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Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Hol, Eugenie; Koopman, Siem Jan - Tinbergen Instituut - 2000
returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …
Persistent link: https://www.econbiz.de/10011255461
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Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment
Koopman, Siem Jan; Ooms, Marius; Hindrayanto, Irma - Tinbergen Institute - 2006
This paper discusses identification, specification, estimation and forecasting for a general class of periodic … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …
Persistent link: https://www.econbiz.de/10005137026
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Likelihood-based Analysis for Dynamic Factor Models
Jungbacker, Borus; Koopman, Siem Jan - Tinbergen Institute - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10005137376
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The Stochastic Volatility in Mean Model
Koopman, Siem Jan; Uspensky, Eugenie Hol - Tinbergen Institute - 2000
In this paper we present an exact maximum likelihood treatment for the estimation of a Stochastic Volatility in Mean (SVM) model based on Monte Carlo simulation methods. The SVM model incorporates the unobserved volatility as an explanatory variable in the mean equation. The same extension is...
Persistent link: https://www.econbiz.de/10005281817
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Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility
Hol, Eugenie; Koopman, Siem Jan - Tinbergen Institute - 2000
daily squared returns and intradaily squared returns for forecasting horizons ranging from 1 to 10 days. For the daily …
Persistent link: https://www.econbiz.de/10005281987
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Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models
Blasques, Francisco; Koopman, Siem Jan; Mallee, Max - Tinbergen Instituut - 2014
The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...
Persistent link: https://www.econbiz.de/10011256800
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Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment
Koopman, Siem Jan; Ooms, Marius; Hindrayanto, Irma - Tinbergen Instituut - 2006
, pages 683-713.<P> This paper discusses identification, specification, estimation and forecasting for a general class of … formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues …
Persistent link: https://www.econbiz.de/10011256849
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Forecasting Interest Rates with Shifting Endpoints
Dijk, Dick van; Koopman, Siem Jan; Wel, Michel van der; … - Tinbergen Instituut - 2012
"><I>Journal of Applied Econometrics</I></A>, 2014, 29, pages 693-712.<P> Many economic studies on inflation forecasting have found … existing studies on interest rate forecasting either treat yields as being stationary, without any shifting endpoints, or treat … yields as a random walk process. In this study we consider the problem of forecasting the term structure of interest rates …
Persistent link: https://www.econbiz.de/10011257019
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The Stochastic Volatility in Mean Model
Koopman, Siem Jan; Uspensky, Eugenie Hol - Tinbergen Instituut - 2000
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011257033
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Likelihood-based Analysis for Dynamic Factor Models
Jungbacker, Borus; Koopman, Siem Jan - Tinbergen Instituut - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10011257194