This document reviews and applies recently developed techniques for Bayesian estimation and model selection in the context of Time Series modelingfor Stochastic volatility. After the literature review on Generalized Conditional Autoregressive models, Stochastic Volatility models, and the...

Complexity in the financial markets requires intelligent forecasting models for return volatility. In this paper, historical simulation, GARCH, GARCH with skewed student-t distribution and asymmetric normal mixture GRJ-GARCH models are combined with Extreme Value Theory Hill by using artificial...

In this paper we propose a feasible way to price American options in a model with time varying volatility and conditional skewness and leptokurtosis using GARCH processes and the Normal Inverse Gaussian distribution. We show how the risk neutral dynamics can be obtained in this model, we...

Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly...

The aim of this paper is twofold. First it aims to compare several GARCH family models in order to model and forecast the conditional variance of German, Swiss, and UK stock market indexes. The main result is that all GARCH family models show evidence of asymmetric effects. Based on the “out...

Recently, Fagiolo et al. (2008) find fat tails of economic growth rates after adjusting outliers, autocorrelation and heteroskedasticity. This paper employs US quarterly real output growth, showing that this finding of fat tails may reflect the Great Moderation. That is, leptokurtosis disappears...

In this paper we consider a class of conditionally Gaussian state space models and discuss how they can provide a flexible and fairly simple tool for modelling financial time series, even in presence of different components in the series, or of stochastic volatility. Estimation can be computed...

Using two data series, namely GDP and the index of industrial production, we study the relationship between output variability and the growth rate of output. Ng-Perron unit root test shows that the growth rate of GDP is non-stationary but the growth rate of industrial output is stationary. Thus,...

This paper compares alternative time-varying volatility models for daily stock-returns using data from Spanish equity index IBEX-35. Specifically, we have estimated a parametric family of models of generalized autoregressive heteroskedasticity (which nests the most popular symmetric and...

In recent years multivariate models for asset returns have received much attention, in particular this is the case for models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes to option pricing. Specifically, we derive the risk...