- Abstract:
-
We propose a methodology to employ high frequency financial data to obtain estimates of volatility of log-prices which are not affected by microstructure noise and Lévy jumps. We introduce the “number of jumps” as a variable to explain and predict volatility and show that the number of jumps in SPY prices is an important variable to explain the daily volatility of the SPY log-returns, has more explanatory power than other variables (e.g., high and low, open and close), and has a similar expla...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 741.4KB)
-
- Publisher copy:
- 10.1080/07474938.2014.976529
-
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- Publisher:
- Taylor & Francis Group Publisher's website
- Journal:
- Econometric Reviews Journal website
- Volume:
- 35
- Issue:
- 6
- Pages:
- 929-950
- Publication date:
- 2014-10-20
- DOI:
- ISSN:
-
0747-4938 and 1532-4168
- Pubs id:
-
pubs:624669
- URN:
-
uri:881242b6-3609-4f4a-8c81-cc4e6fb7ddac
- UUID:
-
uuid:881242b6-3609-4f4a-8c81-cc4e6fb7ddac
- Local pid:
- pubs:624669
- Paper number:
- 6
- Keywords:
- Copyright holder:
- Taylor & Francis Group
- Copyright date:
- 2014
- Notes:
-
This is an
accepted manuscript of a journal article published by Taylor & Francis Group in Econometric Reviews on 2014-10-20, available online: http://dx.doi.org/10.1080/07474938.2014.976529
Journal article
The relationship between the volatility of returns and the number of jumps in financial markets
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