Using EGARCH models to predict volatility in unconsolidated financial markets: the case of European carbon allowances.

The growing interest and direct impact of carbon trading in the economy have drawn an increasing attention to the evolution of the price of CO2 allowances (European Union Allowances, EUAs) under the European Union Emissions Trading Scheme (EU ETS). As a novel financial market, the dynamic analysis of its volatility is essential for policymakers to assess market efficiency and for investors to carry out an adequate risk management on carbon emission rights. In this research, the main autoregressive conditional heteroskedasticity (ARCH) models were applied to evaluate and analyze the volatility of daily data of the European carbon future prices, focusing on the last finished phase of market operations (phase III, 2013-2020), which is structurally and significantly different from previous phases. Some empirical findings derive from the results obtained. First, the EGARCH (1,1) model exhibits a superior ability to describe the price volatility even using fewer parameters, partly because it allows to collect the sign of the changes produced over time. In this model, the Akaike information criterion (AIC) is lower than ARCH (4) and GARCH (1,1) models, and all its coefficients are significative (p < 0.02). Second, a sustained increase in prices is detected at the end of phase III, which makes it possible to foresee a stabilization path with higher prices for the first years of phase IV. These changes will motivate both companies and individual energy investors to be proactive in making decisions about the risk management on carbon allowances.

A risk measurement study evaluating the impact of COVID-19 on China's financial market using the QR-SGED-EGARCH model.

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis.

Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality.

This paper explores the asymmetric effect of COVID-19 pandemic news, as measured by the coronavirus indices (Panic, Hype, Fake News, Sentiment, Infodemic, and Media Coverage), on the cryptocurrency market. Using daily data from January 2020 to September 2021 and the exponential generalized autoregressive conditional heteroskedasticity model, the results revealed that both adverse and optimistic news had the same effect on Bitcoin returns, indicating fear of missing out behavior does not prevail. Furthermore, when the nonlinear autoregressive distributed lag model is estimated, both positive and negative shocks in pandemic indices promote Bitcoin's daily changes; thus, Bitcoin is resistant to the SARS-CoV-2 pandemic crisis and may serve as a hedge during market turmoil. The analysis of frequency domain causality supports a unidirectional causality running from the Coronavirus Fake News Index and Sentiment Index to Bitcoin returns, whereas daily fluctuations in the Bitcoin price Granger affect the Coronavirus Panic Index and the Hype Index. These findings may have significant policy implications for investors and governments because they highlight the importance of news during turbulent times. The empirical results indicate that pandemic news could significantly influence Bitcoin's price.

The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets.

Understanding the dynamics of cryptocurrency markets during financial crises such as the recent one caused by the COVID-19 pandemic is crucial for policy makers and investors. In this study, the effect of COVID-19 pandemic on the return-volatility and return-volume relationships for the ten most traded cryptocurrencies, namely Tether, Bitcoin, Ethereum, Ripple, Litecoin, Bitcoin Cash, EOS, Chainlink, Cardano, and Monero is examined. Further, the behavior of cryptocurrencies during COVID-19 pandemic is compared with less volatile markets such as Gold, WTI, and BRENT crude oil markets. To study the effect of volatility on cryptocurrency return, an EGARCH-M model is employed while for the return-volume relationships the VAR model and Granger causality tests are utilized. Results show that the return-volatility relationships for Tether, Ethereum, Ripple, Bitcoin Cash, EOS, and Monero are significant during COVID-19 pandemic, while the same relationship is not significant prior to the pandemic for any of the studied cryptocurrencies. Our findings of the return-volume relationship support the availability of causal relations from return to trading volume changes for Chainlink and Monero in the pre-COVID-19 period and for Ethereum, Ripple, Litecoin, EOS, and Cardano during the COVID-19 period. However, considering the absolute values of returns, we found a significant relationship from cryptocurrencies' absolute returns to trading volume changes for both the prior and during COVID-19 periods. From a managerial perspective, gold can be considered a suitable asset for portfolio hedging during the pandemic period and trading volume can help traders and investors identify the effect of momentum and potential trend in cryptocurrencies on their investments.

Volatility in metallic resources prices in COVID-19 and financial Crises-2008: Evidence from global market.

The prevalence of uncertainty is evident in natural resources and financial markets almost every period. However, the global financial crisis and the recent Covid-19 pandemic is considered the most distressful event that disturbs the global economic and financial performance. In such crises, natural resource (mineral) prices also fluctuate as a result of demand and supply shocks. Identifying volatility in metallic resource prices is now the time's need, which consequently leads to implementing appropriate policies for recovery of the global markets. In this sense, the current study analyzed these two period from August 21, 2007, to December 31, 2009 (global financial crisis) and from January 01, 2019, to September 17, 2021 (Covid-19 pandemic). The empirical results obtained via threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and exponential autoregressive conditional heteroscedasticity (EGARCH) model asserted that volatility exists in metallic resource prices in both the crises periods. Concerning the global financial cristhe metallic resource prices were more volatile in 2008, while such priwere are highly volatile during the Covid-19 pandemic peak year (2020). Additionally, volatility in metallic resources is found higher in the Covid-19 pandemic, relative to global financial crisis. Based on the empirical results, this study suggests the appropriate policy measures that could help tackle the issue of metallic resource price volatility.

Testing volatility and relationship among BRICS stock market returns.

BRICS economies are important in recent times because the economic growth rates will be higher than the growth rates of G-6 economies in the near future. But the year 2020 has smashed up this tendency due to volatile stock markets of BRICS economies. A detailed examination of the BRICS stock market to determine volatility and relationships since the crisis of 2020 is hardly available in the available research. With this in mind, an attempt has been made to track the stock market's volatility and relationship among the BRICS (Brazil, Russia, India, China, and South Africa) stock market return based on the daily for the period from November 18, 2019 to May 7, 2021. This study deals with the statistical test of GARCH family model and ARDL model. GARCH model shows that the stock market of Russia and India are volatile. The EGARCH model demonstrates that leverage effect exists only in the Indian stock market. ARDL test validates a long-run relationship of the stock market of Russia with China and of the Indian stock market with South Africa. ARDL test also shows a short-run relationship running from the Brazil stock market to the other select stock market, from the Indian stock market to the stock markets of Brazil and South Africa, and from the South African stock market to the Indian stock market. So it can finally be said that investors under the BRICS stock markets should design adequate measures to protect their investments by executing appropriate hedging plan.

Twitter Sentiment Analysis and Influence on Stock Performance Using Transfer Entropy and EGARCH Methods.

Financial economic research has extensively documented the fact that the impact of the arrival of negative news on stock prices is more intense than that of the arrival of positive news. The authors of the present study followed an innovative approach based on the utilization of two artificial intelligence algorithms to test that asymmetric response effect. Methods: The first algorithm was used to web-scrape the social network Twitter to download the top tweets of the 24 largest market-capitalized publicly traded companies in the world during the last decade. A second algorithm was then used to analyze the contents of the tweets, converting that information into social sentiment indexes and building a time series for each considered company. After comparing the social sentiment indexes' movements with the daily closing stock price of individual companies using transfer entropy, our estimations confirmed that the intensity of the impact of negative and positive news on the daily stock prices is statistically different, as well as that the intensity with which negative news affects stock prices is greater than that of positive news. The results support the idea of the asymmetric effect that negative sentiment has a greater effect than positive sentiment, and these results were confirmed with the EGARCH model.

Backtesting VaR under the COVID-19 sudden changes in volatility.

We analyze the impact of the COVID-19 pandemic on the conditional variance of stock returns. We look at this effect from a global perspective, so we employ series of major stock market and sector indices. We use the Hansen's Skewed-t distribution with EGARCH extended to control for sudden changes in volatility. We oversee the COVID-19 effect on measures of downside risk such as the Value-at-Risk. Our results show that there is a significant sudden shift up in the return distribution variance post the announcement of the pandemic, which must be explained properly to obtain reliable measures for financial risk management.

Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index.

This research aims to compare the performance of ARIMA as a linear model with that of the combination of ARIMA and GARCH family models to forecast S&P500 log returns in order to construct algorithmic investment strategies on this index. We used the data collected from Yahoo Finance with daily frequency for the period from 1 January 2000 to 31 December 2019. By using a rolling window approach, we compared ARIMA with the hybrid models to examine whether hybrid ARIMA-SGARCH and ARIMA-EGARCH can really reflect the specific time-series characteristics and have better predictive power than the simple ARIMA model. In order to assess the precision and quality of these models in forecasting, we compared their equity lines, their forecasting error metrics (MAE, MAPE, RMSE, MAPE), and their performance metrics (annualized return compounded, annualized standard deviation, maximum drawdown, information ratio, and adjusted information ratio). The main contribution of this research is to show that the hybrid models outperform ARIMA and the benchmark (Buy&Hold strategy on S&P500 index) over the long term. These results are not sensitive to varying window sizes, the type of distribution, and the type of the GARCH model.

Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis.

This study investigates the dynamic mechanism of financial markets on volatility spillovers across eight major cryptocurrency returns, namely Bitcoin, Ethereum, Stellar, Ripple, Tether, Cardano, Litecoin, and Eos from November 17, 2019, to January 25, 2021. The study captures the financial behavior of investors during the COVID-19 pandemic as a result of national lockdowns and slowdown of production. Three different methods, namely, EGARCH, DCC-GARCH, and wavelet, are used to understand whether cryptocurrency markets have been exposed to extreme volatility. While GARCH family models provide information about asset returns at given time scales, wavelets capture that information across different frequencies without losing inputs from the time horizon. The overall results show that three cryptocurrency markets (i.e., Bitcoin, Ethereum, and Litecoin) are highly volatile and mutually dependent over the sample period. This result means that any kind of shock in one market leads investors to act in the same direction in the other market and thus indirectly causes volatility spillovers in those markets. The results also imply that the volatility spillover across cryptocurrency markets was more influential in the second lockdown that started at the beginning of November 2020. Finally, to calculate the financial risk, two methods-namely, value-at-risk (VaR) and conditional value-at-risk (CVaR)-are used, along with two additional stock indices (the Shanghai Composite Index and S&P 500). Regardless of the confidence level investigated, the selected crypto assets, with the exception of the USDT were found to have substantially greater downside risk than SSE and S&P 500.

The Impact of Inflation and Its Uncertainty on Pharmaceutical Prices: Evidence from Iran.

Pharmaceutical productions are recognized as an essential commodity in the economical literature; therefore, an increase in their prices leads to an increase in the household budget. Currently, about 15-20% of the entire health expenditure in Iran is allocated to the pharmaceutical sector. This study aimed to investigate the effect of inflation and its uncertainty on inflation in pharmaceutical prices in Iran. In this study, the monthly time series of consumer price index from 2001 to 2017 was used to calculate inflation uncertainty based on a generalized autoregressive conditional heteroscedasticity model. Hylleberg-Engle-Granger-Yoo test was performed to determine the stationary of the data. Feasibility tests were also used to explore the application of Autoregressive conditional heteroscedasticity family models to these data. The causal relationship between inflation uncertainty and inflation in the pharmaceutical sector was investigated using the Granger causality test. A causal relationship was found between inflation and inflation uncertainty at the 95% confidence interval for the monthly data during the study. It was revealed that Inflation uncertainty did not affect the inflation in the pharmaceutical prices, but inflation can be a cause of pharmaceutical inflation. Although inflation uncertainty has no association with pharmaceutical inflation, it seems that it could affect pharmaceutical inflation through inflation in other sectors. Therefore, adopting appropriate monetary policies aimed at controlling liquidity and inflation can effectively control pharmaceutical prices.

Futures market and the contagion effect of COVID-19 syndrome.

The paper aims to investigate the existence of financial contagion between China and its major trading partners during the ongoing COVID-19 pandemic using the multivariate ADCC-EGARCH model. The analysis results reveal significant financial contagion in most developed and emerging markets having significant trade relationships with China during COVID-19 syndrome. The evidence about financial contagion is vital for regulators and different classes of market participants for varying purposes, and hence the results should find practical implications similar to policymakers, investors, and risk managers.

Using E-GARCH to Analyze the Impact of Investor Sentiment on Stock Returns Near Stock Market Crashes.

Purpose: Investor sentiment, the willingness of market participants to invest, is a difficult concept to measure. Exploring the relationship between investor sentiment and stock returns can reveal how investor sentiment affects the operation of the stock market. Such an understanding can assist market participants in making more rational investment decisions based on market laws. Such an understanding can also assist regulators in their roles of supervision and policy making. Methodology: Although the E-GARCH model has the advantage of considering volatility clustering, it has not previously been used to investigate the impact of investor sentiment changes on the Shanghai Composite Index's market return. This research therefore applies the E-GARCH approach to data from 2015 to 2018, to explore the influence of investor sentiment on the return rate of the Shanghai Composite Index. Main Findings: There are three main findings. First, when the investor sentiment is increased by the same amount, the rate of return before a stock market crash will have a smaller increase than the rate of change after the crash, which is a new finding. Second, the rate of return on stocks is susceptible to emotional sentiment, rather than simply depending on stock price. Third, the tendency of retail investors to follow the crowd is less in periods of pessimism than it is in periods of optimism, which, in turn, can push up stock yields. Application: Based on these research results, this article can provide insights to understand how investors' subjective judgments on future earnings affect their investment behavior and how great the impact is on the market. At the same time, it can help investors make more rational investment decisions based on an understanding of market laws, and help regulators with guidance for their supervision and policy making. Originality/Value: This paper contributes to the theory of the investor sentiment index, improving the index construction method by adding two sentiment proxy indicators: investor activity ACT and stock market leverage level. After constructing the sentiment index and comparing it with the stock market index (Shanghai Composite Index), the fit is found to be improved.

The Informational Dynamics of Mean‒Variance Relationships in Fertilizer Markets: An Entropic Investigation.

The risk‒return trade-off is a fundamental relationship that has received a large amount of attention in financial and economic analysis. Indeed, it has important implications for understanding linear dynamics in price returns and active quantitative portfolio optimization. The main contributions of this work include, firstly, examining such a relationship in five major fertilizer markets through different time periods: a period of low variability in returns and a period of high variability such as that during which the recent global financial crisis occurred. Secondly, we explore how entropy in those markets varies during the investigated time periods. This requires us to assess their inherent informational dynamics. The empirical results show that higher volatility is associated with a larger return in diammonium phosphate, potassium chloride, triple super phosphate, and urea market, but not rock phosphate. In addition, the magnitude of this relationship is low during a period of high variability. It is concluded that key statistical patterns of return and the relationship between return and volatility are affected during high variability periods. Our findings indicate that entropy in return and volatility series of each fertilizer market increase significantly during time periods of high variability.

Chinese Influence in Australia: What Do Financial Markets Tell Us?

Australia-China relations, and especially Chinese influence in Australia, have been the subject of heated debate in Australia since 2016. The central issue is, how to balance concerns over Chinese influence in Australia with the economic benefits of Chinese trade and investment? This study-arguably the first of its kind-answers this question using rigorous empirical modelling. First, it uses Google Trends search results to measure Chinese influence in Australia. Second, it connects Chinese influence, as reflected in Google Trends search results, to financial markets, including stock markets, government bond markets and foreign exchange markets. Weekly data for January 2016-December 2019 are entered into an exponential generalised autoregressive conditional heteroskedastic model. The study finds that the effects of concerns over Chinese influence relate mainly to increased volatility of stock market indices and government bond yields, and downward pressure on the share prices of individual firms that are heavily exposed to Chinese markets. However, the overall effects appear to be minor or insignificant. The implications of these results are that China's economic coercion (if any) may not be effective, and Australia's responses to Chinese influence and interference (if any) may generate insignificant costs. Finally, this study makes original and significant academic contributions to academia by providing a novel framework for exploring international relations.

Examination of information release on return volatility: A market and sectoral analysis.

This paper examines the role of information release in explaining the return volatility of the Australian equity market. The study applies proxies of greater accuracy to examine the effect of public and private information on return volatility. Analyst price targets (PTR) and Morningstar stock star ratings (MSR) were used as private information proxies while Australian Securities Exchange (ASX) announcements were used as the public information proxy. Daily data was collected for ASX 200 listed firms for the period 2013 to 2017. Analysis was conducted at both the aggregate market level and the sectoral level. Findings suggest that PTR have the largest effect on return volatility at both levels, with varied effects within each sector. This indicates that investors rely heavily on this information when undertaking investment decisions. In contrast, MSR had a negligible effect, likely due to the lower degree of informational content. Public information had a minor effect on return volatility at both the aggregate market and sectoral levels. These mixed results show that information flow varies depending on the information type (i.e. public or private) with each sector interpreting the same type of information differently. The research findings provide a valuable guide to investors regarding the appropriate information to generate excess returns as well as to hedge against future losses.

Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model

Objective: To forecast the visceral leishmaniasis cases using autoregress integrated moving average (ARIMA) and hybrid ARIMA-EGARCH model, which offers a scientific basis to control visceral leishmaniasis spread in Kashgar Prefecture of Xinjiang, China. Methods: The data used in this paper are monthly visceral leishmaniasis cases in the Kashgar Prefecture of Xinjiang from 2004 to 2016. The sample data between 2004 and 2015 were used for the estimation to choose the best model and the sample data in 2016 were used for the forecast. Time series of visceral leishmaniasis started on 1 January 2004 and ended on 31 December 2016, consisting of 1 790 observations reported in Kashgar Prefecture. Results: For Xinjiang, the total number of reported cases were 2 187, the male-to-female ratio of cases was 1:1.42. Patients aged between 0 and 10 years accounted for 82.72% of all reported cases and the largest percentage of visceral leishmaniasis cases was detected among scattered children who accounted for 68.82%. The monthly incidences fitted by ARIMA (2, 1, 2) (1, 1, 1)

Predicting the number of visceral leishmaniasis cases in Kashgar, Xinjiang, China using the ARIMA-EGARCH model

Objective: To forecast the visceral leishmaniasis cases using autoregress integrated moving average (ARIMA) and hybrid ARIMA-EGARCH model, which offers a scientific basis to control visceral leishmaniasis spread in Kashgar Prefecture of Xinjiang, China. Methods: The data used in this paper are monthly visceral leishmaniasis cases in the Kashgar Prefecture of Xinjiang from 2004 to 2016. The sample data between 2004 and 2015 were used for the estimation to choose the best model and the sample data in 2016 were used for the forecast. Time series of visceral leishmaniasis started on 1 January 2004 and ended on 31 December 2016, consisting of 1 790 observations reported in Kashgar Prefecture. Results: For Xinjiang, the total number of reported cases were 2 187, the male-to-female ratio of cases was 1:1.42. Patients aged between 0 and 10 years accounted for 82.72% of all reported cases and the largest percentage of visceral leishmaniasis cases was detected among scattered children who accounted for 68.82%. The monthly incidences fitted by ARIMA (2, 1, 2) (1, 1, 1)12 model were consistent with the real data collected from 2004 to 2015. However, the predicted cases failed to comply with the observed case number; we then attempted to establish a hybrid ARIMA-EGARCH model to fit visceral leishmaniasis. Finally, the ARIMA (2, 1, 2) (1, 1, 1)12- EGARCH (1, 1) model showed a good estimation when dealing with volatility clustering in the data series. Conclusions: The combined model has been determined as the best prediction model with the root-mean-square error (RMSE) of 7.23% in the validation phase, which means that this model has high validity and rationality and can be used for short-term prediction of visceral leishmaniasis and could be applied to the prevention and control of the disease.