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The objective of this article is to examine the volatility spillover effect between the foreign exchange market and the stock market of Brazil, Russia, India, China and South Africa (BRICS) countries along with Japan as the developed country in the region, affecting the BRICS countries. Generalized Autoregressive Conditionally Heteroscedastic (GARCH) (1,1) method is used to study the volatility between the stock market and the foreign exchange market in selected countries, and asymmetric model, that is, Exponential Generalized Autoregressive Conditional Heteroscedasticity—EGARCH (1,1) is also used to investigate the presence of leverage effects in both stock market and foreign exchange market in selected countries. GARCH findings suggest a two-way volatility spillover between the stock market and foreign exchange markets for India, China and South Africa. In BRICS countries, volatility spillover from the currency market to the stock market is seen as more evident and robust as compared to spillover from the stock market to the currency market. A positive asymmetry in spillover is also observed from the foreign exchange market to the stock market. The findings of the study may provide valuable information to investors for decision-making in international portfolio investment and also for economic policymakers for their financial stability perspective. [ABSTRACT FROM AUTHOR]

We investigate the performances of the ARFIMA, HAR, and EGARCH models in capturing the time-varying property of idiosyncratic volatility (IVOL). We find that the expected IVOL predictions by HAR are superior. In diverse portfolio scenarios, a greater degree of judgment is required to assess the pricing ability of expected IVOLs. For the lowest value-weighted quintiles and the expected IVOL estimated by the HAR model, the IVOL-return relationship is negative. Conversely, the IVOL-return relationship is positive for the expected IVOL estimated by the EGARCH model. Further evidence suggests a complicated and mixed relationship between the expected IVOL estimated by the ARFIMA model and stock returns. [ABSTRACT FROM AUTHOR]

This study investigates calendar anomalies and their impact on returns and volatility patterns in the cryptocurrency market, focusing on day-of-the-week effects before and during the COVID-19 pandemic. Using advanced statistical models from the GARCH family, we analyze the returns of Binance USD, Bitcoin, Binance Coin, Cardano, Dogecoin, Ethereum, Solana, Tether, USD Coin, and Ripple. Our findings reveal significant shifts in volatility dynamics and day-of-the-week effects on returns, challenging the notion of market efficiency. Notably, Bitcoin and Solana began exhibiting day-of-the-week effects during the pandemic, whereas Cardano and Dogecoin did not. During the pandemic, Binance USD, Ethereum, Tether, USD Coin, and Ripple showed multiple days with significant day-of-the-week effects. Notably, positive returns were generally observed on Sundays, whereas a shift to negative returns on Mondays was evident during the COVID-19 period. These patterns suggest that exploitable anomalies persist despite the market's continuous operation and increasing maturity. The presence of a long-term memory in volatility highlights the need for robust trading strategies. Our research provides valuable insights for investors, traders, regulators, and policymakers, aiding in the development of effective trading strategies, risk management practices, and regulatory policies in the evolving cryptocurrency market. [ABSTRACT FROM AUTHOR]

This paper investigates five different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student's t, GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student's t-distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087. [ABSTRACT FROM AUTHOR]

Purpose – This paper examines the impact of macroeconomic volatility on stock volatility, both under normal conditions and during the COVID-19 pandemic in Vietnam. Design/methodology/approach – We extend the existing Exponential Generalized Autoregressive Conditional Heteroskedasticity model by adding a new component: the thresholds – the levels of macroeconomic volatility at which the market may respond differently. These thresholds are estimated for both positive and negative volatility. Findings – The impact of macroeconomic volatility on stock volatility is asymmetric: there are thresholds of macroeconomic volatility at which its pattern changes. These thresholds are higher in the case of positive volatility compared with negative volatility. The thresholds were also higher during the COVID-19 pandemic. Macroeconomic variables influence stock volatility differently depending on market conditions. While GDP is more significant in normal periods, interest rates affect it in both normal and unstable phases. Research limitations/implications – Our models consider only two variables representing macroeconomic variables: interest rate and GDP. Furthermore, only one lag period of the variables is included in the analysis. In the future, more macrovariables and longer lags could be included when computational techniques advance. Practical implications – Policymakers should consider the impact of macroeconomic volatility on the stock market when designing policies, especially at thresholds. Similarly, investors should pay more attention to macroeconomic volatility when constructing and managing their portfolios, particularly when such volatility is close to thresholds. Originality/value – The inclusion of thresholds as parameters to be estimated into the model provides more insights into the impact of macroeconomic variables on stock volatility.

This study examines the relationship between extreme weather conditions and the S&P500 return index, representing the U.S. stock market. The literature review and analysis show extreme weather events can impact the S&P500 return index. This effect is observed in two ways. First, extreme weather events create a market anomaly in the U.S. stock market, indicating that prices move in a way that cannot be explained by a rational model. Second, extreme weather events create financial uncertainty and have a negative impact on firms’ future cash flows. These findings suggest that investors and financial markets should be more cautious about extreme weather events. In addition, the impact of extreme weather on the U.S. stock market is weak. This can be explained in two ways. First, extreme weather events are predictable and seasonally recurring. Second, the American stock market is close to the efficient market hypothesis.

This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market. The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship. The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market. This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

Abstract Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models.

Focusing on the Russia–Ukraine war, this paper investigates natural gas futures volatilities. Applying several hybrid GARCH and EGARCH models, which innovatively incorporate both fat-tailed distribution errors and structural breaks, we derive the following new evidence. First, our hybrid modeling approach is effective in timely capturing the natural gas futures volatility spike when tensions simmered on the Russia–Ukraine border. Second, the hybrid modeling approach is effective for not only GARCH modeling but also EGARCH modeling. Third, the volatility estimates from our hybrid models have predictive power for the volatilities of nonhybrid models. Fourth, the volatility estimates from the nonhybrid models lag behind the volatilities of our hybrid models. [ABSTRACT FROM AUTHOR]

This study leverages the Autoregressive Distributed Lag (ARDL) and Exponential Generalized Conditional Heteroscedasticity (EGARCH) models to conduct a thorough examination of the impact of fiscal and monetary policies on the Ghanaian stock market from 1990 to 2022. Key findings indicate that government spending and tax revenue, as components of fiscal policy, are positively associated with stock returns, contrasting with the negative influence of the industrial production index. On the monetary policy front, interest rates are found to negatively affect stock performance, while exchange rates and the money supply exert positive influences. In the short term, government spending enhances stock returns, although the effects of GDP and the industrial production index are inconsistent, with exchange rates and money supply demonstrating a negative impact. The study underscores the profound sway that policy decisions have on stock market dynamics, underscoring an urgent need for investors and policymakers to closely monitor policy shifts and their market reverberations. A pivotal policy recommendation emerging from this research is the strategic synchronization of fiscal and monetary policies by policymakers to underpin stock market stability and growth. Such harmonization can counteract the adverse effects of policy-induced volatility, cultivating an investment-friendly climate. Investors and policymakers are encouraged to draw upon a spectrum of credible sources, encompassing financial news, governmental releases, and market analyses, to remain abreast of policy evolutions. This research offers precious perspectives on the nexus between economic policies and market movements, offering value for academic inquiry and informing practical decision-making strategies.

ABSTRACT This study aimed to test the volatility model of BBCA and BMRI stocks on the IDX. The research problem is whether there is an influence of BBCA and LQ45 volatility on BMRI and vice versa. The study also tested whether BRIS's volatility was influenced by its majority shareholder, BMRI. The EGARCH model analyzed daily return data for 2015-2022 in bearish/bullish markets. The results showed that the data experienced heteroscedasticity problems, and the EGARCH Student's model was selected. The volatility of BBCA and BMRI returns does not affect each other but is influenced by LQ45 when bearish/bullish. The volatility of BRIS returns is influenced by BMRI only when it is bearish and the LQ45 index when bullish. The implications of the research prove the independence of stock investors (BMRI and BBCA) in making decisions. However, it was indicated that both investors were influenced by the decisions of most investors, which was reflected in the significance of the LQ45 index. ABSTRAK Tujuan penelitian ini adalah untuk menguji model volatilitas saham BBCA dan BMRI di BEI. Permasalahan penelitiannya adalah apakah terdapat pengaruh volatilitas return saham BBCA dan LQ45 terhadap BMRI dan sebaliknya. Penelitian tersebut juga menguji apakah volatilitas BRIS dipengaruhi oleh return saham pemegang saham mayoritasnya, BMRI. Model EGARCH digunakan untuk menganalisis data return harian tahun 2015-2022 saat pasar bearish/bullish. Hasil penelitian menunjukkan bahwa data mengalami masalah heteroskedastisitas dan model EGARCH Student's-t yang dipilih. Volatilitas return BBCA dan BMRI tidak saling mempengaruhi, namun dipengaruhi oleh LQ45 saat bearish/bullish. Volatilitas imbal hasil BRIS hanya dipengaruhi oleh BMRI saat bearish dan indeks LQ45 saat bullish. Implikasi penelitian membuktikan independensi investor saham (BMRI dan BBCA) dalam mengambil keputusan. Namun kedua investor tersebut terindikasi dipengaruhi keputusan mayoritas investor yang tercermin signifikansinya indeks LQ45.

Modern finans teorisinin köşe taşlarından biri olan Etkin Piyasa Hipotezi, piyasada mevcut olan tüm bilginin kullanılması suretiyle piyasanın üzerinde getiri elde edilemeyeceğini öne sürmektedir. Bununla birlikte finansal piyasalarda yapılan çalışmaların birçoğu, yatırımcıların bazı dönemlerde normalin üzerinde getiri elde ettiğini gösteren bulgular ortaya koymaktadır. Etkin Piyasa Hipotezi ile çelişen ve bazı dönemlerde elde edilen getirilerin ve katlanılan riskin diğer dönemlere göre farklılaştığını ifade eden etkiler takvim anomalileri olarak tanımlanmaktadır. Takvim anomalileri içerisinde genellikle günlere, aylara ve yıllara göre farklılaşan etkiler incelenmektedir. Bu çalışmada Bitcoin ve Ethereum kripto para piyasasında takvim anomalilerinin incelenmesi amaçlanmıştır. Bu kapsamda haftanın günü, yılın ayı ve yıl dönümü anomalileri kukla değişken ile temsil edilerek Bitcoin ve Ethereum için belirlenen TGARCH(1,1) ve EGARCH(2,2) modeline ilave edilmiş ve Bitcoin için 18.07.2010 – 17.05.2023 dönemini, Ethereum için 10.03.2016 – 17.05.2023 dönemini kapsayan günlük veriler üzerinden analiz yapılmıştır. Çalışma sonucunda elde edilen bulgular, Bitcoin ve Ethereum piyasasında haftanın günü ve yılın ayı anomalilerinin bulunduğunu göstermektedir.

Bu çalışmada özellikle gelişmekte olan ülkelerin iç ve dış denge amaçları açısından son derece önemli olan finansal dışa açıklık ve faiz oranının döviz kuru oynaklığı üzerindeki etkileri araştırma konusu yapılmıştır. Bu amaçla çalışmada; Türkiye için finansal açıklık, faiz oranları ve EGARCH yöntemi ile tespit edilen döviz kuru oynaklığına ilişkin 2002Q1-2023Q1 dönemine ait çeyreklik veriler kullanılmıştır. Çalışmada, geleneksel ADF birim kök testinin yanı sıra yeni nesil zaman serisi analizleri olan F-Kruse birim kök ve Fourier-Shin eş-bütünleşme testleri kullanılmıştır. Ayrıca uzun dönem katsayısı belirlemek için DOLS modeli tahmin edilmiştir. Yapılan analizlerin sonuçları, Türkiye’de döviz kuru oynaklığı üzerinde finansal dışa açıklıktaki ve faiz oranlarındaki artışın sırasıyla negatif ve pozitif etkileri olduğunu göstermektedir. Söz konusu bulgular, Türkiye’nin dış borçlanmaya ihtiyacı olan bir ülke konumunda olduğuna, faizlerin yanı sıra finansal istikrara da önem verilmesi gerektiğine işaret etmektedir.

The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis.

The media, speculators, investors, and governments throughout the world have all become increasingly interested in cryptocurrencies in recent years. The price swings of cryptocurrencies are notoriously unstable and have a high level of volatility. This study focused on modeling that volatility of cryptocurrencies, the purpose of this study is to identify the most suitable or appropriate innovation distribution and different GARCH Models to model the returns of the most popular cryptocurrencies. The majority of our work was focused on the top ten cryptocurrencies, but we also extended our analysis to 377 cryptocurrencies. To describe the time dependent volatility of the cryptos, we utilize eleven different GARCH models, including the sGARCH, iGARCH, GJRGARCH, eGARCH, tGARCH, AVGARCH, CSGARCH, ALLGARCH, NGARCH, APARCH, and NAGARCH. For the research period of September 14, 2014 to November 10, 2022, the daily closing prices of cryptocurrencies are collected. The underlying innovation(error) distribution are assumed to be from one of the following eight distributions of Normal, Student's t, Generalized Error, Skew Normal, Skew Student's t, Skew Generalized error, Normal Inverse Gaussian and Generalized Hyperbolic Distribution. Each GARCH-type model was fitted with this eight innovations. [ABSTRACT FROM AUTHOR]

In this study, the effects of financial openness and interest rate on exchange rate volatility, which are important especially for the internal and external balance purposes of developing countries, were investigated. For this purpose, quarterly data for the period 2002Q1-2023Q1 regarding financial openness, interest rates, and exchange rate volatility determined by the EGARCH method for Turkey were used in the study. In the study, ADF unit root test and the new generation time series analyses, F-Kruse unit root and Fourier-Shin cointegration tests, were used. Furtermore, DOLS model was estimated to determine the longterm coefficients. The results of used analyzes showed that the increase in financial openness and interest rates had respectively negative and positive effects on exchange rate volatility in Turkey. These findings have pointed that Turkey is a country in need of external borrowing, and it should be given importance financial stability as well as interest rates. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Selçuk University Social Sciences Vocational School is the property of Journal of Selcuk University Social Sciences Vocational School and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An empirical investigation was held regarding whether topological properties associated with point clouds formed by cryptocurrencies' prices could contain information on (locally) explosive dynamics of the processes involved. Those dynamics are associated with financial bubbles. The Phillips, Shi and Yu [33,34] (PSY) timestamping method as well as notions associated with the Topological Data Analysis (TDA) like persistent simplicial homology and landscapes were employed on a dataset consisting of the time series of daily closing prices of the Bitcoin, Ethereum, Ripple and Litecoin. The note provides some empirical evidence that TDA could be useful in detecting and timestamping financial bubbles. If robust, such an empirical conclusion opens some interesting paths of further research.

Copyright of Journal of Academic Finance is the property of Academic Finance Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An empirical investigation was held regarding whether topological properties associated with point clouds formed by cryptocurrencies' prices could contain information on (locally) explosive dynamics of the processes involved. Those dynamics are associated with financial bubbles. The Phillips, Shi and Yu [33, 34] (PSY) timestamping method as well as notions associated with the Topological Data Analysis (TDA) like persistent simplicial homology and landscapes were employed on a dataset consisting of the time series of daily closing prices of the Bitcoin, Ethereum, Ripple and Litecoin. The note provides some empirical evidence that TDA could be useful in detecting and timestamping financial bubbles. If robust, such an empirical conclusion opens some interesting paths of further research. [ABSTRACT FROM AUTHOR]

The blockchain ecosystem has seen a huge growth since 2009, with the introduction of Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In this article, we contribute a comparative analysis encompassing deep learning and quantum methods within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH (Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In this study, we evaluated how well Neural Networks and Genetic Algorithms predict "buy" or "sell" decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This research provides insights for investors, regulators, and developers in the cryptocurrency market. Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural network models for enhanced analysis. [ABSTRACT FROM AUTHOR]

SCDS (Sovereign Credit Default Swaps) are becoming more widely used as a country risk indicator after 2008 and stand out for providing real-time information rather than periodic reporting. The COVID-19 pandemic has led to economic disruptions and a decline in international trade. Understanding how the Pandemic affects SCDS return volatility in emerging economies like BRICS forms the motivation for our research. With this study, we aim to determine the impact of the COVID-19 Pandemic on SCDS return volatility in Brazil, Russia, India, China and South Africa, known as the BRICS countries. We used the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to analyze the data, which consisted of the daily closing price data for SCDS. The date of the first COVID-19 case in each country has been taken as the beginning of the COVID-19 Pandemic in each country. The results of the estimated GARCH models show that the volatility processes of the SCDS return series differ between periods. EGARCH model results indicate that shocks created by news in these countries during the Pandemic have a small and persistent effect on Brazil and Russia's SCDS return volatility, while they have a large and enduring effect on China and South Africa's SCDS return volatility. The findings will guide policymakers and portfolio managers in determining risk management models. [ABSTRACT FROM AUTHOR]

In today's environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China's carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China's carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China's carbon market. [ABSTRACT FROM AUTHOR]

The relevance of the development is determined by the possibility of testing a complex analytical methodology for forecasting the daily volatility of Bulgarian investment funds, which will support the investment community in making adequate investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1.1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk concentration of investment funds (in Bulgaria) through the testing of complex, analytical and specialized models from the GARCH group. The object of the study includes quantitative analysis, estimation and forecasting of daily volatility through the models GARCH, EGARCH, GARCH-M and TGARCH with specification (1.1). The research covers the net balance sheet value of forty-two investment funds for the period from 13 July 2020 to 13 July 2023, where the results of the research show that according to three of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund "Golden Lev Index 30" stands out. An exception to the thus formed trend is related to the TGARCH model in which the future conditional volatility is with the "EF Rapid" investment fund. When testing the models, we found that the GARCH model and the EGARCH model successfully optimize the regression parameters of the final equation for all analyzed investment funds, and as a result, valid forecasts are formed. In the case of the remaining two GARCH-M and TGARCH models, the impossibility of applicability of the model for some investment funds was found because of the optimization procedure, in which the parameters of the models have a value of zero. The present study is a unique mechanism for forecasting the daily volatility of Bulgarian investment funds, which further assists investors in risk assessment and is a prerequisite for making adequate and responsible investment decisions. The wide-spectrum toolkit of risk forecasting models allows their testing in investment funds with different risk natures (high-risk, balanced and low-risk). From a research point of view, in future research dedicated to modeling the risk attribution of investment funds, the analytical toolkit can be enriched with the following models: QGARCH, PGARCH, GJR-GARCH, IGARCH, SGARCH, AVGARCH, NGARCH and GAS. From a statistical point of view, we can apply the analyzed models to different probability distributions in order to describe the risky nature of investment funds. [ABSTRACT FROM AUTHOR]

Objectif : L'objectif primordial de cette étude est d'analyser les répercussions de la crise liée au COVID-19 sur le secteur bancaire turc. En se basant sur des données empiriques, nous cherchons à évaluer l'impact de la pandémie sur les performances de l'indice du secteur bancaire de Borsa Istanbul (BIST) pendant la période allant de janvier 2020 à décembre 2021. Cette analyse vise à fournir des insights précieux pour guider les décisions en matière de politique économique et d'investissement, en éclairant les acteurs concernés sur les dynamiques spécifiques qui ont façonné le paysage financier turc durant cette période tumultueuse. Méthode : Notre méthodologie de recherche repose sur l'utilisation conjointe de l'indice du secteur bancaire de Borsa Istanbul (BIST) et des données relatives aux cas quotidiens de COVID-19. Pour analyser les effets de la pandémie sur le secteur bancaire, nous avons opté pour la méthode Hétéroscédasticité Conditionnelle Autorégressive Exponentielle Généralisée (EGARCH). Cette approche statistique sophistiquée nous permet d'évaluer de manière précise et rigoureuse les variations de volatilité et les réactions du marché financier turc face aux chocs induits par la crise sanitaire mondiale. Résultats : Les résultats de notre étude révèlent un effet positif modéré du COVID-19 sur les rendements de l'indice du secteur bancaire BIST. Cette constatation suggère que malgré les défis posés par la pandémie, le secteur bancaire turc a démontré une résilience relative, potentiellement grâce aux mesures de soutien économique mises en œuvre pour atténuer les impacts négatifs. De plus, notre analyse indique l'absence de retombées significatives en termes de volatilité, soulignant ainsi la stabilité relative du secteur bancaire turc pendant la période étudiée. En outre, nous avons observé qu'il n'y avait pas d'effet de levier notable, ce qui suggère que les nouvelles positives et négatives ont eu des répercussions similaires sur le secteur bancaire turc au cours de la crise du COVID-19. Originalité : Cette étude apporte une contribution significative à la compréhension des effets de la crise du COVID-19 sur le secteur bancaire turc en utilisant une approche méthodologique novatrice et en se basant sur des données empiriques récentes. En mettant en lumière les dynamiques spécifiques du marché financier turc pendant cette période sans précédent, notre recherche offre des insights précieux pour les décideurs politiques, les investisseurs et les acteurs du secteur financier, les aidant ainsi à prendre des décisions éclairées dans un environnement économique incertain.

Volatility spillover informs whether the information in one market impacts the information in another. This paper examines whether oil market volatility spills over to the equity markets of selected SAARC countries. The study uses data from February 2013 to September 2019 to obtain updated evidence about the transmission of global oil price volatility to the equity markets of the SAARC member countries. The bivariate EGARCH model is used to test for volatility transmission from the oil market to the stock market. It is found that oil price shocks do not significantly impact equity market volatility, except in Bangladesh. Policymakers can use these findings when making policy decisions.

This paper investigates the impact of currency substitution on exchange rate volatility using monthly data from January 1990 to May 2019. The paper applies the exponential generalized autoregressive conditional heteroscedastic in mean (EGARCH-M) model as the estimation technique. The results reveal that currency substitution has a significant positive impact on exchange rate volatility. The paper also confirms the existence of leverage effects in the exchange rate volatility. It is also revealed that negative shocks are found to have greater effect than positive shocks. Furthermore, the results indicate that inflation targeting framework has a significant positive impact on exchange rate volatility. Based on the findings and discussion, the paper concludes that currency substitution increases exchange rate volatility in Ghana. Given the findings, vital policy implications aimed at reducing or eliminating volatility in exchange rate have been provided for policy consideration.

AbstractThis study tests for calendar anomalies in returns for petroleum and petroleum products via the futures market, specifically, the day-of-the-week (DOW) effect. The energy future contracts in this study are the WTI (West Texas Intermediate), Brent, RBOB (Reformulated Blendstock for Oxygenate Blending) Gasoline, Heating Oil, and Natural Gas. Futures provide a more liquid insight into price movements relative to spot prices, where financial market participants can engage. We ensure the most appropriate price is used by focusing on the most liquid contracts by combining the front two months of the studied commodities nearing expiration. Our research shows that the DOW effect varies across the respective energy commodities; however, for investors engaged in trading these futures, our results may help time their trade decisions.

Copyright of Economics & Regional Development is the property of Ferdowsi University of Mashhad Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Russia-Ukraine military conflict, commencing on February 24, 2022, notably impacted the international community. This study aims to quantify the volatility engendered by the conflict, drawing from the analysis of stock market indices across 40 countries. Time-series returns data from January 1 to December 31, 2022, were examined utilizing EGARCH econometric models. The relationship between volatility and news regarding the conflict was analyzed through a vector autoregression model, and associations between variables were examined using the Granger causality test. Findings suggest that some markets proximate to Ukraine, notably in Hungary, Polassnd Poland, Serbia, Bosnia and Herzegovina, and the Czech Republic, reacted in anticipation of the conflict, days prior to February 24. Remote markets experienced comparatively lower volatility, along with the primary stock markets. Additionally, a decline in volatility was observed as war-related information became available. Notably, the period between March 2 and March 16, 2022, recorded the highest volatility in 21 countries. Conversely, the value markets of the US, China, Japan, the UK, and Germany navigated the analyzed period with lower volatilities. These results demonstrate that conflict shocks influence stock markets globally. The implications of these findings are significant for investors, decision-makers, portfolio managers, investment funds, and central banks. [ABSTRACT FROM AUTHOR]

Copyright of Comparative Economic Research is the property of Wydawnictwo Uniwersytetu Lodzkiego and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the impact of investor sentiment on the Bitcoin returns and conditional volatility taking into account the Covid-19 outbreak by using different investor sentiment proxies and by employing the EGARCH model. Estimation results show that investor sentiment has a positive impact on the Bitcoin returns and their volatility, especially after the Covid-19 outbreak. The VAR model is employed to investigate whether investor sentiment and Bitcoin returns are related in a dynamic setting and to make distinguish between rational and irrational investor sentiments. The results from the VAR model show that both rational and irrational investor sentiments have an impact on Bitcoin returns indicating that the Bitcoin market is also driven by emotions and noise traders have an impact on the data generating process of Bitcoin returns. The positive impact of investor sentiment can be attributed to the fear of missing out (FOMO) behavior of speculative and irrational investors. [ABSTRACT FROM AUTHOR]

Copyright of Cameroon Academy of Sciences Journal is the property of Cameroon Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using a standard event study methodology and the EGARCH model, this study examined the depth of market anomaly at the onset of the Russia–Ukraine conflict in 2022. Equity markets in Africa and G7 nations were analyzed for their varied political and economic connections to the conflict. While the G7 nations were strongly opposed to Russia, African countries remained neutral. This study shows that abnormal losses in the initial period of the conflict were larger and more persistent in the G7 markets, contradicting the widely held notion that more developed equity markets are more efficient than the less developed markets. EGARCH results revealed that volatility persistence was widely present, although the leverage effect was only confirmed for U.S. and Canada. Throughout the period, commodity prices rose sharply, producing significant abnormal gains in the futures market. Unfortunately, this had a deleterious effect on African economies due to their heavy reliance on grain and fuel imports, all of which are priced in U.S. dollars, and which also rose sharply during the period. This study concludes with suggestions on how to mitigate currency and commodity price shocks to dollar-reliant and import-dependent economies. [ABSTRACT FROM AUTHOR]

Abstract 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.