Details
| Original language | English |
|---|---|
| Pages (from-to) | 758-792 |
| Number of pages | 35 |
| Journal | Journal of Futures Markets |
| Volume | 36 |
| Issue number | 8 |
| Publication status | Published - 1 Aug 2016 |
| Externally published | Yes |
Abstract
This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in-sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions.
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Accounting
- Business, Management and Accounting(all)
- General Business,Management and Accounting
- Economics, Econometrics and Finance(all)
- Finance
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Journal of Futures Markets, Vol. 36, No. 8, 01.08.2016, p. 758-792.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets
AU - Prokopczuk, Marcel
AU - Symeonidis, Lazaros
AU - Wese Simen, Chardin
PY - 2016/8/1
Y1 - 2016/8/1
N2 - This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in-sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions.
AB - This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in-sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions.
UR - http://www.scopus.com/inward/record.url?scp=84951111480&partnerID=8YFLogxK
U2 - 10.1002/fut.21759
DO - 10.1002/fut.21759
M3 - Article
AN - SCOPUS:84951111480
VL - 36
SP - 758
EP - 792
JO - Journal of Futures Markets
JF - Journal of Futures Markets
SN - 0270-7314
IS - 8
ER -