We consider the stochastic volatility model with smooth transition and persistent la- tent factors. We argue that this model has advantages over the conventional stochastic model for the persistent volatility factor. Though the linear filtering is widely used in the state space model, the simulation result, as well as theory, shows that it does not work in our model. So we apply the density-based filtering method; in particular, we develop two methods to get solutions. One is the conventional approach using the Maximum Likelihood estimation and the other is the Bayesian approach using Gibbs sampling. We do a simulation study to explore their characteristics, and we apply both methods to actual macroeconomic data to extract the volatility generating process and to compare macro fundamentals with them. Next we extend our model into multivariate model extracting common and id- iosyncratic volatility for multivariate processes. We think it is interesting to apply this multivariate model into measuring time-varying uncertainty of macroeconomic variables and studying the links to market returns via a consumption-based asset pric- ing model. Motivated by Bansal and Yaron (2004), we extract a common volatility factor using consumption and dividend growth, and we find that this factor predicts post-war business cycle recessions quite well. Then, we estimate a long-run risk model of asset prices incorporating this macroeconomic uncertainty. We find that both risk aversion and the intertemporal elasticity of substitution are estimated to be around two, and our simulation results show that the model can match the first and second moments of market return and risk-free rate, hence the equity premium.

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

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 provide a parsimonious means of estimating panel VARs with stochastic volatility. We assume that coefficients associated with domestic lagged endogenous variables arise from a finite mixture of Gaussian distribution. Shrinkage on the cluster size is introduced through suitable priors on the component weights and cluster-relevant quantities are identified through novel normal-gamma shrinkage priors. To assess whether dynamic interdependencies between units are needed, we moreover impose shrinkage priors on the coefficients related to other countries' endogenous variables. Finally, our model controls for static interdependencies by assuming that the reduced form shocks of the model feature a factor stochastic volatility structure. We assess the merits of the proposed approach by using synthetic data as well as a real data application. In the empirical application, we forecast Eurozone unemployment rates and show that our proposed approach works well in terms of predictions. / Series: Department of Economics Working Paper Series

<p>In this thesis we have created a computer program in Java language which calculates European call- and put options with four different models based on the article The Pricing of Options on Assets with Stochastic Volatilities by John Hull and Alan White. Two of the models use stochastic volatility as an input. The paper describes the foundations of stochastic volatility option pricing and compares the output of the models. The model which better estimates the real option price is dependent on further research of the model parameters involved.</p>

In this thesis we have created a computer program in Java language which calculates European call- and put options with four different models based on the article The Pricing of Options on Assets with Stochastic Volatilities by John Hull and Alan White. Two of the models use stochastic volatility as an input. The paper describes the foundations of stochastic volatility option pricing and compares the output of the models. The model which better estimates the real option price is dependent on further research of the model parameters involved.

This thesis focuses on comparing an online parameter estimator to an offline estimator, both based on the PaRIS-algorithm, when estimating parameter values for a stochastic volatility model. By modeling the stochastic volatility model as a hidden Markov model, estimators based on particle filters can be implemented in order to estimate the unknown parameters of the model. The results from this thesis implies that the proposed online estimator could be considered as a superior method to the offline counterpart. The results are however somewhat inconclusive, and further research regarding the subject is recommended. / Detta examensarbetefokuserar på att jämföra en online och offline parameter-skattare i stokastiskavolatilitets modeller. De två parameter-skattarna som jämförs är båda baseradepå PaRIS-algoritmen. Genom att modellera en stokastisk volatilitets-model somen dold Markov kedja, kunde partikelbaserade parameter-skattare användas föratt uppskatta de okända parametrarna i modellen. Resultaten presenterade idetta examensarbete tyder på att online-implementationen av PaRIS-algorimen kanses som det bästa alternativet, jämfört med offline-implementationen.Resultaten är dock inte helt övertygande, och ytterligare forskning inomområdet

Este trabalho apresenta um estudo através da abordagem Bayesiana em modelos de volatilidade estocástica, para modelagem de séries temporais financeiras, com o uso do método de Monte Carlo Hamiltoniano (HMC). Propomos o uso de outras distribuições para os erros da equação de observações do modelos de volatilidade estocástica, além da distribuição Gaussiana, para tratar problemas como caudas pesadas e assimetria nos retornos. Além disso, utilizamos critérios de informações, recentemente desenvolvidos, WAIC e LOO que aproximam a metodologia de validação cruzada, para realizar a seleção de modelos. No decorrer do trabalho, estudamos a qualidade do método HMC através de exemplos, estudo de simulação e aplicação a conjunto de dados. Adicionalmente, avaliamos a performance dos modelos e métodos propostos através do cálculo de estimativas para o Valor em Risco (VaR) para múltiplos horizontes de tempo. / This paper presents a study using Bayesian approach in stochastic volatility models for modeling financial time series, using Hamiltonian Monte Carlo methods (HMC). We propose the use of other distributions for the errors of the equation at stochastic volatiliy model, besides the Gaussian distribution, to treat the problem as heavy tails and asymmetry in the returns. Moreover, we use recently developed information criteria WAIC and LOO that approximate the crossvalidation methodology, to perform the selection of models. Throughout this work, we study the quality of the HMC methods through examples, simulation study and application to dataset. In addition, we evaluated the performance of the proposed models and methods by calculating estimates for Value at Risk (VaR) for multiple time horizons.

La première partie de cette thèse est consacrée aux méthodes numériques pour la simulation de processus aléatoires définis par des équations différentielles stochastiques (EDS). Nous commençons par l’étude de l’algorithme de Beskos et al. [13] qui permet de simuler exactement les trajectoires d’un processus solution d’une EDS en dimension 1. Nous en proposons une extension à des fins de calcul exact d’espérances et nous étudions l’application de ces idées à l’évaluation du prix d’options asiatiques dans le modèle de Black & Scholes. Nous nous intéressons ensuite aux schémas numériques. Dans le deuxième chapitre, nous proposons deux schémas de discrétisation pour une famille de modèles à volatilité stochastique et nous en étudions les propriétés de convergence. Le premier schéma est adapté à l’évaluation du prix d’options path-dependent et le deuxième aux options vanilles. Nous étudions également le cas particulier où le processus qui dirige la volatilité est un processus d’Ornstein-Uhlenbeck et nous exhibons un schéma de discrétisation qui possède de meilleures propriétés de convergence. Enfin, dans le troisième chapitre, il est question de la convergence faible trajectorielle du schéma d’Euler. Nous apportons un début de réponse en contrôlant la distance de Wasserstein entre les marginales du processus solution et du schéma d’Euler, uniformément en temps. La deuxième partie de la thèse porte sur la modélisation de la dépendance en finance et ce à travers deux problématiques distinctes : la modélisation jointe entre un indice boursier et les actions qui le composent et la gestion du risque de défaut dans les portefeuilles de crédit. Dans le quatrième chapitre, nous proposons un cadre de modélisation original dans lequel les volatilités de l’indice et de ses composantes sont reliées. Nous obtenons un modèle simplifié quand la taille de l’indice est grande, dans lequel l’indice suit un modèle à volatilité locale et les actions individuelles suivent un modèle à volatilité stochastique composé d’une partie intrinsèque et d’une partie commune dirigée par l’indice. Nous étudions la calibration de ces modèles et montrons qu’il est possible de se caler sur les prix d’options observés sur le marché, à la fois pour l’indice et pour les actions, ce qui constitue un avantage considérable. Enfin, dans le dernier chapitre de la thèse, nous développons un modèle à intensités permettant de modéliser simultanément, et de manière consistante, toutes les transitions de ratings qui surviennent dans un grand portefeuille de crédit. Afin de générer des niveaux de dépendance plus élevés, nous introduisons le modèle dynamic frailty dans lequel une variable dynamique inobservable agit de manière multiplicative sur les intensités de transitions. Notre approche est purement historique et nous étudions l’estimation par maximum de vraisemblance des paramètres de nos modèles sur la base de données de transitions de ratings passées / The first part of this thesis deals with probabilistic numerical methods for simulating the solution of a stochastic differential equation (SDE). We start with the algorithm of Beskos et al. [13] which allows exact simulation of the solution of a one dimensional SDE. We present an extension for the exact computation of expectations and we study the application of these techniques for the pricing of Asian options in the Black & Scholes model. Then, in the second chapter, we propose and study the convergence of two discretization schemes for a family of stochastic volatility models. The first one is well adapted for the pricing of vanilla options and the second one is efficient for the pricing of path-dependent options. We also study the particular case of an Orstein-Uhlenbeck process driving the volatility and we exhibit a third discretization scheme which has better convergence properties. Finally, in the third chapter, we tackle the trajectorial weak convergence of the Euler scheme by providing a simple proof for the estimation of the Wasserstein distance between the solution and its Euler scheme, uniformly in time. The second part of the thesis is dedicated to the modelling of dependence in finance through two examples : the joint modelling of an index together with its composing stocks and intensity-based credit portfolio models. In the forth chapter, we propose a new modelling framework in which the volatility of an index and the volatilities of its composing stocks are connected. When the number of stocks is large, we obtain a simplified model consisting of a local volatility model for the index and a stochastic volatility model for the stocks composed of an intrinsic part and a systemic part driven by the index. We study the calibration of these models and show that it is possible to fit the market prices of both the index and the stocks. Finally, in the last chapter of the thesis, we define an intensity-based credit portfolio model. In order to obtain stronger dependence levels between rating transitions, we extend it by introducing an unobservable random process (frailty) which acts multiplicatively on the intensities of the firms of the portfolio. Our approach is fully historical and we estimate the parameters of our model to past rating transitions using maximum likelihood techniques