Processes with Autoregressive Conditional Heteroskedasticity (ARCH)

2016 ◽  
pp. 127-148
Author(s):  
Uwe Hassler
Keyword(s):  
Conditional Heteroskedasticity ◽  
Autoregressive Conditional Heteroskedasticity

scholarly journals Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

2021 ◽  
Vol 9 ◽  
Author(s):  
Yinpeng Zhang ◽  
Panpan Zhu ◽  
Yingying Xu
Keyword(s):  
Futures Markets ◽  
Vector Autoregression ◽  
Spot Market ◽  
Futures Market ◽  
Conditional Heteroskedasticity ◽  
Volatility Spillover ◽  
Empirical Results ◽  
Dynamic Correlation ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Gold Futures

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

Stepwise local influence in generalized autoregressive conditional heteroskedasticity models

2014 ◽  
Vol 42 (2) ◽  
pp. 428-444 ◽  
Author(s):  
Lei Shi ◽  
Md. Mostafizur Rahman ◽  
Wen Gan ◽  
Jianhua Zhao
Keyword(s):  
Local Influence ◽  
Conditional Heteroskedasticity ◽  
Autoregressive Conditional Heteroskedasticity

Quantitative Modeling of Science and Technology Finance Supporting Industrial Innovation Development Based on Internet of Things Technology

2021 ◽  
Vol 7 (5) ◽  
pp. 2055-2072
Author(s):  
Sai Tang ◽  
Zhihui Wang ◽  
Jiahao Zhou ◽  
Xin Zhang
Keyword(s):  
Internet Of Things ◽  
Financial Support ◽  
Science And Technology ◽  
High Tech ◽  
Conditional Heteroskedasticity ◽  
Finance Industry ◽  
Actual Effect ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Internet Of Things Technology ◽  
Innovation And Development

Objectives: In recent years, science and technology financial support industries are actively supporting the innovation and development of high-tech industries. In order to test the actual effect of S&T financial support industry support plan, a GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model is designed by using K-means (K-means clustering) algorithm and GM (1,1) (grey prediction) algorithm, which can quantitatively display the development of S&T financial industry to promote high-tech. The GARCH model is used to quantify the degree of innovation and development of science and technology finance industry in the Internet of Things (loT) technology. Finally, according to the quantitative data obtained by GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model, the actual effect of science and technology finance industry promoting innovation and development of high-tech is evaluated by FAHP (Fuzzy Analytic Hierarchy Process) model. The results show that science and technology finance industry plays a positive role in promoting the innovation and development of loT technology.

Impact of the COVID-19 Outbreak on the Currency Exchanges of Selected Countries

2022 ◽  
Vol 14 (2) ◽  
pp. 73-91
Author(s):  
Sudhi Sharma ◽  
Miklesh Prasad Yadav ◽  
Babita Jha
Keyword(s):  
Growth Rate ◽  
Negative Impact ◽  
Annual Growth ◽  
Conditional Heteroskedasticity ◽  
Annual Growth Rate ◽  
Dummy Variable ◽  
Currency Market ◽  
Autoregressive Conditional Heteroskedasticity ◽  
The Impact

The paper aims to analyse the impact of the COVID outbreak on the currency market. The study considers spot rates of seven major currencies (i.e., EUR/USD, USD/JPY, GBP/USD, AUD/USD, USD/CAD, USD/CHF, and CHF/JPY). To capture the impact of the outbreak on returns and the volatility of returns of seven currencies during pandemic, the study has segregated in two window periods (i.e., pre- [1st Jan 2019 to 31st Dec, 2019] and post-outbreak of COVID-19 [1st Jan, 2020 to 22nd Dec, 2020]). The study has applied various methods and models (i.e., econometric-based compounded annual growth rate [CAGR], dummy variable regression, and generalized autoregressive conditional heteroskedasticity [GARCH]). The result of the study captures the negative impact of the COVID-19 pandemic on three currencies—USD/JPY, AUD/USD, and USD/CHF—and positive significant impact on EUR/USD, GBP/USD, USD/CAD, and CHF/JPY. Investors can take short position in these while having long position in other currencies. The inferences drawn from the analysis are providing insight to investors and hedgers.

scholarly journals Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

2005 ◽  
Vol 12 (1) ◽  
pp. 55-66 ◽  
Author(s):  
W. Wang ◽  
P. H. A. J. M Van Gelder ◽  
J. K. Vrijling ◽  
J. Ma
Keyword(s):  
Seasonal Variation ◽  
Moving Average ◽  
Arma Model ◽  
Autoregressive Moving Average ◽  
Conditional Heteroskedasticity ◽  
Daily Streamflow ◽  
Arch Effect ◽  
Monthly Streamflow ◽  
Autoregressive Conditional Heteroskedasticity ◽  
Residual Series

Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.

Sign in / Sign up

Export Citation Format

Share Document