Conditional Heteroskedasticity Model Research Articles

Факторы доходности Ethereum как платформы для создания децентрализованных приложений

By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.

America's decoupling from China: A perspective from stock markets

AbstractAmerica's decoupling‐from‐China debate started after July 2018, reached its peak in August 2020, and is likely to continue even if it may not be a high priority for the Biden administration. Many studies have examined various aspects of this issue, especially the potential economic impacts on the US economy. Unlike previous research, this study looks at the response of stock markets. Using Google Trends data, this study created a weekly dataset from January 2020 to June 2021 to measure investor sentiment towards the US decoupling from China. Employing the generalised autoregressive conditional heteroskedasticity (GARCH) models, the study finds that concern over decoupling is associated with significant variations in stock market prices. From this we can infer that the overall effects of decoupling on the US economy are likely to be considerable.

The impact of the COVID-19 pandemic on travel cryptocurrency and stock market performances: an event-study approach

COVID-19 大流行对旅游加密货币和股票市场表现的影响：事件研究方法研究目的本文评估了 COVID 大流行在疫情期间很长一段时间对旅游加密货币和股票市场表现的影响。研究设计/方法/途径为 6 种旅行加密货币和纳斯达克证券交易所上市的前 10 大酒店、7 家航空公司和 26 家餐厅股票开发了 GARCH（广义自回归条件异方差）模型。 将事件研究方法应用于新型冠状病毒及其变种 Omicron 的出现。 此外, 还估计了相应资产因此类事件而产生的异常收益。研究发现结果表明, 旅游加密货币市场对疫情初期没有反应, 但纳斯达克酒店、餐厅和航空公司股票在美国出现疫情时出现异常负回报。 在美国正式宣布大流行后, 加密货币和旅游股均出现异常负回报, 但股票中的负回报要远高于加密货币。 相反, 作为对 Omicron 变体的回应, 只有酒店、餐厅和航空股出现异常的负回报。实际意义这些结果表明, 旅行加密货币是一种独立于酒店、餐厅或航空公司股票的金融工具。因此, 采用旅游加密货币可能有助于投资者和企业在 COVID-19 等长期危机期间分散风险。原创性/价值本文是第一份旨在调查 COVID-19 大流行对最近新兴的旅游加密货币市场的影响的实证研究, 并使用事件研究方法来调查它与旅游股票表现有何不同。

Forecasting agricultural price volatility of some export crops in Egypt using ARIMA/GARCH model

PurposeThis study focuses on forecasting the price of the most important export crops of vegetables and fruits in Egypt from 2016 to 2030.Design/methodology/approachThe study applied generalized autoregressive conditional heteroskedasticity (GARCH) model and autoregressive integrated moving average (ARIMA) model.FindingsThe results show that ARIMA (1,1,1), ARIMA (2.1,2), ARIMA (1,1,0), ARIMA (1,1,2), ARIMA (0,1,0) and ARIMA (1,1,1) are the most appropriate fitted models to evaluate the volatility of price of green beans, tomatoes, onions, oranges, grapes and strawberries, respectively. The results also revealed the presence of ARCH effect only in the case of Potatoes, hence it is suggested that the GARCH approach be used instead. The GARCH (1,1) is found to be a better model in forecasting price of potatoes.Originality/valueThe study of food price volatility in developing countries is essential, since a significant share of household budgets is spent on food in these economies, so forecasting agricultural prices is a substantial requirement for drawing up many economic plans in the fields of agricultural production, consumption, marketing and trade.

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.

Robust dispatching model of active distribution network considering PV time-varying spatial correlation

With a high proportion of photovoltaic (PV) connected to the active distribution network (ADN), the correlation and uncertainty of the PV output will significantly affect the grid dispatching operation. Therefore, this paper proposes a novel robust ADN dispatching model, which considers the dynamic spatial correlation and power uncertainty of PV. First, the dynamic spatial correlation of PV output is innovatively modeled by dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model. DCC can accurately represent and forecast the spatial correlation of the PV output and reflect its time-varying characteristics. Second, a time-varying ellipsoidal uncertainty set constructed using the DCC, is introduced to bound the uncertainty of the PV outputs. Subsequently, the original mixed integer linear programming (MILP) model is transformed into the mixed integer robust programming (MIRP) model to realize robust optimal ADN dispatching. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

Price volatility transmission in the value chain of fresh anchovies in Spain

This study examines the price volatility transmission of fresh anchovies (Engraulis encrasicolus) among different markets along the value chain in Spain. For this purpose, the prices in the first-hand sale, wholesale, and retail markets are considered. A vector autoregressive (VAR) model and an asymmetric multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model are used to analyse the relationship of price volatility among the markets in the value chain. The results indicate that the retail market has the lowest volatility. Therefore, volatility in the first-hand sale and wholesale markets is only minimally transmitted to consumers. Finally, asymmetric effects are observed in the price volatility transmission along the fresh anchovy value chain.

The dynamic role of the Japanese property sector REITs in mixed-assets portfolio

PurposeThis study examined the dynamic role of the Japanese property sector, particularly the real estate investment trusts (REITs), in mixed-asset portfolios of stocks and bonds, as well as office, retail, hotel and residential REITs.Design/methodology/approachDaily data were retrieved from 01 January 2008 to 31 December 2019. The sample time frame consisted of in-sample and out-of-sample periods. The dynamic conditional correlation-generalised autoregressive conditional heteroskedastic (DCC-GJRGARCH) model was deployed to obtain the forecast estimates of time-varying volatility of REITs and correlations with other assets. The estimates were employed to construct out-of-sample portfolios based on the three assets for daily investment. The five sets of portfolios with each individual property sector REITs, as well as a portfolio of stocks and bonds that served as a benchmark, were produced. The average utility for each set of portfolios was estimated and compared with the average utility of the benchmark portfolio. The average transaction cost (TC) for portfolio rebalancing was calculated as well.FindingsThe forecast of volatility estimates for each property sector revealed that each asset displayed a similar pattern with the differences in the volatility magnitude. Notably, hotel and retail REITs were more volatile than other property sector REITs. The property sector REITs exhibited a positive correlation with stocks but negatively linked with bonds. The results unveiled the diversification benefits of incorporating property sector REITs. The portfolio with property sector REITs had higher risk-adjusted returns and utility, compared to portfolio consisting of stocks and bonds. The benefits outweighed the TC for portfolio rebalancing.Practical implicationsThis study highlights the importance of quantifying the conditional time-varying volatility and correlations of the property sector REITs with other asset returns, especially for investment decision, to select and include property sector REITs in mixed-asset portfolios. For fund managers seeking liquid assets in daily investment, this analysis suggests the inclusion of hotel and retail REITs to enhance REITs' portfolio performance.Originality/valueThis study is the first to investigate the dynamic characteristics of the volatility and correlation of each property sector REITs with other financial assets by employing the conditional framework that accounted for short- and long-run persistency in economic shocks. The reported outcomes shed light on the differences in the underlying properties that contribute to the variances in dynamic volatility of each sector REITs, as well as REITs' correlations with stocks and bonds. This application enables the authors to transmit the dynamics of variance-covariance matrix amongst each property sector REITs, stocks and bonds into asset allocation problem on a daily basis.

Changes in the Polish Coal Sector Economic Situation with the Background of the European Union Energy Security and Eco-Efficiency Policy

Poland is a big user of fossil fuels for electricity and heat production. The most important fossil fuel is hard, brown coal and the Polish energy system is based on this source. However, the world has begun to decarbonize the climate and reduce the carbon dioxide and methane which are the main gasses impacting climate change. The main aim of this paper was to recognize changes in Polish coal sector. We focused our attention to the economic situation and employment in coal sector in Poland. The time rage included 1989–2020 and the prognosis 2021–2025. The Polish coal sector faced dramatic changes. The number of hard coal mines decreased from 70 in 1990 to 21 in 2020. In the same timeframe, the prices for hard coal increased from 12.37 PLN/dt to 313.27 PLN/dt. The employment decreased from more than 350 thousand to less than 100 thousand people in hard coal mines. The decrease changes are the effect of strict policies of the European Union. The economic situation of Polish hard coal mines is rather poor. Polish mines achieved negative economic results due to the effect of poor management. We used advanced statistics, including the Augmented Dickey–Fuller test (ADF), to measure the stationarity of analyzed time series. We also used Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models and conducted a prognosis. Our research proved that the time series describing the hard coal economic situation were not stationary. The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models confirmed big changes in Polish coal sector economic results. The elaborated prognosis of variables proved that the price of hard coal will increase in 2021–2025. Moreover, the economic situation will be worse. Our analysis confirmed that global trends of the hard coal sector were influenced by the European Union (EU) energy policy and closing down the mines. The economic situation of Polish hard coal sector worsened.

How the effective reproductive number impacts global stock markets

AbstractThe pandemic caused by the novel coronavirus COVID‐19 has impact the economies of countries across the world. In a short period of time, researchers have begun to analyse the effect of the pandemic on global stock markets. Although the most known measurements of COVID‐19 are the number of new cases and deaths, there are more robust indicators. In particular, the effective reproductive number is one of the most important indicators to analyse the pandemic which indicates the degree to which the spread is under control. In this paper, we assess the impact that the Effective Reproductive Number (Rt) has on 26 countries around the world (32 stock market indexes) comparing the performance of various forms of Generalized AutoRegressive Conditional Heteroskedasticity models. The results demonstrate that of the 32 stock markets analysed, 37.5% had a negative effect with respect to Rt and only in 12.5% of the cases was the effect of the variation of Rt positive. This implies that in more than a third of the stock markets analysed as the pandemic progressed uncontrolled the result was a decrease in the value of the market index. The 11 of the 26 countries analysed had a negative and significant effect (Brazil, Germany, Indonesia, Israel, Italy, Japan, Russia, South Korea, Sweden, Taiwan, and United States). Findings suggest that the Effective Reproductive Number volatility had a significant impact on 10 of the 26 countries analysed (38.5%) (Australia, Brazil, Canada, China, India, Italy, Mexico, Russia, Singapore and United Kingdom).

Environmental effect on the variability of anchovy (Engraulis ringens) in northern Chile: Autoregressive conditional heteroskedastic approach with exogenonus variable and missing values

This article studies the monthly variability of anchovy (Engraulis ringens) in northern Chile, related with the environmental effect of sea surface temperature on the landings of the fishery. In order to achieve that goal, a variant of the autoregressive conditional heteroskedastic (ARCH) model is proposed, in which an additional covariate is included (sea surface temperature) and missing values are considered. To estimate the parameters of the model we use a least square type estimation procedure. The proposed model considers monthly data from the anchovy fishery in northern Chile from 2010 to 2020 with the sea surface temperature as an environmental exogenous variable. The results show the good performance of the model and its capability to further represent the anchovy variability by means of its estimated conditional variance.

Size, conditional idiosyncratic risk and price momentum: an international study

PurposeThis paper jointly examines how firm size and idiosyncratic risk impact momentum returns.Design/methodology/approachUsing regression analysis, the authors investigate how firm size and idiosyncratic risk impact price momentum. The authors review firm price data in 25 country markets in the Thomson Financial Datastream database from 1979 to 2009.FindingsThis study’s findings suggest price momentum is more significant among stocks with smaller size and higher idiosyncratic risk. The authors find that winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles.Research limitations/implicationsThis study’s results are consistent with the notion that firm size matters in price momentum and mispricing is greatest for small firms because of the greater risk potential to arbitrageurs. In addition, this finding that firms with higher idiosyncratic risk have greater price momentum supports the idea that investors underreact to firm-specific information.Practical implicationsThis work finds evidence that investors underreact to firm-specific information. As such, these findings are of particular interest for investors looking to exploit opportunities for abnormal returns through price momentum trading.Originality/valueThis paper jointly examines the effects of firm size and idiosyncratic risk on momentum returns. This investigation considers these effects in the global markets. This work adds to the research base by illustrating that both winner and loser portfolios have significantly smaller size and higher idiosyncratic risk than portfolios in the middle quintiles. Also unique to this study, the authors capture the time-variation of expected IdioRisk and the asymmetric effects of volatility by using an exponential general autoregressive conditional heteroskedastic (EGARCH) model to calculate conditional idiosyncratic risk.

How do the sectoral indices react to COVID-19 Evidence from an emerging economy

The study empirically examines the impact of the COVID-19 on different sectoral indices of the National Stock Exchange (India) using the event study method and a generalised autoregressive conditional heteroskedasticity (GARCH) model. We provide evidence of positive impacts on the auto, oil and gas, healthcare, and pharma sectors. While the bank, financial services, and private bank sectors are the most adversely impacted sectors, the PSU bank, media, and reality sectors are the least impacted, and the rest are moderately impacted sectors. The overall impact of COVID-19 was negative until the implementation of nationwide lockdowns and the announcement of stimulus packages. The GARCH results exhibit more substantial evidence for the negative impact of the pandemic on the FMCG, IT, metal, oil and gas, and PSU bank sectors. We also find a more favourable impact on FMCG, pharma, and healthcare sectors in India.

Preispitivanje iznenadnih promena i postojanje volatilnosti na evropskim tržištima kapitala - neki empirijski dokazi

Understanding the behavior of market volatility is crucial for asset pricing, portfolio selection, risk management, and trading strategies. The standard Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model assumes that there is no shift in variance, hence its inability to produce a good estimate of volatility persistence. Thus, this research paper re-examines volatility persistence as well as the sudden changes in variance for some major European capital markets-French CAC 40, German DAX 30, and Britain's FTSE 100 stock. The study captures the simultaneous shifts in variance, detected by the iterated cumulative sums of squares (ICSS) algorithm, incorporated into the multivariate BEKKGARCH model. Information obtained shows that the detected changes correspond to both global and domestic events. Results also showed that volatility persistence is reduced in a controlled volatility change model compared to a model ignoring volatility changes. The implication of these results indicates that previous studies on European volatility persistence may have reported overestimated results.

Modelling and estimating volatilities in exchange rate return and the response of exchange rates to oil shock

Developing countries have persistently witnessed volatile exchange. Such volatility triggered instability in their exchange rates which induced colossal fluctuations in currency rates leading to uncertainty for both the consumers and firms. All these have instigated changes in official exchange rates that are harmful to underlie trade patterns in these countries. This study estimated fluctuations in daily exchange rate returns of ten African countries using generalized autoregressive conditional heteroskedasticity (GARCH) models, having ascertained the significance of autoregressive conditional heteroskedasticity (ARCH) effects. Structural vector autoregression (SVAR) estimator was utilized. Results showed Kenya shilling is the most relatively stable currency, whereas the Malawian kwacha is the most volatile among the currencies. There had been a series of random spikes in the exchange rate of Ghanaian cedi. Ghana and Kenya exchange rates are best projected using EGARCH, whereas SGARCH may be more efficient in estimating the volatility of Morocco and Zambia exchange rates. Leverage effects indicated a considerable magnitude of the adverse impact of bad news in the foreign exchange (FX) markets of Ghana and Zambia. Volatility shocks are expected to last in the future in those countries.

CBOE Volatility Index Forecasting under COVID-19: An Integrated BiLSTM-ARIMA-GARCH Model

After the outbreak of COVID-19, the global economy entered a deep freeze. This observation is supported by the Volatility Index (VIX), which reflects the market risk expected by investors. In the current study, we predicted the VIX using variables obtained from the sentiment analysis of data on Twitter posts related to the keyword “COVID-19,” using a model integrating the bidirectional long-term memory (BiLSTM), autoregressive integrated moving average (ARIMA) algorithm, and generalized autoregressive conditional heteroskedasticity (GARCH) model. The Linguistic Inquiry and Word Count (LIWC) program and Valence Aware Dictionary for Sentiment Reasoning (VADER) model were utilized as sentiment analysis methods. The results revealed that during COVID-19, the proposed integrated model, which trained both the Twitter sentiment values and historical VIX values, presented better results in forecasting the VIX in time-series regression and direction prediction than those of the other existing models.

The Euro exchange rate's resistance to the exogenous shock caused by COVID-19

Background: The COVID-19 pandemic represents the greatest exogenous global shock in the last few decades, which has deeply affected the macroeconomic aggregates around the world. Bearing in mind that COVID-19 pandemic is an exogenous shock; its effect on the macroeconomic aggregates will take time to be analysed, while it has a persistent impact on the financial markets. Purpose: One-third of the transactions worldwide includes the Euro. Hence, the main objective of this study is to estimate the Euro exchange rate's resistance to the exogenous shock caused by the COVID-19 pandemic. Study design/methodology/approach: This paper employs the General AutoRegressive Conditional Heteroskedasticity (GARCH) model to examine the EUR/USD exchange rate's resistance to the global exogenous shock caused by the COVID-19. In other words, the authors try to find an answer to question whether the COVID-19 pandemic affects the EUR/USD exchange rate volatility. Finding/conclusions: The results show that the COVID-19 pandemic has no effect on the EUR/USD exchange rate volatility in the long run. These results may confirm our assumption of the resistance of the financial market to the exogenous shock and are useful for anyone needing forecasts of the exchange rate futures movements. The obtained results produce pragmatic expertise in order to manage exchange rate risk and should support policymakers to advance exchange rate policy. Limitations/future research: As a limitation of this study, the authors state the estimation of the Euro exchangeratès resistance to only one exogenous shock, caused by COVID-19. Bearing in mind that in the considered period was also the world economic crises which might have caused a higher volatility then COVID-19, for further research the authors propose an examination of the detailed estimation of the Euro exchangeratès resistance to different exogenous shocks.

Influence of demonetization on various sectors of the Indian economy

Background: India demonetized the currency in November 2016, scrapping 86.9 percent of the currency in circulation. This policy disrupted most economic activities because India was predominantly a cash economy. Purpose: The study aims to analyze the impact of demonetization on the informal-formal sector and the Indian stock markets, where investment reflects investors' confidence. Another purpose is to know the usefulness of demonetization in the proliferation of digitalization. Study design/methodology/approach: The study incorporates primary data to determine the impact on informal and formal workers' income and the acceptance of digitalization in rural-urban areas in Faridabad, Haryana. A survey was conducted, and samples for informal-formal workers and rural-urban households were collected and analyzed using the F test and the ANOVA model using an independent dummy or qualitative variables. The secondary data of the Indian stock market were empirically tested and forecasted using the Autoregressive Conditional Heteroskedasticity (ARCH) model. Finding/Conclusions: The empirical analysis reveals that after demonetization, informal workers' earnings dropped significantly, and there is a substantial income disparity between informal-formal workers. A wide gap persists in adopting digital transactions due to low awareness of digital instruments in rural areas compared to urban areas. On the contrary, no significant impact is noticed in the Indian stock market as the forecasted value of shares trading depicts positive growth. The study identifies the gaps in policy implementation. It exposes the implementation of macroeconomic policies ensuring the protection of the interest and livelihood of economically vulnerable populations. The spread of awareness towards electronic transactions may help to promote digitalization Limitations/future research: The study is limited to a few areas. Hence, the scope of future research rests on macro-level data where comparison could be conducted between rural and urban areas across various states in India.

A comparative study between retail sukuk and retail bonds in Indonesia

This research investigates the risk level of retail sukuk price fluctuation compared with conventional retail bonds and the secondary market behaviour of both investment instruments. Expected shortfall, generalised autoregressive conditional heteroskedasticity model and cointegration test are used to analyse series of retail sukuk and conventional retail bonds. The evidence of clustering volatility indicates an increase in domestic investors in the Indonesian capital market. The purpose of the government in expanding domestic individual investor base of the capital market is quite successful. Moreover, both investment instruments have benefits of diversification. The finding encourages fund managers to diversify the portfolio in the retail instruments. At the same time, the individual investors can increase their wealth through capital market.

환율변동성이 베트남의 무역수지에 미치는 실증적 영향: DYNARDL 모형 분석

This study investigate the effect of exchange rate volatility on trade balance for Vietnam during the period of 2010 from first quarter to the 2021 second quarter. The dynamic simulations of autoregressive distributed lag (DYNARDL) approach is applied to analyze positive and negative changes on trade balance of exchange rate volatibility, finalcial development, financial globalization and economic growth in Vietnam. The conditional variance of the generalized autoregressive conditional heteroskedasticity (GARCH) model is used to estimate the exchange rate volatility. The results indicated a significant positive effect of real exchange rate volatility on Viet Nam’s balance trade while there was no effect of nominal exchange rate volatility.