-frequency models, we find that our simple Gaussian VAR forecasts generally produce superior forecasts. Furthermore, we show that, given the theoretically motivated and empirically plausible assumption of normally distributed returns conditional on the realized volatilities, the resulting lognormal-normal mixture

-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal

institution for postgraduate education and research in economics and the social sciences in Austria.

In this thesis we study the log-normal mixture option pricing model proposed by Brigo and Mercurio [1]. This model is of particular interest since it is an analytically tractable generalization of the Black-Scholes option pricing model, but essentially of the same degree of complexity when it comes

The two most common censoring schemes used in life testing experiments are Type-I and Type-II censoring schemes. Hybrid censoring scheme is mixture of Type-I and Type-II censoring scheme. In this work we consider the estimation of parameters of log-normal distribution based on hybrid censored data

by multiple agents, which do not estimate state of the whole system but only its local ‘state’. The resulting estimate is obtained by merging of marginal and conditional posterior probability density functions (pdf) on such local states. We investigate the use of recently proposed non-parametric log-normal

We investigate two important properties of real data: diversity and log-normality. Log-normality accounts for the fact that data follow the lognormal distribution, whereas diversity measures variations of the at-tributes in the data. To our knowledge, these two inherent properties have not been

the usefulness of these distributions in two applications. In the first application, we produce density forecasts of U.S. inflation and show that these forecasts are more accurate, out-of-sample, than density forecasts obtained using normal or standard t-distributions. In the second application, we replicate

values at near-radiative equilibrium to a plateau of about 6000 K. We study raster images and intensity distribution histograms and find that a single log-normal distribution matches these observations very well, and that the spatial structure observed corresponds to a mixture of features

autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate