar modelos de volatilidade estocástica. Essa família de modelos estatísticos tem sido extensivamente usada para analisar séries temporais financeiras, porém não se observa muitas aplicações em dados ambientais e de saúde. Modelos de volatilidade estocástica bivariados e multivariados, sob a aproximação Bayesiana, foram considerados para analisar os dados, especialmente usando métodos MCMC (Monte Carlo em Cadeias de Markov) para obter os sumários a posteriori de interesse, pois pode-se ter muitas dificuldades usando métodos clássicos de inferência estatística / Recent studies related to environmental has been considered in all world due to increasing levels of pollution and of natural resources destruction especially, in the last years. The largest cities in the world are the ones been mostly affected by pollution and in this work we consider the analysis of air pollution data of two important cities: Mexico City and São Paulo. The Mexico City presents serious problems of ozone levels and São Paulo is the Brazilian city with the largest problems related to air pollution. Among the different models which could be used to analyze air pollution data, we consider the use of time series modeling to the weekly or daily levels of pollution. In this way, we consider the use of volatility stochastic models. This family of models has been well explored with financial data but not well explored to analyze environmental and health data. Bivariate and multivariate stochastic models under the Bayesian approach were considered to analyze the data, especially using MCMC (Markov Chain Monte Carlo) methods to obtain the posterior summary of interest, since we usually have big difficulties using standard classical inference methods

In this paper, we estimate a Bayesian vector autoregressive (VAR) model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area (EA). This allows us to incorporate uncertainty directly into the econometric framework and treat it as a latent quantity. Only a limited number of papers estimates impacts of uncertainty and macroeconomic consequences jointly, and most literature in this sphere is based on single countries. We analyze the special case of a shock restricted to the Euro area, whose countries are highly related by definition. Among other variables, we find significant results of a decrease in real activity measured by GDP in most Euro area countries over a period of roughly a year following an uncertainty shock. / Series: Department of Economics Working Paper Series

In this paper, we reconsider the question how monetary policy influences exchange rate dynamics. To this end, a vector autoregressive (VAR) model is combined with a two-country dynamic stochastic general equilibrium (DSGE) model. Instead of focusing exclusively on how monetary policy shocks affect the level of exchange rates, we also analyze how they impact exchange rate volatility. Since exchange rate volatility is not observed, we estimate it alongside the remaining quantities in the model. Our findings can be summarized as follows. Contractionary monetary policy shocks lead to an appreciation of the home currency, with exchange rate responses in the short-run typically undershooting their long-run level of appreciation. They also lead to an increase in exchange rate volatility. Historical and forecast error variance decompositions indicate that monetary policy shocks explain an appreciable amount of exchange rate movements and the corresponding volatility. / Series: Department of Economics Working Paper Series

Derivative pricing, model calibration, and sensitivity analysis are the three main problems in financial modeling. The purpose of this study is to present an algorithm to improve the pricing process, the calibration process, and the sensitivity analysis of the double Heston model, in the sense of accuracy and efficiency. Using the optimized caching technique, our study reduces the pricing computation time by about 15%. Another contribution of this thesis is: a novel application of the Automatic Differentiation (AD) algorithms in order to achieve a more stable, more accurate, and faster sensitivity analysis for the double Heston model (compared to the classical finite difference methods). This thesis also presents a novel hybrid model by combing the heuristic method Differentiation Evolution, and the gradient method Levenberg--Marquardt algorithm. Our new hybrid model significantly accelerates the calibration process.

This thesis examines the empirical performance of four Affine Jump Diffusion models in pricing and hedging S&P 500 Index options: the Black Scholes (BS) model, Heston???s Stochastic Volatility (SV) model, a Stochastic Volatility Price Jump (SVJ) model and a Stochastic Volatility Price-Volatility Jump (SVJJ) model. The SVJJ model structure allows for simultaneous jumps in price and volatility processes, with correlated jump size distributions. To the best of our knowledge this is the first empirical study to test the hedging performance of the SVJJ model. As part of our research we derive the SVJJ model minimum variance hedge ratio. We find the SVJ model displays the best price prediction. The SV model lacks the structural complexity to eliminate Black Scholes pricing biases, whereas our results indicate the SVJJ model suffers from overfitting. Despite significant evidence from in and out-of-sample pricing that the SV and SVJ models were better specified than the BS model, this did not result in an improvement in dynamic hedging performance. Overall the BS delta hedge and SV minimum variance hedge produced the lowest errors, although their performance across moneyness-maturity categories differed greatly. The SVJ model???s results were surprisingly poor given its superior performance in out-of-sample pricing. We attribute the inadequate performance of the jump models to the lower hedging ratios these models provided, which may be a result of the negative expected jump sizes.

In this thesis I discuss the method of time-change and its applications in quantitative finance. I mainly consider the time change by writing a continuous diffusion process as a Brownian motion subordinated by a subordinator process. I divide the time change method into two cases: deterministic time change and stochastic time change. The difference lies in whether the subordinator process is a deterministic function of time or a stochastic process of time. Time-changed Brownian motion with deterministic time change provides a new viewpoint to deal with option pricing under stochastic interest rates and I utilize this idea in pricing various exotic options under stochastic interest rates. Time-changed Brownian motion with stochastic time change is more complicated and I give the equivalence in law relation governing the ``original time" and the ``new stochastic time" under different clocks. This is readily applicable in pricing a new product called ``timer option". It can also be used in pricing barrier options under the Heston stochastic volatility model. Conclusion and further research directions in exploring the ideas of time change method in other areas of quantitative finance are in the last chapter.

In this thesis I discuss the method of time-change and its applications in quantitative finance. I mainly consider the time change by writing a continuous diffusion process as a Brownian motion subordinated by a subordinator process. I divide the time change method into two cases: deterministic time change and stochastic time change. The difference lies in whether the subordinator process is a deterministic function of time or a stochastic process of time. Time-changed Brownian motion with deterministic time change provides a new viewpoint to deal with option pricing under stochastic interest rates and I utilize this idea in pricing various exotic options under stochastic interest rates. Time-changed Brownian motion with stochastic time change is more complicated and I give the equivalence in law relation governing the ``original time" and the ``new stochastic time" under different clocks. This is readily applicable in pricing a new product called ``timer option". It can also be used in pricing barrier options under the Heston stochastic volatility model. Conclusion and further research directions in exploring the ideas of time change method in other areas of quantitative finance are in the last chapter.