The mean-variance portfolio optimization theory of Markowitz assumes that stock returns are distributed according to normal probability density functions (pdfs). In reality, stock returns are more accurately described by leptokurtic pdfs which have kurtosis greater than zero. Stocks with...

This paper aims to propose an estimation procedure for the parameters of a generalized fractional process, a fairly general model of long-memory whose auto correlations exhibit periodic and hyperbolic decay. We derive a wavelet-based weighted least squares estimator of the long-memory parameter...

The success of modern portfolio theory critically depends on the use of reliable estimates of the covariance matrix of asset returns. Current statistical theory provides a variety of different models ranging from simple sample estimates to complex multivariate GARCH models to be used in...

The family of Generalized Empirical Likelihood (GEL) estimators provide a number of potential advantages relative to Generalized Method of Moments (GMM) estimators. While it is well known these estimators share an asymptotic distribution, the GEL estimators may perform better in finite sample,...

This paper employes a parametric model of structural breaks in the mean of stock returns which allows them to be endogenously driven by large positive or negative stock market return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime shifts and/or...

This work studies regional fluctuations in the EU12 focusing on regional Gross Domestic Product (GDP) and Employment dynamics over the period 1977-95. The econometric framework is a combination of the Structural Dynamic Factor Model by Forni and Reichlin (1998) and the Dynamic Factor Model by...

This paper proposes some improvements to advanced measurement approach (AMA) to modelling operational losses and applies this approach to US business losses. The AMA involves, among others, modelling a loss severity distribution and estimating its Expected Loss and the 99.9% operational...

Three models are presented: AR (autoregressive), MA (moving average) and ARMA (autoregressive moving average) are common models used in time series forecasting. These three models are the various definition of each element of the General Linear Model: Y = a + b + c. For the study of linear...

In order to discuss nonlinear, it is necessary to know linear regressive as a priori. Without simple regression as the starting point, it would be difficult to understand nonlinear regression. In words, in order to understand the curve and the behavior of a curve, it is necessary to known a...