This paper examines the effects of the unemployment rate, consumption of renewable energy, and oil rents on the annual greenhouse gas emission rate of G-20 countries from 1991 to 2020 by using second-generation panel data analysis. The primary goal is to examine the validation of Environmental...

Dans cet article, nous nous intéressons aux questions liées à l'estimation des quantités d'émissions azotées et des courbes de coût marginal d'épuration dans le bassin d'un cours d'eau. L'application et les calculs empiriques sont réalisés pour les secteurs d'activité et les régions...

This research uses annual time series data on CO2 emissions in India from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Our diagnostic tests indicate that India CO2 emission data is I (2). The study presents the ARIMA (2, 2, 0) model. The diagnostic tests...

Trend analysis and forecasting of time series data on air-pollutants is important to design effective measures to minimize damages to ecosystems and human health. In this study, autoregressive, moving average, autoregressive-moving average and autoregressive integrated moving average processes...

The asymmetric moving average model (asMA) is extended to allow forasymmetric quadratic conditional heteroskedasticity (asQGARCH). Theasymmetric parametrization of the conditional variance encompassesthe quadratic GARCH model of Sentana (1995). We introduce a framework fortesting asymmetries in...

A simple methodology is presented for modeling time variation in volatilities and other higher-order moments using a recursive updating scheme similar to the familiar RiskMetricsTM approach. We update parameters using the score of the forecasting distribution. This allows the parameter dynamics...

The present paper presents a theoretical extension of our earlier work entitled“A comparative study of two models SV with MCMC algorithm” cited, Rev Quant Finan Acc (2012) 38:479-493 DOI 10.1007/s11156-011-0236-1 where we propose initially a mixture stochastic volatility model providing a...

We present a simple new methodology to allow for time-variation in volatilities using a recursive updating scheme similar to the familiar RiskMetrics approach. It exploits the link between exponentially weighted moving average and integrated dynamics of score driven time varying parameter...

A new multivariate time series model with various attractive properties is motivated and studied. By extending the CCC model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility clustering, non-normality (excess kurtosis and asymmetry),...

We provide empirical evidence of volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently-developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and...