Cătălin Stărică
Biographic Data
| ID | 8880045 |
|---|---|
| NAME | Cătălin Stărică |
| GIVEN NAMES | Cătălin |
| FAMILY NAME | Stărică |
| SIGNATURE | STĂRICĂ C |
| AFFILIATIONS | Chalmers University of Technology |
| ORCID | 0000-0003-3201-5453 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2004 |
| LATEST PUBLICATION YEAR | 2005 |
| H-INDEX | 0 |
Nonstationarities in Stock Returns
The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seve…
Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects
We give the theoretical basis of a possible explanation for two stylized facts observed in long log-return series: the long-range dependence (LRD) in volatility and the integrated GARCH (IGARCH). Both these effects can be explained theoretically if one assumes that the data are nonstationary
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Nonstationarities in Financial Time Series, the Long-Range Dependence, and the IGARCH Effects
We give the theoretical basis of a possible explanation for two stylized facts observed in long log-return series: the long-range dependence (LRD) in volatility and the integrated GARCH (IGARCH). Both these effects can be explained theoretically if one assumes that the data are nonstationary
Nonstationarities in Stock Returns
The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seve…
Autoregressive conditional heteroskedasticity (2 works) · Complex Systems and Time Series Analysis (2 works) · Econometrics (2 works) · Economics (2 works) · Financial Risk and Volatility Modeling (2 works) · Market Dynamics and Volatility (2 works) · Mathematics (2 works) · Series (stratigraphy) (2 works) · Volatility (finance) (2 works) · Finance (1 work)