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Time Series Models with a Common Stochastic Variance for Analysing Economic Time Series

Koopman, Siem Jan; Bos, Charles S. - 2002

concerned with themodelling and forecasting of two U.S. macroeconomic time series:inflation and industrial production. …

Persistent link: https://www.econbiz.de/10010324992

Unobserved Components with Stochastic Volatility in U.S. Inflation: Estimation and Signal Extraction

Li, Mengheng; Koopman, Siem Jan - 2018

applied to quarterly and monthly US inflation in an empirical study. We find that the persistence of quarterly inflation has … and density forecasts for monthly US inflation. …

Persistent link: https://www.econbiz.de/10011819542

An empirical assessment of the U.S. Phillips curve over time

Vlekke, Marente; Koopman, Siem Jan; Mellens, Martin C. - 2021

the Phillips curve over time. We consider different specifications and different measures for inflation. Furthermore, we ….S. headline inflation has remained empirically relevant over the years. …

Persistent link: https://www.econbiz.de/10012797257

Long Memory Modelling of Inflation with Stochastic Variance and Structural Breaks

Bos, Charles S.; Koopman, Siem Jan; Ooms, Marius - 2008

We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the … conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the … efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of …

Persistent link: https://www.econbiz.de/10014221102

The Stochastic Volatility in Mean Model

Koopman, Siem Jan; Uspensky, Eugenie Hol - 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/10010324578

Round-the-Clock Price Discovery for Cross-Listed Stocks: US-Dutch Evidence

Menkveld, Albert J.; Koopman, Siem Jan; Lucas, André - 2003

U.S. trading in non-U.S. stocks has grown dramatically. Round-the-clock, these stocks trade in the home market, in the U.S. marketand, potentially, in both markets simultaneously. We use a state space model to study 24-hour price discovery. As opposed to thestandard variance ratio'' approach,...

Persistent link: https://www.econbiz.de/10010324874

Stock Index Volatility Forecasting with High Frequency Data

Hol, Eugenie; Koopman, Siem Jan - 2002

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved ex-post volatility measurements but has also inspired research into their potential value as an informa-tion source for longer horizon volatility forecasts. In this paper...

Persistent link: https://www.econbiz.de/10010324972

Pro-Cyclicality, Empirical Credit Cycles, and Capital Buffer Formation

Koopman, Siem Jan; Lucas, André; Klaassen, Pieter - 2002

We model 1927-1997 U.S. business failure rates using a time series approach based on unobserved components. Clear evidence is found of cyclical behavior in default rates. The cycle has a period of around 10 years. We also detect longer term movements in default probabilities and default...

Persistent link: https://www.econbiz.de/10010325004

A Non-Gaussian Panel Time Series Model for Estimating and Decomposing Default Risk

Koopman, Siem Jan; Lucas, André; Daniels, Robert - 2005

We model 1981–2002 annual US default frequencies for a panel of firms in different rating and age classes. The data is decomposed into a systematic and firm-specific risk component, where the systematic component reflects the general economic conditions and default climate. We have to cope...

Persistent link: https://www.econbiz.de/10010325605

Macro, Industry and Frailty Effects in Defaults: The 2008 Credit Crisis in Perspective

Koopman, Siem Jan; Lucas, Andre; Schwaab, Bernd - 2010

We determine the magnitude and nature of systematic default risk using 1971{2009) default data from Moody's. We disentangle systematic risk factors due to business cycle effects, common default dynamics (frailty), and industry-specific dynamics (including contagion). To quantify the contribution...

Persistent link: https://www.econbiz.de/10010325719