Details
| Original language | English |
|---|---|
| Pages (from-to) | 72-84 |
| Number of pages | 13 |
| Journal | Journal of Empirical Finance |
| Volume | 31 |
| Publication status | Published - 1 Mar 2015 |
Abstract
In this paper, we study jumps in commodity prices. Unlike assumed in existing models of commodity price dynamics, a simple analysis of the data reveals that the probability of tail events is not constant but depends on the time of the year, i.e. exhibits seasonality. We propose a stochastic volatility jump-diffusion model to capture this seasonal variation. Applying the Markov Chain Monte Carlo (MCMC) methodology, we estimate our model using 20. years of futures data from four different commodity markets. We find strong statistical evidence to suggest that our model with seasonal jump intensity outperforms models featuring a constant jump intensity. To demonstrate the practical relevance of our findings, we show that our model typically improves Value-at-Risk (VaR) forecasts.
Keywords
- Commodities, Jump frequency, Markov Chain Monte Carlo, Seasonality
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)
- Finance
- Economics, Econometrics and Finance(all)
- Economics and Econometrics
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In: Journal of Empirical Finance, Vol. 31, 01.03.2015, p. 72-84.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Time-variations in commodity price jumps
AU - Diewald, Laszlo
AU - Prokopczuk, Marcel
AU - Wese Simen, Chardin
PY - 2015/3/1
Y1 - 2015/3/1
N2 - In this paper, we study jumps in commodity prices. Unlike assumed in existing models of commodity price dynamics, a simple analysis of the data reveals that the probability of tail events is not constant but depends on the time of the year, i.e. exhibits seasonality. We propose a stochastic volatility jump-diffusion model to capture this seasonal variation. Applying the Markov Chain Monte Carlo (MCMC) methodology, we estimate our model using 20. years of futures data from four different commodity markets. We find strong statistical evidence to suggest that our model with seasonal jump intensity outperforms models featuring a constant jump intensity. To demonstrate the practical relevance of our findings, we show that our model typically improves Value-at-Risk (VaR) forecasts.
AB - In this paper, we study jumps in commodity prices. Unlike assumed in existing models of commodity price dynamics, a simple analysis of the data reveals that the probability of tail events is not constant but depends on the time of the year, i.e. exhibits seasonality. We propose a stochastic volatility jump-diffusion model to capture this seasonal variation. Applying the Markov Chain Monte Carlo (MCMC) methodology, we estimate our model using 20. years of futures data from four different commodity markets. We find strong statistical evidence to suggest that our model with seasonal jump intensity outperforms models featuring a constant jump intensity. To demonstrate the practical relevance of our findings, we show that our model typically improves Value-at-Risk (VaR) forecasts.
KW - Commodities
KW - Jump frequency
KW - Markov Chain Monte Carlo
KW - Seasonality
UR - http://www.scopus.com/inward/record.url?scp=84939936657&partnerID=8YFLogxK
U2 - 10.1016/j.jempfin.2015.02.004
DO - 10.1016/j.jempfin.2015.02.004
M3 - Article
AN - SCOPUS:84939936657
VL - 31
SP - 72
EP - 84
JO - Journal of Empirical Finance
JF - Journal of Empirical Finance
SN - 0927-5398
ER -