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Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange

Lunde, Asger; Olesen, Kasper V. - School of Economics and Management, University of Aarhus - 2014

analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH … framework for the joint modeling of returns and realized measures of volatility. An influential bias in these measures is … over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit …

Persistent link: https://www.econbiz.de/10010945126

Forecasting with the Standardized Self-Perturbed Kalman Filter

Grassi, Stefano; Nonejad, Nima; Magistris, Paolo Santucci de - School of Economics and Management, University of Aarhus - 2014

realized volatility series with a time-varying parameters HAR model with exogenous variables. …

Persistent link: https://www.econbiz.de/10010851262

Forecasting US Recessions: The Role of Sentiments

Christiansen, Charlotte; Eriksen, Jonas Nygaard; … - School of Economics and Management, University of Aarhus - 2013

We examine sentiment variables as new predictors for US recessions. We combine sentiment variables with either classical recession predictors or with common factors based on a large panel of macroeconomic and ?nancial variables. Sentiment variables hold vast predictive power for US recessions in...

Persistent link: https://www.econbiz.de/10010851274

Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions

Bollerslev, Tim; Patton, Andrew J.; Wang, Wenjing - School of Economics and Management, University of Aarhus - 2015

We construct daily house price indices for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the methodology of the popular monthly Case-Shiller...

Persistent link: https://www.econbiz.de/10011118617

Forecasting Long Memory Series Subject to Structural Change: A Two-Stage Approach

Dias, Gustavo Fruet; Papailias, Fotis - School of Economics and Management, University of Aarhus - 2014

A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the multi-step ahead forecasts...

Persistent link: https://www.econbiz.de/10011099291

Combining Long Memory and Level Shifts in Modeling and Forecasting the Volatility of Asset Returns

Varneskov, Rasmus Tangsgaard; Perron, Pierre - School of Economics and Management, University of Aarhus - 2011

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long memory and level shifts by decomposing the underlying process into a simple mixture model and ARFIMA dynamics. The Kalman filter is used to construct the likelihood function after...

Persistent link: https://www.econbiz.de/10009150791

Forecasting with Universal Approximators and a Learning Algorithm

Kock, Anders Bredahl - School of Economics and Management, University of Aarhus - 2009

This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the Kolmogorov-Gabor polynomials, as well as the Elliptic Basis Function Networks. Even though forecast combination has a long history in econometrics focus has not been on proving loss...

Persistent link: https://www.econbiz.de/10005012487

Forecasting autoregressive time series under changing persistenceCreation-Date: 20100701

Kruse, Robinson - School of Economics and Management, University of Aarhus

Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

Persistent link: https://www.econbiz.de/10008461102

Exploiting the Errors: A Simple Approach for Improved Volatility Forecasting

Bollerslev, Tim; Patton, Andrew J.; Quaedvlieg, Rogier - School of Economics and Management, University of Aarhus - 2015

We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the … asymptotic theory for high-frequency realized volatility estimation to improve the accuracy of the forecasts. By allowing the … existing models that implicitly ignore the temporal variation in the magnitude of the realized volatility measurement errors. …

Persistent link: https://www.econbiz.de/10011207425

Modelling and Forecasting Multivariate Realized Volatility

Chiriac, Roxana; Voev, Valeri - School of Economics and Management, University of Aarhus - 2008

This paper proposes a methodology for modelling time series of realized covariance matrices in order to forecast multivariate risks. The approach allows for flexible dynamic dependence patterns and guarantees positive definiteness of the resulting forecasts without imposing parameter...

Persistent link: https://www.econbiz.de/10005440044