Credit Risk Volatility: Evidences From the Green Bond Market

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

Investigating financial opportunities for traditional clothing industry in South Asia based on an analysis of internationally diversified portfolio using ARCH and GARCH models

This paper investigates the benefits of forming an internationally diversified portfolio in the stock markets of Bangladesh, India and Pakistan using the stock market indices data from April 2013 to March 2020. The portfolio comprises of three stock market indices from Pakistan, India and Bangladesh. The goal is to identify financial opportunities for traditional clothing industry in South Asia. Bangladesh, India and Pakistan are neighbouring countries in South Asia. Tradition, culture and specific ethnic elements influence traditional clothing in the case of the selected country cluster consisting of Bangladesh, India and Pakistan. Our empirical results indicate that internationally diversified portfolio does not reduce the risk due to global market integration in the background. Furthermore, ARCH and GARCH models reveal that large change in conditional variance is followed by large changes in conditional variance whereas small change in conditional variance is followed by small changes in conditional variance.

Tracking COVID-19 Decease Through Rolling Conditional Variance

The effects of COVID-19 have been devastating globally. However, countries have essential asymmetries regarding the disease spread dynamics and the respective mortality rates. In addition to containment strategies and boosting growth and economic development in the face of the COVID-19 pandemic, society calls for solutions that allow the development of vaccines, treatments for the disease, and especially, indicators or early warnings that anticipate the evolution of new infections and deaths. This research aims to track the total deaths caused by COVID-19 in the most affected countries by the pandemics after the approval, distribution, and implementation of vaccines from 2021. We proposed an Autoregressive Integrated Moving Average (ARIMA) specification as a first adjustment. Subsequently, we estimate the conditional variance of total deaths from an Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). Finally, we compute a rolling density backtesting within a 7-day rolling window to demonstrate the robustness estimation for COVID-19 mortality. The work's main contribution lies in exhibiting a tracking indicator for volatility and COVID-19 direction, including a weekly window to observe its evolution.

Price Risk Measurement of China’s Soybean Futures Market Based on the VAR-GJR-GARCH Model

As one of the main forces in the futures market, agricultural product futures occupy an important position in China’s market. As China’s futures market started late and its maturity was low, there are many risks. This study focuses on the Dalian soybean futures market. Dynamic risk measurement models were established to empirically analyze risk measurement problems under different confidence levels. Then, the conditional variance calculated by the volatility model was introduced into the value-at-risk model, and the accuracy of the risk measurement was tested using the failure rate test model. The empirical results show that the risk values calculated by the established models at the 99% and 95% confidence levels are more valuable through the failure rate test, and the risk of China’s soybean futures market can be measured more accurately. The characteristics of “peak thick tail” and “leverage effect” are added to the combination model to calculate the conditional variance more accurately. The failure rate test method is used to test the model, which enriches the research problem of risk measurement.

Mean–variance relationship and uncertainty

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

An Empirical Analysis of the Price Volatility Characteristics of China’s Soybean Futures Market Based on ARIMA-GJR-GARCH Model

As the main force in the futures market, agricultural product futures occupy an important position in the China’s market. Taking the representative soybean futures in Dalian Commodity Futures Market of China as the research object, the relationship between price fluctuation characteristics and trading volume and open position was studied. The empirical results show that the price volatility of China’s soybean futures market has a “leverage effect.” The trading volume and open interest are divided into expected parts and unexpected parts, which are added to the conditional variance equation. The expected trading volume coefficient is estimated. Also, the estimated value of the expected open interest coefficient is, respectively, smaller than the estimated value of the unexpected trading volume coefficient and the estimated value of the unexpected open interest coefficient. Therefore, the impact of expected trading volume on the price fluctuation of China’s soybean futures market is less than that of unexpected trading volume on the price of soybean futures market. This paper adds transaction volume as an information flow to the variance of the conditional equation innovatively and also observes transaction volume as the relationship between conditional variance and price fluctuations.

An Empirical Analysis on the Volatility of Return of CSI 300 Index

In order to better observe the trend of the stock market, this paper selects the daily closing price data of CSI 300 index from April 12, 2016 to September 30, 2021, and makes an empirical analysis on the logarithmic return of CSI 300 index. It is found that: (1) the return series of the CSI 300 index shows the statistical characteristics of peak, thick tail, bias, asymmetry and persistence. The ARMA (2,3) model can effectively fit the yield series and predict the future trend to a certain extent. (2) The residuals of ARMA model show obvious cluster effect and ARCH effect (conditional heteroscedasticity). GARCH (1,1) model can better fit the conditional heteroscedasticity, so as to eliminate the ARCH effect. (3) By constructing GARCH (1,1) model, it is found that the sum of ARCH term coefficient and GARCH term coefficient is very close to 1, indicating that GARCH process is wide and stable, the impact on conditional variance is lasting, and the market risk is large, that is, the impact plays an important role in all future forecasts.

Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan

This study adopted the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to examine the 10 ozone precursors of the highest concentrations among the 54 that were assessed over a number of years at the four photochemical assessment monitoring stations (PAMSs) in the Kaohsiung–Pingtung Area in Taiwan. First, the 10 ozone precursors, which were all volatile organic compounds (VOCs), were analyzed using the factor analyses in multiple statistical analyses that had the most significant impact on the area’s ozone formation: mobile pollution factor, which included 1,2,4-Trimethylbenzene (C9H12), toluene (C7H8), and Isopropyl benzene (C9H12). Then, taking into consideration that the number sequences might be affected by standardized residuals, this study applied the vector autoregressive moving average-EGARCH (VARMA-EGARCH) model to analyze the correlation between the three VOCs under different polluting activities. The VARMA-EGARCH model in this research included dummy variables representing changing points of variance structures in the variance formula to predict the conditional variance. This process proved able to effectively estimate the relevant coefficients of the three VOCs’ dynamic conditions that changed with time. The model also helped to prevent errors from occurring when estimating the conditional variance. Based on the testing results, this study determined the VARMA(2,1)-EGARCH(1,0) as the most suitable model for exploring the correlation between the three VOCs and meteorological phenomena, as well as the interplay between them in regard to interaction and formation. With the most representative of the three, toluene (TU), as the dependent variable and 1,2,4-Trimethylbenzene (TB) and Isopropyl benzene (IB) as the independent variables, this study found it impossible to calculate the TU concentration with TB and IB concentrations in the same period; estimations of TB and IB concentrations with a period of lag time were required because TU was mainly contributed by automobiles and motorcycles in Kaohsiung. TB and IB resulted from other stationary pollution sources in the region besides cars and motorbikes. When TU was evenly distributed and stayed longer in the atmosphere, the TB and IB concentrations were lower, so distribution conditions and concentrations could not be used to effectively estimate the concentration of toluene. This study had to wait until the next period, or when stationary pollution sources started producing TB and IB of higher concentrations during the daytime, in order to estimate the TU concentrations in a better photochemical situation.