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Forecasting
20
realized volatility
9
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fractional cointegration
7
long memory
7
stochastic volatility
7
likelihood inference
6
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5
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option pricing
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Model-Free Implied Volatility
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Johansen, Søren
14
Teräsvirta, Timo
14
Nielsen, Morten Ørregaard
10
Bollerslev, Tim
9
Engsted, Tom
9
Pedersen, Thomas Q.
8
Rahbek, Anders
8
Stentoft, Lars
8
Andersen, Torben G.
7
Magistris, Paolo Santucci de
7
Proietti, Tommaso
7
Christensen, Bent Jesper
6
Christiansen, Charlotte
6
Grassi, Stefano
6
Kock, Anders Bredahl
6
Voev, Valeri
6
Kristensen, Dennis
5
Nonejad, Nima
5
Rombouts, Jeroen V.K.
5
Tauchen, George
5
Bach, Christian
4
Hansen, Peter Reinhard
4
Lasak, Katarzyna
4
Lunde, Asger
4
Møller, Stig V.
4
Nielsen, Bent
4
Taylor, A.M. Robert
4
Timmermann, Allan
4
Todorov, Viktor
4
Amado, Cristina
3
Callot, Laurent A.F.
3
Carlini, Federico
3
Cavaliere, Giuseppe
3
Dziubinski, Matt P.
3
Kristensen, Johannes Tang
3
Lanne, Markku
3
Medeiros, Marcelo C.
3
Osterrieder, Daniela
3
Silvennoinen, Annastiina
3
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School of Economics and Management, University of Aarhus
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RePEc
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1
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
A modification of the self-perturbed Kalman filter of Park and Jun (1992) is proposed for the on-line estimation of models subject to parameter instability. The perturbationterm in the updating equation of the state covariance matrix is weighted by the measurement error variance, thus avoiding...
Persistent link: https://www.econbiz.de/10010851262
Saved in:
2
The Value of Multivariate Model Sophistication: An Application to pricing Dow Jones Industrial Average options
Rombouts, Jeroen V.K.
;
Stentoft, Lars
;
Violante, Francesco
-
School of Economics and Management, University of Aarhus
-
2012
We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their...
Persistent link: https://www.econbiz.de/10009492823
Saved in:
3
The Model Confidence Set
Hansen, Peter R.
;
Lunde, Asger
;
Nason, James M.
-
School of Economics and Management, University of Aarhus
-
2010
forecasting
problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare …
Persistent link: https://www.econbiz.de/10008784441
Saved in:
4
Oracle Efficient Estimation and
Forecasting
with the Adaptive LASSO and the Adaptive Group LASSO in Vector Autoregressions
Kock, Anders Bredahl
;
Callot, Laurent A.F.
-
School of Economics and Management, University of Aarhus
-
2012
variable selection and estimation in one step. We evaluate the
forecasting
accuracy of these estimators for a large set of …
Persistent link: https://www.econbiz.de/10010851261
Saved in:
5
Forecasting
Medium and Large Datasets with Vector Autoregressive Moving Average (VARMA) Models
Dias, Gustavo Fruet
;
Kapetanios, George
-
School of Economics and Management, University of Aarhus
-
2014
We address the issue of modelling and
forecasting
macroeconomic variables using medium and large datasets, by adopting …
Persistent link: https://www.econbiz.de/10010940885
Saved in:
6
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
Saved in:
7
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
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8
Global Hemispheric Temperature Trends and Co–Shifting: A Shifting Mean Vector Autoregressive Analysis
Holt, Matthew T.
;
Teräsvirta, Timo
-
School of Economics and Management, University of Aarhus
-
2012
This paper examines trends in annual temperature data for the northern and southern hemisphere (1850-2010) by using variants of the shifting-mean autoregressive (SM-AR) model of González and Teräsvirta (2008). Univariate models are first fitted to each series by using the so called QuickShift...
Persistent link: https://www.econbiz.de/10010851222
Saved in:
9
Thresholds and Smooth Transitions in Vector Autoregressive Models
Hubrich, Kirstin
;
Teräsvirta, Timo
-
School of Economics and Management, University of Aarhus
-
2013
This survey focuses on two families of nonlinear vector time series models, the family of Vector Threshold Regression models and that of Vector Smooth Transition Regression models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in...
Persistent link: https://www.econbiz.de/10010886057
Saved in:
10
EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area
Proietti, Tommaso
;
Marczak, Martyna
;
Mazzi, Gianluigi
-
School of Economics and Management, University of Aarhus
-
2015
EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model...
Persistent link: https://www.econbiz.de/10011186679
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