Risk management of commodity prices is an important yet challenging task. Given the complex behaviour of commodity prices, this creates the need of using sophisticated models of commodity prices dynamics. Obviously, parameter estimation of such models poses another challenge. Previous literature has addressed this problem using Markov Chain Monte Carlo, which is computationally expensive for parameter estimation and inference. In this paper we develop an efficient Maximum Likelihood Estimation procedure based on the characteristic function. We then estimate parameters a stochastic volatility model with stochastic drift utilizing the time-series of rice and coffee prices. We show that such model produces realistic distributions of both commodity prices. Finally, using the estimated model parameters we calculate various risk measures such as Value at Risk or Expected Shortfall.
We study idiosyncratic volatility in the Vietnamese stock market between July 2007 and February 2015. We show that there is no relationship between the idiosyncratic volatility and average returns in the Vietnamese market. Our results also indicate that neither the aggregate market volatility, the aggregate idiosyncratic volatility nor can predict market returns. Finally, we find no trend in idiosyncratic volatility and a decreasing trend of market volatility over the sample period. In addition, we find strong evidence of both short as well as long term reversal in Vietnam stock market during the sample period.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.