Time series properties of ARCH processes with persistent covariates

We consider ARCH processes with persistent covariates and provide asymptotic theories that explain how such covariates affect various characteristics of volatility. Specifically, we propose and study a volatility model, named ARCH-NNH model, that is an ARCH(1) process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. Statistical properties of time series given by this model are investigated for various volatility functions. It is shown that our model generates time series that have two prominent characteristics: high degree of volatility persistence and leptokurtosis. Due to persistent covariates, the time series generated by our model has the long memory property in volatility that is commonly observed in high frequency speculative returns. On the other hand, the sample kurtosis of the time series generated by our model either diverges or has a well-defined limiting distribution with support truncated on the left by the kurtosis of the innovation, which successfully explains the empirical finding of leptokurtosis in financial time series. We present two empirical applications of our model. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than GARCH(1,1) and FIGARCH at relatively lower frequencies.ARCH; nonstationarity; nonlinearity; NNH; volatility persistence; leptokurtosis

Time series properties of ARCH processes with persistent covariates

We consider ARCH processes with persistent covariates and provide asymptotic theories that explain how such covariates affect various characteristics of volatility. Specifically, we propose and study a volatility model, named ARCH-NNH model, that is an ARCH(1) process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. Statistical properties of time series given by this model are investigated for various volatility functions. It is shown that our model generates time series that have two prominent characteristics: high degree of volatility persistence and leptokurtosis. Due to persistent covariates, the time series generated by our model has the long memory property in volatility that is commonly observed in high frequency speculative returns. On the other hand, the sample kurtosis of the time series generated by our model either diverges or has a well-defined limiting distribution with support truncated on the left by the kurtosis of the innovation, which successfully explains the empirical finding of leptokurtosis in financial time series. We present two empirical applications of our model. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than GARCH(1,1) and FIGARCH at relatively lower frequencies

Time series properties of ARCH processes with persistent covariates

We consider ARCH processes with persistent covariates and provide asymptotic theories that explain how such covariates affect various characteristics of volatility. Specifically, we propose and study a volatility model, named ARCH-NNH model, that is an ARCH(1) process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. Statistical properties of time series given by this model are investigated for various volatility functions. It is shown that our model generates time series that have two prominent characteristics: high degree of volatility persistence and leptokurtosis. Due to persistent covariates, the time series generated by our model has the long memory property in volatility that is commonly observed in high frequency speculative returns. On the other hand, the sample kurtosis of the time series generated by our model either diverges or has a well-defined limiting distribution with support truncated on the left by the kurtosis of the innovation, which successfully explains the empirical finding of leptokurtosis in financial time series. We present two empirical applications of our model. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than GARCH(1,1) and FIGARCH at relatively lower frequencies

Econometric analysis of ARCH models with persistent covariates

We consider a volatility model, named ARCH-NNH model, that is specifically an ARCH process with a nonlinear function of a persistent, integrated or nearly integrated, explanatory variable. We first establish the asymptotic theories showing that the time series properties of our model successfully describe stylized facts about volatility in financial time series. Due to persistent covariates, the model generates time series showing the long memory property in volatility and leptokurtosis which are commonly observed in speculative return series. Next, we derive the asymptotic distribution theory of the maximum likelihood estimator in our model. In particular, we establish the consistency and asymptotic mixed normality of the maximum likelihood estimator in the model, which ensure the standard inference procedure is valid in our model. Additionally, we conduct misspecification analysis and provide an explanation of the commonly observed IGARCH behavior of financial time series. Our theory shows that the IGARCH behavior could be spurious and could be the result of ignoring persistent covariates in ARCH type models. Finally, we present two empirical applications and a forecast evaluation of our model. Both empirical applications show that our model fits the data very well, and the estimation results confirm the findings of other literature. It is shown that the default premium (the yield spread between Baa and Aaa corporate bonds) predicts stock return volatility, and the interest rate differential between two countries accounts for exchange rate return volatility. The forecast evaluation shows that our model generally performs better than other standard volatility models at relatively lower frequencies

The reduction and control technology of tar during biomass gasification/pyrolysis: An overview

Biomass is an important primary energy source as well as renewable energy source. As the most promising biomass utilization method, gasification/pyrolysis produces not only useful fuel gases, char and chemicals, but also some byproducts like fly ash, NOx, SO2 and tar. Tar in the product gases will condense at low temperature, and lead to clogged or blockage in fuel lines, filters and engines. Moreover, too much tar in product gases will reduce the utilization efficiency of biomass. Therefore, the reduction or decomposition of tar in biomass derived fuel gases is one of the biggest obstacles in its utilization for power generation. In this paper, we review the literatures pertaining to tar reduction or destruction methods during biomass gasification/pyrolysis. On the basis of their characteristics, the current tar reduction or destruction methods can be broadly divided into five main groups: mechanism methods, self-modification, thermal cracking, catalyst cracking and plasma methods.Biomass Gasification Tar Reduction or destruction