The existing parametric multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model could hardly capture the nonlinearity and the non-normality, which are widely observed in …nancial data. We propose semiparametric conditional covariance (SCC) model to capture the information hidden in the standardized residuals and missed by the parametric MGARCH models. Our two-stage...

This study investigates the effect of climate change on Asian stock markets using Generalized Autoregressive Conditional Heteroskedasticity variant Mixed Data Sampling (GARCH-MIDAS) model. The results reveal that long-term return volatility about 40% are unaffected by change. implies investments in insensitive to financing. More awareness investment climate-oriented stocks is recommended.

A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to i...

This paper introduces a new multivariate conditional volatility model for returns that utilizes realized covariance matrices. The model decomposes the conditional and realized covariance matrices into standard deviations and correlations matrices. On a first level, the univariate variances are estimated by a modified Generalized Autoregressive Conditional Heteroskedasticity (GARCH) that exploit...

Within the last three decades commodity markets, including soft commodities have become more and like financial markets. As a result, prices of may exhibit similar patterns or anomalies as those observed in behaviour different assets. Their existence cast doubts on competitiveness efficiency It motivates us to conduct research presented this paper, aimed at examining Halloween effect markets ba...

The empirical evidence suggests that stock returns in the emerging technology environment exhibit high return volatility. fundamental aim of article is to investigate dynamic, time series properties correlations between daily log and magnitude volatility transmissions from technologies Spanish banking sector, market portfolio finance industry EU area. Using for performance variables an equally ...

This article examines whether incorporating investors’ uncertainty, as captured by the conditional volatility of sentiment, can help forecasting stock markets. In this regard, using Markov-switching multifractal (MSM) model, we find that uncertainty substantially increase accuracy forecasts market according to forecast encompassing test. We further provide evidence MSM outperforms dynamic corre...

This paper investigates the trend in the monthly real price of oil between 1990 and 2008 with a generalized autoregressive conditional heteroskedasticity (GARCH) model. Trend and volatility are estimated jointly with the maximum likelihood estimation. There is long persistence in the variance of oil price shocks, and a GARCH unit root (GUR) test can potentially yield a significant power gain re...

We apply the ACD-ICV method proposed by Tse and Yang (2011) for the estimation of intraday volatility to estimate monthly volatility, and empirically compare this method against the realized volatility (RV) and generalized autoregressive conditional heteroskedasticity (GARCH) methods. Our Monte Carlo results show that the ACD-ICV method performs well against the other two methods. Evidence on t...