China
Africa
Explore chapters and articles related to this topic
Value At Risk: Recent Advances
Published in George Anastassiou, Handbook of Analytic-Computational Methods in Applied Mathematics, 2019
Irina N. Khindanova, Svetlozar T. Rachev
Popular models explaining time-varying volatility are autoregressive conditional heteroscedasticity (ARCH) models, introduced by Engle [22]. In the ARCH models the conditional variances follow autoregressive processes. The ARCH(q) model assumes the returns on the i-th asset Ri,1, Ri,2,… are explained by the process: Ri,t=μi+σi,tui,t,σi,t2=αi+∑j=1qβij(Ri,t−j−μi)2,
Modeling structural breakpoints in volatility of Philippine Peso-US Dollar currency exchange rate
Published in Yuli Rahmawati, Peter Charles Taylor, Empowering Science and Mathematics for Global Competitiveness, 2019
The Generalized Autoregressive Conditional Heteroskedasticity or GARCH, is a forecasting method proposed by (Bollerslev, 1986, Bollerslev et al, 1992) that generalizes the ARCH model of Engle in 1982. The GARCH (p, q) models the return yt () yt=C+ϵt,
Design of an automated tea grader
Published in Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla, Chemometric Monitoring: Product Quality Assessment, Process Fault Detection, and Applications, 2017
Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla
Time series data are statistical data collected and recorded over successive increments of time. Time series such as electrical signals that are continuous in time are treated herein as discrete time series because only digitized values in discrete time intervals are used for the computation. GARCH (generalized autoregressive conditional heteroskedasticity) is a time series modeling technique that uses past variances, and past variance forecasts, to forecast future variances. Whenever a time series is said to have GARCH effects, the series is heteroskedastic; that is, its variances vary with time. Using the GARCH toolbox, the auto-correlation function (ACF) and partial autocorrelation function (PACF) are derived for time series data of tea. Figure 5.6 presents the ACFs of representative tea samples for each electrode system, namely, silver (Ag), platinum (Pt), gold (Au), and glassy carbon along with the upper and lower standard deviation confidence bounds. Figure 5.7 presents the PACFs of representative tea samples for each electrode system considered along with the upper and lower standard deviation confidence bounds. From the above correlograms, the presence of correlation among the successive observations in the time series of tea samples (collected over the Ag, Pt, and Au electrodes) is prevalent. The observations indicate that a significant amount of nonstationarity is present in the time series corresponding to various tea brands. In this regard, the glassy-carbon electrode manifested different behavior; the time series data collected using it did not show any significant correlation. The Ljung-Box Q-statistic and Engle's ARCH (autoregressive conditional heteroskedastic effects) tests (lack-of-fit hypothesis tests) are performed on mean-centered time series data. The presence of an ARCH effect and significant Q statistics in the data of various tea brands indicates the presence of nonstationarity in the data. The nonstationary behavior may be due to the change of covariance structure as well as the time-varying variance.
Artificial Intelligence Methods: Toward a New Decision Making Tool
Published in Applied Artificial Intelligence, 2022
Ismail Lotfi, Abdelhamid El Bouhadi
Generally, GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models are used to model asset volatility as well as the asymmetry of the distribution (leptokurtic and platikurtic) of returns. It is with this objective in mind that studies of different assets work with returns and not with prices, for two essential points: Any modeling of the price history with the aim of projecting itself into the future will be incorrect, and this is because certain properties of price processes do not remain constant over time. Reference is made to non-stationarity, and thus one prefers to use yield processes that have stationarity as a characteristic.The notion of correlation stipulates the existence of a temporal dependence between an observation at the present moment and its past history.
Forecasting gasoline consumption using machine learning algorithms during COVID-19 pandemic
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2022
Zeynep Ceylan, Derya Akbulut, Engin Baytürk
All of the studies listed in Table 1 are on the estimation of fuel demand without considering the effects of unexpected natural disasters and/or hazards. However, the current epidemic crisis has prompted researchers to analyze energy demand and /or consumption considering the impact of the COVID-19 outbreak. Table 2 shows the details of the literature review on the analysis of gasoline and diesel demand under the effects of COVID-19. For example, Ou et al. (2020) proposed a neural network-based model that considers personal mobility to project the impact of COVID-19 on gasoline demand. Under different pandemic and policy scenarios, they estimated the daily motor gasoline demand in the USA from March 2020 to October 2020. Güngör, Ertuğrul, and Soytaş (2021) examined the effects of the COVID-19 epidemic on gasoline consumption in Turkey with a stochastic time series model. Besides, the autoregressive conditional heteroskedasticity (ARCH) models were used to analyze volatility in time series to forecast future volatility.
Double ensemble system for wind energy forecasting based on generalized autoregressive conditional heteroskedasticity and neural network models with variational mode decomposition
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Angel Colmenares, Jianzhou Wang
Autoregressive conditional heteroskedasticity (ARCH) (Engle 1982) is a statistical method for time-series data that defines the variance of the present error or innovation as a function of the current magnitude of the preceding periods’ error terms; typically, the variance is associated with the squares of the preceding innovations. The ARCH model is suitable when the error variance in a time series can be described as an autoregressive (AR) model. When an ARMA model is presumed for the error variance, the model is a GARCH model (Bollerslev 1986). The autoregressive conditional heteroskedasticity has the following form: