Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

Zeitlberger, Alexander; Brauneis, Alexander

doi:10.1007/s10100-014-0340-0

Cited by 26 publications

(8 citation statements)

References 43 publications

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“…MS and MS-GARCH models have been used for several purposes, such as the characterization of stock indexes, currencies or commodities' time series [57][58][59][60][61][62][63][64][65][66][67][68][69][70]. The use of these models for the aforementioned has had also interesting extensions in Fuzzy logic and control applications [71,72].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Torre-Torres

Venegas-Martı́nez

Martínez-Torre-Enciso

2021

Mathematics

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In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Torre-Torres

Venegas-Martı́nez

Martínez-Torre-Enciso

2021

Mathematics

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“…What's more, due to the simple structure and easy implementation, the generalized autoregressive conditional heteroscedasticity (GARCH) model proposed by Bollerslev (1986) becomes the most popular volatility model. As a consequence, many authors apply the GARCH model to predict EUA futures volatility, see, e.g., Byun and Cho (2013), Zeitlberger and Brauneis (2016), Wang et al (2019), Naik et al (2020), Huang et al (2021). Later, some scholars expand GARCH model and construct TGARCH, GJR-GARCH, ARMAX-GARCH, STR-GARCH models to predict the volatility of carbon futures price (Arouri et al, 2012;Byun and Cho, 2013;Rannou and Barneto, 2016;Sheng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the volatility of European Union allowance futures with time-varying higher moments and time-varying risk aversion

Mei

Ding

2022

Front. Environ. Sci.

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There is increasing evidence that European Union allowance (EUA) futures return distributions exhibit features of time-varying higher moments (skewness and kurtosis), which plays an important role in modeling and forecasting EUA futures volatility. Moreover, a number of studies have shown that time-varying risk aversion (RA) contains useful information for forecasting EUA futures volatility. In light of this, this paper proposes the GARCH-MIDAS with skewness and kurtosis (hereafter GARCH-MIDAS-SK) to empirically investigate the impact and predictive role of RA on EUA futures volatility. Our empirical results show that RA has a significantly negative impact on the long-term volatility of EUA futures. The EUA futures return distributions exhibit obvious features of time-varying higher moments. Incorporating RA and time-varying higher moments improves the in-sample fitting of the model. Furthermore, out-of-sample results suggest that incorporating RA and time-varying higher moments leads to significantly more accurate volatility forecasts. This finding is robust to alternative out-of-sample forecasting windows.

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“…The first is the quantitative statistical model. They are autoregressive integral moving average (ARIMA) model [6], Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model [7], and ARIMA-GARCH model [8]. However, due to the high complexity and nonlinearity of carbon prices, the prediction results of statistical models are often not ideal.…”

Section: Introductionmentioning

confidence: 99%

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Zhou

Wang

2021

Energies

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Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

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Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models

Cited by 26 publications

References 43 publications

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Forecasting the volatility of European Union allowance futures with time-varying higher moments and time-varying risk aversion

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

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