Forecasting conditional volatility on the RIN market using MS GARCH model

Kakorina, Ekaterina (2014): Forecasting conditional volatility on the RIN market using MS GARCH model.

Abstract

In the recent years the topic about pollution of environment is quite popular. Many countries organize the government policy taking into account environmentally friendly policy. Maybe because of big share of the world pollution the USA organized not only the emission market, but also the RIN market where RIN is a 38-digit serial number, tax, security and investment. All actors of the RIN market can be divided into six groups: farmers, refiners, blenders, owners of fuel stations, EPA and private agencies. Models which can forecast are ARMA, ARMA-GARCH, GARCH-M and MS ARMA-GARCH. We identify that non-path-dependent MS AR(1)-GARCH-M(1,1) model cannot forecast better than AR(1)-t-GARCH(1,1) model, because it cannot forecast zero returns. Additionally, according to White’s test we identify than standard normal distribution is better then Student-t. At the same time our forecast of volatility using MS GARCH with standard normal distribution does not work the right way. In other words, forecasted volatility and returns are not fluctuated and also forecasted returns differ significantly from the real returns, especially, after the fourth period. Futhermore, we compare our price forecast with data which are presented by EPA (bid and ask prices). Using White test again, we find that the statistic is less in our case. In addition, the price does not change the way it should do, in other words, maybe we do not include a significant factor in our analysis.

• Item Type: MPRA Paper
• Language: English
• Keywords: RIN market, Renewable Identification Number, ecology, security, volatility forecast, price forecast, MS GARCH
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q2 - Renewable Resources and Conservation > Q28 - Government Policy

References

Blazsek S., Downarowicz A., Forecasting hedge fund volatility: a Markov regime-switching approach // The European Journal of Finance (2013), vol. 19, no. 4, pp. 243-275.

1.26. Marcucci J., Forecasting stock market volatility with regime-switching GARCH models // Studies in Nonlinear Dynamics & Econometrics, 2005, vol. 9, issue 4, pp. 1-55.