Across the globe, COVID-19 has disrupted financial markets, making them more volatile. Thus, this paper examines market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, Sugar during pandemic. We applied GARCH (1, 1), GJR-GARCH EGARCH 1) econometric models on daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical finding...

Based on the data of meteorological, soil, and land use/land cover (LULC), InVEST model was used to evaluate water yield service in middle upper Ganjiang River region, which is an important ecological function area hilly region South China. This study discusses value parameter Z, impact climate LULC change service, complements related studies monsoon watershed China with obvious precipitation c...

This paper investigates the price returns and volatility linkages between the foreign exchange (KRW) and stock (KOSPI) markets in Korea, using the cointegration test, and bivariate GJR-GARCH model. Our findings from empirical analysis are summarized as follows. First, there is no long-term equilibrium relationship between the KRW and KOSPI markets. Second, exogenous variables (yen/dollar exchan...

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, an...

This paper features an analysis of the relationship between the volatility of the Dow Jones Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The expansion of on-line financial news sources, such as internet news and social media sou...

a r t i c l e i n f o JEL classification: C53 G17 Keywords: GARCH Higher conditional moments Approximate predictive distributions Value-at-Risk S&P 500 Treasury bill rate Euro–US dollar exchange rate It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency...

We use a time-varying copula model to investigate the impact of the introduction of the Euro on the dependence between seventeen European stock markets during the period 1994-2003. The model is implemented with a GJR-GARCH-t model for marginal distributions and the Gaussian copula for the joint distribution, which allows capturing time-varying, non-linear relationships. The results show that wi...

An understanding of volatility in stock markets is important for determining the cost of capital and for assessing investment and leverage decisions as volatility is synonymous with risk. Substantial changes in volatility of financial markets are capable of having significant negative effects on risk averse investors. Using daily returns from 1992 to 2002, we investigate volatility co-movement ...

This paper aims at analyzing the financial risk and co-movement of stock markets in three countries: Indonesia, Philippine and Thailand. It consists of analyzing the conditional volatility and test the leverage effect in the stock markets of the three countries. To capture the pairwise and conditional dependence between the variables, we use the method of vine copulas. In addition, we illustrat...