Franc Klaassen
Biographic Data
| ID | 8920248 |
|---|---|
| NAME | Franc Klaassen |
| GIVEN NAMES | Franc |
| FAMILY NAME | Klaassen |
| SIGNATURE | KLAASSEN F |
| AFFILIATIONS | University of Amsterdam |
| ORCID | 0000-0002-9869-1924 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2005 |
| LATEST PUBLICATION YEAR | 2012 |
| H-INDEX | 0 |
Why Frequency Matters for Unit Root Testing in Financial Time Series
It is generally believed that the power of unit root tests is determined only by the time span of observations, not by their sampling frequency. We show that the sampling frequency does matter for stock data displaying fat tails and volatility clustering, such as financial time series. Our claim builds on recent work on unit root testing based on non-Gaussian GARCH-based likelihood functions. Such methods yield power gains in the presence of fat …
Long Swings in Exchange Rates
Several authors have reported evidence of long swings in U.S. dollar exchange rates by rejecting the random walk in favor of a Markov regime-switching model. We show that this evidence is not robust to an extension of the sample period. One should, however, not conclude that long swings are thus absent, because the tests may have insufficient power. A possible reason for this is the low data frequency, because existing tests are based on quarterl…
No prominent works on this page.
Long Swings in Exchange Rates
Several authors have reported evidence of long swings in U.S. dollar exchange rates by rejecting the random walk in favor of a Markov regime-switching model. We show that this evidence is not robust to an extension of the sample period. One should, however, not conclude that long swings are thus absent, because the tests may have insufficient power. A possible reason for this is the low data frequency, because existing tests are based on quarterl…
Why Frequency Matters for Unit Root Testing in Financial Time Series
It is generally believed that the power of unit root tests is determined only by the time span of observations, not by their sampling frequency. We show that the sampling frequency does matter for stock data displaying fat tails and volatility clustering, such as financial time series. Our claim builds on recent work on unit root testing based on non-Gaussian GARCH-based likelihood functions. Such methods yield power gains in the presence of fat …
Econometrics (2 works) · Economics (2 works) · Finance (2 works) · Financial Risk and Volatility Modeling (2 works) · Market Dynamics and Volatility (2 works) · Mathematics (2 works) · Statistics (2 works) · Autoregressive conditional heteroskedasticity (1 work) · Cluster analysis (1 work) · Complex Systems and Time Series Analysis (1 work)