Michel van der Wel
Biographic Data
| ID | 5733156 |
|---|---|
| NAME | Michel van der Wel |
| GIVEN NAMES | Michel |
| FAMILY NAME | van der Wel |
| SIGNATURE | VAN DER WEL M |
| AFFILIATIONS | Erasmus School of Economics, Erasmus University Rotterdam; Erasmus Research Institute for Management (ERIM); Tinbergen Institute; Center for Research in Econometric Analysis of Time Series (CREATES) |
| ORCID | 0000-0001-5133-7680 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A Smooth Shadow-Rate Dynamic Nelson-Siegel Model for Yields at the Zero Lower Bound
We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period. By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression. This permits the implementation of readily available DNS extensions such as allowing for time-varying parameters and the integration of macroeconomic…
Market Set‐up in Advance of Federal Reserve Policy Rate Decisions
This article considers the extent to which the federal (fed) funds futures market prepares for Federal Open Market Committee (FOMC) announcements. We demonstrate that there is often less variation in fed funds futures prices during the period immediately preceding an FOMC announcement than in earlier periods, despite greater trading activity, as the market has already incorporated anticipated signals. We find that macro announcements and central …
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter tha…
No prominent works on this page.
Analyzing the Term Structure of Interest Rates Using the Dynamic Nelson–Siegel Model With Time-Varying Parameters
In this article we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector autoregressive factors. We extend this framework in two directions. First, the factor loadings in the Nelson–Siegel yield model depend on a single loading parameter tha…
Market Set‐up in Advance of Federal Reserve Policy Rate Decisions
This article considers the extent to which the federal (fed) funds futures market prepares for Federal Open Market Committee (FOMC) announcements. We demonstrate that there is often less variation in fed funds futures prices during the period immediately preceding an FOMC announcement than in earlier periods, despite greater trading activity, as the market has already incorporated anticipated signals. We find that macro announcements and central …
A Smooth Shadow-Rate Dynamic Nelson-Siegel Model for Yields at the Zero Lower Bound
We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period. By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression. This permits the implementation of readily available DNS extensions such as allowing for time-varying parameters and the integration of macroeconomic…
Economics (3 works) · Econometrics (2 works) · Mathematical Dynamics and Fractals (2 works) · Mathematics (2 works) · Monetary policy (2 works) · Stochastic processes and financial applications (2 works) · Applied Mathematics (1 work) · Autoregressive conditional heteroskedasticity (1 work) · Autoregressive model (1 work) · Blackout (1 work)