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Macroeconomics, nonlinearities, and the business cycle

Reif, Magnus - 2019

Im Zentrum dieser Dissertation steht das Beschreiben und Erklären von Konjunkturdynamiken. Motiviert durch den außerordentlich starken wirtschaftlichen Einbruch in 2008/2009 betont die Arbeit dabei die Wichtigkeit der Nutzung von nichtlinearen Modellansätzen. Die Dissertation kann als Beitrag...

Persistent link: https://www.econbiz.de/10012154125

Stochastic Volatility Models with ARMA Innovations an Application to G7 Inflation Forecasts

Zhang, Bo - 2018

We introduce a new class of stochastic volatility models with autoregressive moving average (ARMA) innovations. The conditional mean process has a flexible form that can accommodate both a state space representation and a conventional dynamic regression. The ARMA component introduces serial...

Persistent link: https://www.econbiz.de/10012913784

Stochastic Volatility Models with ARMA Innovations : An Application to G7 Inflation Forecasts

Zhang, Bo - 2018

Persistent link: https://www.econbiz.de/10012915821

Deep Bayesian Estimation of the Rough Path-Dependent Volatility Model for Forecasting Purposes

Parent, Léo - 2023

with a second DNN. After formalizing the estimation problem within the framework of Bayesian decision theory, the article …

Persistent link: https://www.econbiz.de/10014354222

The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk

Dobrev, Dobrislav - 2011

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

Persistent link: https://www.econbiz.de/10013128339

Volatility Forecasting and Explanatory Variables : A Tractable Bayesian Approach to Stochastic Volatility

Dorion, Christian; Chapados, Nicolas - 2013

We provide a formulation of stochastic volatility (SV) based on Gaussian process regression (GPR). Forecasting volatility out-of-sample, both simulation and empirical analyses show that our GPR-based stochastic volatility (GPSV) model clearly outperforms SV and GARCH benchmarks, especially at...

Persistent link: https://www.econbiz.de/10014186681

A MIDAS Approach to Modeling First and Second Moment Dynamics

Pettenuzzo, Davide - 2015

standard Gibbs sampling methods. When applied to monthly time series on growth in industrial production and inflation, we find …

Persistent link: https://www.econbiz.de/10013033107

Asymmetric Quantile Persistence and Predictability : The Case of U.S. Inflation

Manzan, Sebastiano - 2013

The aim of this paper is to investigate the evidence and implications of time-variation and asymmetry in the …

Persistent link: https://www.econbiz.de/10013089921

The variability and volatility of sleep: an archetypal approach

Hamermesh, Daniel S.; Pfann, Gerard A. - 2022

Using Dutch time-diary data from 1975-2005 covering over 10,000 respondents for 7 consecutive days each, we show that … individuals' sleep time exhibits both variability and volatility characterized by stationary autoregressive conditional … independent of other demographics. A theory of economic incentives to minimize the dispersion of sleep predicts that higher …

Persistent link: https://www.econbiz.de/10012805079

Real-Time Density Forecasts from VARs with Stochastic Volatility

Clark, Todd E. - 2013

density forecasting. Accordingly, this paper examines, with real-time data, density forecasts of U.S. GDP growth, unemployment … improves the real-time accuracy of point and density forecasts …

Persistent link: https://www.econbiz.de/10013095864