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Abstract Political instability has increased drastically in Pakistan during the last few decades. This may intensify the fear of investors and eventually affect investment decisions. Therefore, the stock market’s reaction to political stability must be explored and appropriate policy measures should be prescribed. This paper examines the effect of political stability on stock market returns and volatility using time series data from Pakistan for the period from January 02, 2004, to May 31, 2018. The study uses the Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) models to achieve the main objectives. The findings of the GARCH and EGARCH models confirm that political stability has a positive and significant influence on both stock market returns and volatility in Pakistan. In addition, the EGARCH findings indicate that negative shock (political instability) creates more volatility in the stock market than positive shock (political stability). The results of this study reveal that financial market investors are affected more by political instability. In conclusion, this study suggests that political stability is an essential factor affecting financial market volatility, and it leads to stable financial markets. It also emphasizes that political instability needs to be resolved to achieve stable financial markets.

تعتبر وظيفة الاكتتاب من أحد أهم الوظائف الفنية لشركات التأمين، حيث أن نتائج أعمال شركات التأمين وقدرتها على الاستمرار في السوق تعتمد بشكل أساسي على السياسة الاكتتابية للشركة، بجانب الاعتماد على كل من السياسة الاستثمارية وسياسات إعادة التأمين.ويمثل معدل هامش ربح الاكتتاب التأميني أحد أهم المؤشرات المالية المستخدمة في قياس مدي جودة الأخطار التي يتم الاكتتاب فيها، كما يساهم في التخطيط الجيد لسياسة الاكتتاب وبما ينعكس على تحديد السعر العادل والذي يتناسب مع درجة الخطورة لكل خطر من الأخطار المكتتب فيها.وقد توصل البحث إلى قدرة نموذج ARIMA – GARCH عن نموذج ARIMA فقط، في تقديم وصف دقيق للتقلبات التي تحدث في السلسلة الزمنية الخاصة بهامش ربح الاكتتاب بالتطبيق على شركة المهندس للتأمين بالسوق المصري. حيث توصل البحث أن نموذج ARIMA(1,2,3) GARCH(1,2) هو أفضل النماذج التي تتناسب مع التقلبات التي تحدث في السلسلة الزمنية عن الفترة الزمنية من 1998 إلى 2023 الخاصة بهامش ربح الاكتتاب في شركة المهندس للتأمين للتأمينات العامة. هذا وقد بلغت القيمة التفسيرية لهذا النموذج 85%، وهو ما يشير إلى القدرة التنبؤية الذي يقدمها النموذج للتنبؤ بهامش ربح الاكتتاب.بالإضافة إلى ما سبق عند استخدام النموذج المقترح في التنبؤ بهامش ربح الاكتتاب بشركة المهندس للتأمين محل الدراسة خلال الفترة القادمة من 2024 إلى 2027، أتضح استمرار انخفاض معدل هامش ربح الاكتتاب، مما يعني ضرورة النظر مرة أخري في السياسة الاكتتابية لدي شركة المهندس للتأمين.

Numerous scholars have examined the relationship between stock returns and metal commodities, but there has been less emphasis on specific metal commodities such as coal and nickel. This study sought to investigate the correlation between coal and nickel price fluctuations and their impact on stock market volatility. The researchers employed the GARCH and EGARCH models for analysis. This study utilized data on coal price, nickel price, and two stock market indices in Indonesia, specifically LQ45 and IDX30, spanning from January 2020 to December 2023. The results indicate that there was no substantial correlation between coal shock and nickel shock on stock market volatility. The EGARCH model would be more suitable for prediction. The significance of this research lies in its discovery of the relationship between coal shock and nickel shock on stock return throughout a dynamic period.

This study aims to predict the ESG (environmental, social, and governance) return volatility based on ESG index data from 26 October 2017 and 31 March 2023 in the case of India. In this study, we utilized GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and LSTM (Long Short-Term Memory) models for forecasting the return of ESG volatility and to evaluate the model’s suitability for prediction. The study’s findings demonstrate the GARCH effect inside the ESG return volatility data, indicating the occurrence of volatility in response to market fluctuations. This study provides insight concerning the suitability of models for volatility predictions. Moreover, based on the analysis of the return volatility of the ESG index, the GARCH model is more appropriate than the LSTM model.

The aim of this study is to determine the number of transactions among the currencies, which will eventually become a part of our lives, cannot be physically held, can move quickly, and emerge as a new shopping and investment tool in the changing world order, as of the year (2023) when this study was conducted. The study focuses on the analysis of the variables that affect the most popular currency, Bitcoin. Although the analysis of variables that influence Bitcoin was determined as the primary aim of the study, the study also attempted to reach a general conclusion about the variables affected by the cryptocurrencies. Since there is no other cryptocurrency that is traded as much as Bitcoin, Bitcoin is thought to be a good model for the analysis of cryptocurrencies. The method used in the study was autoregressive conditional heteroskedastic (ARCH) models. It is believed that the most suitable models for the Bitcoin variable, whose value changes every second, are ARCH and its derivatives. Other models selected from the ARCH models were also added to the analysis as a method. The models used in the study can be listed as follows: linear ARC, generalized ARC (GARCH), exponential GARCH and threshold GARCH. A statistical model called autoregressive conditional heteroscedasticity (ARCH) is used to study the volatility of time series. Through the provision of a volatility model that more closely mimics actual markets, ARCH modeling is utilized in the financial sector to quantify risk. According to ARCH modeling, periods of high volatility are followed by even higher volatility, and periods of low volatility are followed by even lower volatility. In this study, 5 different variables were selected using literature to analyze the variables affecting Bitcoin returns using ARCH models. The dependent variable in the study is the price of Bitcoin. The remaining variables were included in the models as independent variables. These variables are actually variables that are accepted and selected as the best among a set of variables. In other words, 15 variables were first added to the study using the literature. After this, a correlation analysis was carried out. As a result of the correlation analysis, the variables with the highest correlation with the price of Bitcoin, which is the dependent variable, and the lowest correlation with each other were retained in the model. These variables are Bitcoin Price, Crude Oil Spot Price, Euro-Dollar Parity, Gold Spot Price and NASDAQ Composite Index. The study period is between 2020 and 2023 and it was studied using daily data. Days with no data were removed from the daily period from 2020 to 2023 and loss of information was prevented. After removing missing observations, this study examined the remaining 837 observations. During the research, while running the models created using different methods, it was found that the model that gives the best result is the GARCH model. In other words, when modeling the variables affecting bitcoin (cryptocurrency from the perspective of the population), it was seen that the GARCH model gave the best results when comparing linear ARCH, generalized ARCH (GARCH), exponential GARCH, and threshold GARCH of the ARCH model.Comparing the output of the GARCH model with other ARCH models not included in this study can be a recommendation for the future study [ABSTRACT FROM AUTHOR]

The booming real estate sector has been regarded as the “gray rhino” risk emerging in China over the past decade. Yet, the house price volatility per se has not been thoroughly examined. Filling the gap in the literature, this paper explores the house price volatility and its determinants for 70 large and medium-sized cities in China, using an extensive monthly data set from 2005 to 2019. We find evidence of significant geographical disparities in both the GARCH effects and the best-fitted volatility specification. Significant GARCH effects are found in 57 cities, among which 40% of cities show a persistent volatility pattern. We also find that both the house price volatility pattern and the associated volatility value are affected significantly by education and healthcare amenities.

This study contributes to the ongoing debate on the persistence of stock market anomalies in equity markets (McLean and Pontiff, 2016; Jacobs and Müller, 2020) and concentrates on the day-of-the-week effect in the European real estate sector. Interest payments and settlement effects were discussed as the main factors to explain this anomaly in the past. Today the persistence is highly questionable concerning the dynamically adjusting economic and institutional environment. While previous research indicated a significant Monday and Friday effect in other sectors, literature can only support a Friday effect for real estate. Furthermore, the real estate sector has a lower level of mispricing, which makes it more difficult for an anomaly to survive (Bampinas et al., 2016). The data is splitted in three ten-year periods to analyze the effect's existence over the long term. Applying OLS and GARCH models, results reveal an evolution in a significant Friday effect for cross-country indices (Europe and Global). From 1990 to 2000, the effect is weak but significant. It gains in importance during the period 2000 to 2009. In the final period until 2020, the anomaly weakens again but does not disappear for cross-country indices. From a country-specific perspective, there is no pattern in the significance of the day-of-the-week effect. The real estate sector has a local business character. Therefore, country-specific effects are possible, but a pattern is not found. In conclusion, there is still a day-of-the-week effect in the real estate sector in Europe from a cross-country perspective.

Abstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.

Accurate risk assessment is crucial for predicting potential financial losses. This paper introduces an innovative approach by employing expected risk models that utilize risk samples to capture comprehensive risk characteristics. The innovation lies in the integration of classical credibility theory with expected risk models, enhancing their stability and precision. In this study, two distinct expected risk models were developed, referred to as Model Type I and Model Type II. The Type I model involves independent and identically distributed random samples, while the Type II model incorporates time-varying stochastic processes, including heteroscedastic models like GARCH(p,q). However, these models often exhibit high variability and instability, which can undermine their effectiveness. To mitigate these issues, we applied classical credibility theory, resulting in credible expected risk models. These enhanced models aim to improve the accuracy of Value-at-Risk (VaR) forecasts, a key risk measure defined as the maximum potential loss over a specified period at a given confidence level. The credible expected risk models, referred to as CreVaR, provide more stable and precise VaR forecasts by incorporating credibility adjustments. The effectiveness of these models is evaluated through two complementary approaches: coverage probability, which assesses the accuracy of risk predictions; and scoring functions, which offer a more nuanced evaluation of prediction accuracy by comparing predicted risks with actual observed outcomes. Scoring functions are essential in further assessing the reliability of CreVaR forecasts by quantifying how closely the forecasts align with the actual data, thereby providing a more comprehensive measure of predictive performance. Our findings demonstrate that the CreVaR risk measure delivers more reliable and stable risk forecasts compared to conventional methods. This research contributes to quantitative risk management by offering a robust approach to financial risk prediction, thereby supporting better decision making for companies and financial institutions.

Abstract Purpose The Group Method of Data Handling (GMDH) neural network has demonstrated good performance in data mining, prediction, and optimization. Scholars have used it to forecast stock and real estate investment trust (REIT) returns in some countries and region, but not in the United States (US) REIT market. The primary goal of this study is to predict the US REIT market using GMDH and then compare its accuracy with that derived from the traditional prediction method. Design/methodology/approach To forecast the return on the US REIT index, this study used the GMDH neural network and the generalized autoregressive conditional heteroscedasticity (GARCH) model. In this test, the training samples, testing samples, and kernel functions of the GMDH model are controlled to investigate their impact on the accuracy of the machine learning approach. Corresponding experiments were performed using the GARCH model, and the accuracies of these two approaches were compared. Findings Compared with GARCH, GMDH’s accuracy is much higher, indicating that the machine learning approach can provide a highly accurate prediction of REIT prices. The size of the training samples and the kernel functions in the GMDH model affect the accuracy of the prediction results. In particular, the kernel function has a significant impact on prediction accuracy. The linear and linear covariance kernel functions are simple to train and yield accurate predictions, whereas the quadratic function is difficult to train. Even with small training samples, GMDH can outperform GARCH in prediction accuracy. Research limitations/implications Although GMDH shows good performance in predicting the US REIT return, it is still a black-box model, and the algorithm is difficult for financial analysts to develop and customize. The data used in this study come from the US REIT market, which is the world’s largest and most liquid market. Social implications This research shows that the GMDH model outperforms the GARCH model in forecasting REIT returns. Hence, investors can use the machine learning approach to make more accurate predictions of the target REITs’ returns and thus better investment decisions. Future investors and researchers may use GMDH to forecast the performance of REITs in other markets. Originality/value This is the first study to apply the GMDH neural network to the US REIT market and determine the impact of the two factors on its performance. For example, this research first discusses the impact of kernel functions on the US REIT market using the GMDH neural network. It also includes short-term daily prediction returns that were not previously considered, making it a valuable reference for financial industry analysts.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models. Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference. The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio. One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class. This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The influence of foreign exchange on all spheres of any economy is germane to determining its extent of development. Most developing countries have been subjected to managing the integrity of the exchange rate arising from their disadvantageous position in world trade. Nigeria is not an exemption either; this is due to its constant interventions in the foreign exchange market from time to time and by the policy of the existing government in power. However, the interventionist policy framework has been more harmful to Naira as the currency depreciates unabatedly. The pertinent question is whether monetary policy should be the only approach to effective exchange rate management or be combined with fiscal and income policies. Irrespective of the policy choice, the aim, target, and instrument must be complementary without unnecessary disruptions midway, as usually experienced in Nigeria. These inconsistencies have come to bear enormously on the country's exchange rate management. Rather than focusing on the impact of exchange rate volatility on industrial development that could warrant foreign exchange inflows, this paper considers industrialization as an approach to effectively managing exchange rates in Nigeria. This analysis employs a univariate GARCH and BEKK-MGARCH model, using high-frequency monthly time series data from 2000 to 2019, to examine the volatility transmission between the foreign exchange market and the industrial sector. The model estimation uses the Conditional Maximum Likelihood Technique (CMLT). It was discovered that industrial development positively influenced the official market exchange rate compared to the parallel market exchange rate, reducing its volatility. The study thus suggests the vigorous pursuit of foreign exchange earnings and usage policy by earning entities, floating Diaspora bonds, enhancing and encouraging remittances and repatriation of illicit financial outflows to enhance industrialization. This will discourage arbitraging, increasing foreign exchange inflows and stabilizing the economy.

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.

AbstractThis study examines the determinants of time-varying return volatility of Africa’s equity markets using monthly indices of eight top African stock markets. The conditional variance is modelled as a proxy for Africa’s volatility indices using the best fitting model among SGARCH, EGARCH and GJR-GARCH. In a bid to account for the dynamic and persistent nature of volatility, the least squares dummy variable bias correction (LSDVC) model is employed. The study finds that volatility of African stock markets follows a dynamic process and is explained by past volatility, domestic exchange rates, Treasury bill rates, money supply, inflation rates, movements in world crude oil prices, volatility of the US and UK stock markets and COVID-19 shocks. The results are largely robust to sub-sample analysis. Further analysis: pre-, during and post-crisis periods (Global Financial Crisis (GFC) and COVID-19 pandemic) reveal that (1) African stock markets became sensitive to advanced market volatility in the period during the GFC and have remained sensitive in the post GFC period and (2) past domestic market return volatility, Treasury bill rates and exchange rates have been the main drivers of stock return volatility during the COVID-19 pandemic in Africa. Additionally, markets in North Africa are revealed as the only African markets immune to advanced market volatility spillovers. These markets may thus provide international diversification opportunities for portfolio managers.

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.

Socially responsible mutual funds (SRMF) and the "antisocially conscious", Vitium Global Fund Barrier Fund (formerly known as the Vice Fund, the term used in this paper) returns, volatility patterns, and causal effects are examined in this study within the context of the lessons learned from the 2008 Global Economic and Financial Crisis (GEFC). In times of a new and unprecedented crisis due to the COVID-19 pandemic, a look back to our recent past reveals that volatility patterns on daily stock returns presented some level of predictability on prices for both types of funds. The research findings are significant as funds' potential predictability could help market players when designing their investment strategies. More specifically, an increase in volatility persistence is found after the GEFC, together with an increase in the Vice Fund's resilience to market shocks. Although all funds, without substantial differences, take time to absorb the shocks. A noteworthy outcome relates to SRMF that was able to achieve higher returns and exhibited lower volatility levels during the crisis period. Whereas the Vice Fund revealed long-run sustainable performance offering fund managers and investors investment opportunities that are endorsed by the fund performance over the period. Furthermore, unidirectional causality was found running from the Vice Fund to the SRMF, exhibiting a clear dominance during the GEFC period. The research findings contribute to the debate on the future of socially responsible investment, indicating that SRMF appears to be driven by "antisocially conscious" funds signaling limited rewards for investors inclined to invest in funds that are considered socially responsible.

The Bitcoin futures market is growing and, as such, becoming more sophisticated. A small change in price may therefore have a large impact on the market. This paper investigates the propensity of 18 different competing GARCH family models and error distributions to model and forecast the volatility of Bitcoin futures returns. The study employs two different time periods (from January 2, 2018 to June 14, 2021; and March 11, 2020 to June 14, 2021). From the results, iGARCH(1, 1)-Students't-distribution (STD) is selected as the best performing model among the constructed models for the first period. By fitting the best three models from the first period to the second period, the iGARCH(1, 1)-STD is again selected as the optimal model. However, the iGARCH(1, 1)-normal inverse Gaussian (NIG) provides a significant variance forecast when used for in-sample and out-of-sample forecasts before the financial crisis and during the financial crisis, respectively. Our results indicate the impacts of past squared shocks on squared returns of Bitcoin futures and the ability of iGARCH(1, 1)-STD to capture such innovations and the propensity of iGARCH(1, 1)-NIG to optimally forecast the variance of Bitcoin futures returns.

Time-varying calendar anomaly is thinly investigated in frontier stock markets. This study evaluates the day-of-the-week (DOW) calendar effects within the adaptive market hypothesis framework in frontier African stock markets. The study applies rolling analyses of the various GARCH family models to estimate daily stock indices return of Ghana stock exchange, Nairobi securities exchange, Botswana stock exchange and Bourse Regionale des Valeurs Mobilieres (BRVM) for 2000:1-2020:6 periods. The results show changing DOW effects in Kenya and Botswana which is consistent with the AMH. However, DOW effects cannot be validated in BRVM and Ghana. It suggests that each market must be treated with their own peculiarity even though they are ranked as frontier markets. We conclude that the changing DOW effects in the AMH context cannot be generalised in the frontier African markets and the existence of DOW effects must be treated with caution in BRVM and Ghana.

In this study, based on the monetary behavior theory, which considers the mean and standard deviation of GDP per capita besides the inflation difference between two countries, we first present a model for determining the fair value of the Russian ruble in the long run from 1999 to 2021 based on macroeconomic indicators including inflation, and GDP per capita. And then we modeled the effect of widespread Russian economic sanctions on the value of the Russian ruble during the turbulent days of February 9 to April 9. Our research results show that there is not much difference between market value and fair value in the long run. Also, by observing the behavior of the ruble during the turbulent days of February 25, 2022, to April 26, 2022, and by entering the conditional risk factor and weighted average of the ruble, the USD to ruble equality between 76.23 and 91.6 was evaluated

This paper uses theValue at Risk (VaR) and the Expected Shortfall (ES) to compare the riskiness of the two currency exchange rate volatility, namely BitCoin against the US dollar (BTC/USD) and the South African Rand against the US dollar (ZAR/USD). The risks calculated are tail-related measures, so the Extreme Value Theory is used to capture extreme risk more accurately. The Generalized Pareto distribution (GPD) is assumed under Extreme Value Theory (EVT). The family of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models was used to model the volatility-clustering feature. The Maximum Likelihood Estimation (MLE) method was used in parameter estimation. Results obtained from the GPD are compared using two underlying distributions for the errors, namely: the Normal and the Student-t distributions. The findings show that the tail VaR on the BitCoin averaging 1.6 and 2.8 is riskier than on South Africa’s Rand that averages 1.5 and 2.3 at 95% and 99%, respectively. The same conclusion is made about tail ES, the BitCoin average of 2.3 and 3.6 is higher (riskier) than the South African Rand averages at 2.1 and 2.9 at 95% and 99%, respectively. The backtesting results confirm the model adequacy of the GARCH-GPD in the estimation of VaR and ES, since all p-values are above 0.05.

The aim of this paper is to examine exchange rate volatility using GARCH models with a new innovation distribution, the Normal Tempered Stable. We estimated daily exchange rate volatility using different distributions (Normal, Student, NIG) in order to specify the performed model. In addition, a forecasting analysis is performed to check which distribution reveals the best out-of-sample results. We found that the estimated parameters of GARCH-NTS model outperform the GARCH-N and GARCH-t ones for all currencies. Besides, we asserted that GARCH-NTS and EGARCH-NTS are the preferred models in terms of out-of sample forecasting accuracy. Our results indicating the performance of GARCH models with NTS distribution contribute to increase the accuracy of risk measures which is very important for international traders and investors.

Abstract The importance of cryptocurrency to the global economy is increasing steadily, which is evidenced by a total market capitalization of over $2.18T as of December 17, 2021, according to coinmarketcap.com (Coin, 2021). Cryptocurrencies are too confusing for laymen and require more investigation. In this study, we analyze the impact that the effective reproductive rate, an epidemiological indicator of the spread of COVID-19, has on both the price and trading volume of eight of the largest digital currencies—Bitcoin, Ethereum, Tether, Ripple, Litecoin, Bitcoin Cash, Cardano, and Binance. We hypothesize that as the rate of spread decreases, the trading price of the digital currency increases. Using Generalized Autoregressive Conditional Heteroskedasticity models, we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region. These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies.

Shipping freight rates fluctuation is considered as one of the most important risk factors that participants face in the tanker shipping market (ship-owners, charterers, traders, hedge funds, banks and other financial institutions) in order to watch its evolution. This study examines freight rates for two of the most popular clean and dirty tanker routes; TC2 and TD3 from 22 May 2007 to 21 September 2015, using daily spot and future prices. The full data sample is divided into two sub periods, from 22 May 2007 to 13 August 2013 (in sample period) on which the model estimation section is based and from 14 August 2013 to 21 September 2015 (out of sample period) over which the Value at Risk is measured and backtesting process was performed. In all cases tested, there are observed high peaks and fat tails in all distributions. We apply a range of VaR models (parametric and non-parametric) in order to estimate the risk of the returns of TC2 route and TD3 route for spot, one month and three months future market. Backtesting tools are implemented in order to find the best fit model in terms of economic and statistical accuracy. Our empirical analysis concludes that the best fit models used for mitigating risk are simple GARCH model and non-parametric model. The above outcome seems to be valid a) for spot returns as well as for future returns and b) for short and long positions. In addition to the aforementioned conclusions, it is observed high freight rate risk at all routes. Our results are useful for risk management purposes for all the tanker shipping market participants and derivatives' counterparties.

A recession is part of the economic cycle that occurs in a certain period. This article investigates the effects of volatility spillover in the COVID-19 pandemic that occurred in 2020 using stock market index data from the US and ASEAN countries: Indonesia, Malaysia, Singapore, Thailand, and the Philippines. Investigations in the prior, during, and post of the 2008 crisis period were also investigated to analyze the differences between the two. This study used the BEKK-MGARCH model to analyze the spillover effect of volatility between stock indices. The results are not much different from previous research from Vo (2020), where all ASEAN stock markets except the Philippines were affected by the volatility spillover by the US market. In general, from the two periods, each ASEAN index also gives a bidirectional influence of volatility to other ASEAN indices, with the JKSE, KLSE, and SET indices having the most volatility integrated with other indices and the PSE index being the least integrated.

Perdagangan saham suatu negara memiliki karakteristik yang sama ataupun berbeda dengan negara lainnya. Karakteristik dari pasar tersebut merupakan cerminan karakter dari investor yang berperan dalam perdagangan di bursa saham tersebut. Meskipun ada perbedaan ataupun persamaan karakter pada bursa saham suatu negara, terdapat suatu hal yang dialami oleh semua bursa saham di berbagai negara, yaitu pergerakan nilai harga saham dan volume pada perdagangan saham secara dinamis yang dikenal dengan istilah volatilitas. Volatilitas sebagai risiko yang harus dihadapi seorang investor dalam berinvestasi memerlukan kemampuan memprediksi volatilitas sehingga risiko kerugian yang ditanggung investor bisa diperkecil. Model peramalan volatilitas dengan metode Garch dan E Garch diharapkan mampu menjadi salah satu pertimbangan investor dalam membuat keputusan investasi yang rasional. Kata Kunci: Volatilitas, Garch, E Garch, Saham

AbstractThis study investigated the impact of investor sentiment impact on sectoral returns and their volatility on the Johannesburg Stock Exchange using a proxy-based composite investor sentiment index and generalised autoregressive conditional heteroscedasticity models. Overall, findings showed a negative relationship between prevailing sentiment and subsequent returns and a positive relationship between investor sentiment and sector returns volatilities. Additionally, there was evidence of variability of sentiment effects on the sector returns and volatilities. Accordingly, firms that raise financing through the stock market, portfolio managers with investments thereon and policymakers seeking to ensure that markets operate efficiently need to consider the impact of market-wide investor sentiment on volatility and returns.

In this paper, the generalised autoregressive heteroskedasticity (GARCH) model is applied to the pricing of collateralised options in the South African equity market. Symmetric GARCH and nonlinear asymmetric GARCH (AGARCH) models are considered. The models are used to price fully collateralised and zero collateral options (European, Asian, and lookback options). The effect of collateral is illustrated by the difference between zero collateral and fully collateralised option price surfaces. Finally, the effect of asymmetry is shown by the difference between the symmetric and asymmetric GARCH option price surfaces.

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

The cryptocurrency market debate resumed in 2020 with renewed vigour as the price of Bitcoin surpassed late 2017 highs. This study aims to analyse possible factors of Bitcoin’s pricing at various cryptocurrency market development stages — before the 2017 price bubble, after and during the COVID-19 pandemic. The main method of analysis is a generalized autoregressive conditional heteroskedasticity model with conditional generalized error distribution (GARCHGED). Two groups of indicators are used as possible factors related to the Bitcoin dynamics. The first group consists of various quantitative indicators directly related to Bitcoin (the so-called internal factors) — the volume of exchange trade, the volume of transactions in the Bitcoin blockchain, the number of new and active wallets, hash rate, the sum of fees paid in the blockchain, as well as the dynamics of Google Trends search queries. The second group is the return on various financial assets — stock and bond indexes, commodities, and currency markets. The results of the analysis demonstrate the absence of a stable correlation between any of the factors under consideration and Bitcoin returns in all the periods that we focus on. In the period before the 2017 price bubble, the internal factors and Bitcoin returns showed generally co-directional dynamics, but the situation changed in 2018. In early 2021, the correlation between Bitcoin and traditional financial assets returns has increased significantly. We can conclude that Bitcoin is becoming a popular means of diversification as a high-risk asset, which, however, follows the pattern of a speculative bubble at the beginning of 2021. The increased demand for the need to invest in Bitcoin using various exchange-traded instruments (ETFs for cryptocurrencies) may soon lead to a further increase in the price of this cryptocurrency if such instruments are registered on the exchange.

Forecasting multiple-step value-at-risk (VaR) consistently across asset classes is hindered by the limited sample size of low-frequency returns and the potential model misspecification when assuming identical return distributions over different holding periods. This paper hence investigates the predictive power for multi-step VaR of a framework that models separately the volatility component and the error term of the return distribution. The proposed model is illustrated with ten asset returns series including global stock markets, commodity futures, and currency exchange products. The estimation results confirm that the volatility-filter residuals demonstrate distinguished tail dynamics to that of the return series. The estimation results suggest that volatility-filtered residuals may have either negative or positive tail dependence, unlike the unanimous negative tail dependence in the return series. By comparing the proposed model to several alternative approaches, the results from both the formal and informal tests show that the specification under concern performs equivalently well if not better than its top competitors at the 2.5% and 5% risk level in terms of accuracy and validity. The proposed model also generates more consistent VaR forecasts under both the 5-step and 10-step setup than the MIDAS-Q model. AcknowledgmentThe authors are grateful to the editor and an anonymous referee. This research is sponsored by the National Natural Science Foundation of China (Award Number: 71501117). All remaining errors are our own.

The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal that political and global news has a significant impact on KSE-100 index. Blue chip stocks are considered safer investments, while short-term panic responses often overshadow rational decision-making in the stock market. Investors tend to quickly react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting stock market fluctuations compared to the simple OLS method.

This literature aims to analyze the impact of the Dollar Index and Gold Price returns and volatility on stock market volatility of India and China, viz., Shanghai Stock Exchange and Bombay Stock Exchange Sensex, during the period of Covid-19. This study employs daily time-series data from January up to August for 2019, 2020, and a merged data of 2019-2020, i.e., Pre-Pandemic, Mid-Pandemic and Pre through Mid-Pandemic periods, respectively; to avoid possible abnormalities and heteroscedasticity, the GARCH (1,1) model is utilized to scrutinize the data depending on which distribution is more acceptable, GED or Gaussian, which is decided based on the Unit-Root and normality test results. The findings of this study prove that Gold Price mostly does have a significant effect on both markets, especially during times of financial crisis like the Covid-19 epidemic. Whereas Dollar Index has a significant impact on emerging markets such as India and China though significant effects persist in some cases, it is not valid in most cases. DOI: 10.26905/jkdp.v25i3.5142

Objective: The purpose of this paper is to demonstrate the effectiveness of the nonparametric GARCH model for the prediction of future Bitcoin prices. Methodology: The parametric GARCH models to characterize the volatility of Bitcoin returns are widely used in the empirical literature. Alternatively, we consider a non-parametric approach to model and forecast the volatility of Bitcoin returns. Results: We show that the volatility forecast of the nonparametric GARCH model yields superior performance compared to an extended class of parametric GARCH models. Originality / relevance: The improved accuracy of forecasting the volatility of Bitcoin returns based on the nonparametric GARCH model suggests that this method offers an attractive and viable alternative to commonly used GARCH parametric models.

The month of Ramadan is anticipated to influence the behavior of the stock market, where the environment in Ramadan is different from other months. During the Ramadan month, transformations in the social life of people are quite apparent and significant, and the overall economic activity tends to decelerate as the number of working hours decreases. This paper aims to explore the impact of the Ramadan month on the stock market returns, volatility, and trading volume on the Tunis Stock Exchange (TSE) between September 2009 and July 2019. To achieve these objectives, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) technique and the ordinary least squares regression are used. The results show that the impact of the Ramadan month on the daily returns is positive and statistically significant at a 1% level. It is also found that the impact of the Ramadan month on the volatility and trading volume is negative and statistically significant. The findings can help domestic and foreign investors and regulators to better comprehend the Tunisian stock exchange and investor behavior. Moreover, this research can help investors to develop their trading strategies.

According to the literature on risk, bad news induces higher volatility than good news. Although parametric procedures used for conditional variance modeling are associated with model risk, this may affect the volatility and conditional value at risk estimation process either due to estimation or misspecification risks. For inferring non-linear financial time series, various parametric and non-parametric models are generally used. Since the leverage effect refers to the generally negative correlation between an asset return and its volatility, models such as GJRGARCH and EGARCH have been designed to model leverage effects. However, in some cases, like the Tehran Stock Exchange, the results are different in comparison with some famous stock exchanges such as the S&P500 index of the New York Stock Exchange and the DAX30 index of the Frankfurt Stock Exchange. The purpose of this study is to show this difference and introduce and model the "reversed leverage effect bias" in the indices and stocks in the Tehran Stock Exchange.

A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors to the gold futures market, extremely complicates the analysis of the relationship between the underlying variables.One of the methods to forecast the price volatility is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. However, the high percentage of errors in such prediction has forced researchers to apply a variety of techniques in the hope of more accurate projections. Similarly, in this study, a hybrid model of the GARCH and Artificial Neural Network model (ANN) was used to predict the volatility of gold coin spot and future prices in the Iran Mercantile Exchange.In this study, variables such as global gold price, spot or future gold coin price (depending on which one is analyzed), US Dollar/IR Rial, world price of OPEC crude oil, and Tehran Stock Exchange Index were considered as factors affecting the price of gold coin. The results of the study indicate that the ANN-GARCH model provides a better prediction model compared to the Autoregressive models. Moreover, the ANN-GARCH model was utilized to compare the predictive power of spot and future gold coin prices, and it revealed that gold coin future price fluctuations predicted spot price of gold coin more accurately.

This study attempts to identify the existence of asymmetric volatility in the Islamic capital market in Indonesia during the Covid-19 pandemic. The paper employs the symmetric analysis of the GARCH (1,1) model and the asymmetric analysis of the TGARCH (1,1) model in order to identify Islamic capital market behaviour during the first 200 days after the first Covid-19 cases were confirmed. We used the daily closing prices of the Indonesia Sharia Stock Index (ISSI). The symmetric analysis of the GARCH (1,1) model revealed that the current value of return on the ISSI does not have a significant impact on its future value. On the other hand, the TGARCH (1,1) model showed that the asymmetric parameter coefficient was positive and statistically significant. Good news and bad news does not have the same level of impact on the volatility of returns on the ISSI. Furthermore, coefficients αi and γi in the variance equation indicate that good news has a higher volatility impact than bad news. The results indicate that investors should not to worry about the bad news effect of the Covid-19 pandemic, while the government should continue the mitigation of the spread of the coronavirus along with its economic recovery policy.

Abstract Drawing from the experience of the global financial crisis that sprang forth from the US stock market, an empirical assessment of the dynamic correlation analysis of financial contagion with evidence from (5) African countries (South African, Nigeria, Egypt, Kenya, Tunisia) is presented. Monthly stock prices indices from 2004 to 2018 was analyzed using the dynamic conditional correlation multivariate GARCH model to ascertain the contagious effect of the US to the selected African markets. By analyzing the correlation coefficient series, three phases of the crisis periods were identified {pre-crisis (2004–2007); crisis (2007–2009) and post-crisis (2009–2018), respectively}. The study revealed that a significant relationship exists between the returns of the US market and the African markets. The inspection of the pre-crisis, crisis, post-crisis mean and variance estimation shows that the crisis period is characterized by substantial increases in volatility, establishing that the shock experienced in the US posed a threat to the African markets being examined. Further, evidence revealed that in the crisis period, an increase in correlation (contagion) existed, while a continued correlation (herding) existed in the post-crisis period.

Este trabajo analiza la relación entre la volatilidad del precio del petróleo y rendimientos bursátiles sectoriales seleccionados en México (Industrial, materiales, financiero y de consumo discrecional) a través de la implementación de un modelo GARCH bivariado tipo VECH Diagonal para estimar sus covarianzas y correlaciones condicionales. El hallazgo más importante es que existe una relación estadística significativa entre los índices sectoriales y las variaciones en el precio del petróleo. Las correlaciones condicionales sugieren que durante la mayor parte del periodo del análisis la relación entre el precio del petróleo y los rendimientos sectoriales es positiva. La recomendación apoyada por los resultados descritos es que los inversionistas deben tomar en cuenta la interacción mencionada para generar coberturas de riesgo más robustas. Dentro de las limitantes de la investigación, se encuentra la escasez de información a nivel sector en el mercado bursátil del país. La aportación original de este estudio radica en el análisis con enfoque sectorial. Los resultados apoyan a la corriente que sugiere que las fluctuaciones en el precio del petróleo tienen un efecto directo en el mercado bursátil.

The aim of this study is to examine variables that influenced the Jakarta Composite Index (JCI) volatility during the outbreak of COVID-19. The independent variables that are used are gold return volatility and USD/IDR return volatility. This analysis uses daily-time series data which are divided into three periods, those are; before the pandemic (January 2019-August 2019), during the pandemic (January 2020-August 2020), and from January 2019 through August 2020. The method that is used in this study is GARCH analysis in order to dodge data abnormality and heteroscedasticity. Before conducting GARCH analysis, unit root and normality test are conducted to know if the data are stationaries and to decide which GARCH distribution is the most suitable for the data analysis (Gaussian, t-student, or GED distribution). It is proven in this study that during the COVID-19 pandemic, gold return volatility positively affected the JCI volatility and USD/IDR volatility negatively affected JCI volatility. This research can be used as a consideration for investors in choosing their investment during the pandemic or financial crisis by examining gold and USD/IDR volatility effect on JCI volatility. DOI: https://doi.org/10.26905/jkdp.v25i1.4980

This paper examines the intricate link between unobservable characteristics of directors on the corporate board and firm performance. It aims to extend the literature on corporate governance and firm strategic performance from the perspective of emerging African economies. A mix of performance measures were used (Tobin Q, return on assets, and share price) and unobservable characteristics were captured as a stochastic element or heterogeneity of observable board characteristics (board activity, gender diversity, size, and independence). The study applied non-linear generalized auto-regressive conditional heteroscedasticity model to examine the data set consisting of 299 firm-year observations from 23 financial firms listed on the Nigerian Stock Exchange from 2006 to 2018. Positive skewness and leptokurtic distribution were found for all the variables. Correlation matrix revealed no multicollinearity, as the highest value was 0.2386. Empirical results suggest that unobservable characteristics significantly and positively influence firm performance as measured by return on assets and share price. This is because the coefficient of the lagged-value of the variance scaling parameter is positive and significant at the 1% level. However, with respect to Tobin Q measure, the result was positive but not significant at the 5% level. Implicitly, the result is sensitive to performance proxies. Accordingly, this study concludes that unobservable characteristics drive firm performance. It is recommended that boards and regulators should pay attention to unobservable characteristics.

The stronger the level of economic integration between countries, the greater the need to study the formation patterns of the stock market reaction to the financial information signals. This concerns the Ukrainian stock market, which is now in its infancy, and which reaction to financial information signals is sometimes ambiguous. The research aims to identify the formation patterns of return and volatility indicators of the Ukrainian stock market reaction to the US financial information signals. To assess the direct nature of US financial information signals effect on the PFTS stock index, the GARCH econometric modeling toolkit was applied. The research information base is the PFTS stock index and the Federal Reserve System financial information signals at the discount rate for 2000–2019. The fetch is divided into intervals corresponded to the ascent and decline phases of the financial cycle. It was found that an unforeseen increase in the discount rate at the financial cycle decline phase by 25 basis points decreases the PFTS stock index return, on average by 2.9%. Besides, the hypothesis about the general change stabilizing effect in the discount rate on the Ukrainian stock market volatility at the financial cycle growth phase was confirmed. Nevertheless, for investors, the most essential is the regulator’s monetary signals in the discount rate at the financial cycle decline phases rather than at the ascent phases because there is a more significant increase in the volatility level.

Abstract This paper derives a new method for comparing the weak-form efficiency of markets. The author derives the formula of the Sharpe ratio from the ARMA-GARCH model and finds that the Sharpe ratio just depends on the coefficients of the AR and MA terms and is not affected by the GARCH process. For empirical purposes, the Sharpe ratio can be formulated with a monotonic increasing function of R-squared if the sample size is large enough. One can utilize the Sharpe ratio to compare weak-form efficiency among different markets. The results of stochastic simulation demonstrate the validity of the proposed method. The author also constructs empirical AR-GARCH models and computes the Sharpe ratio for S&P 500 Index and the SSE Composite Index.

Abstract Understanding the irrational sentiments of the market participants is necessary for making good investment decisions. Despite the recent academic effort to examine the role of investors’ sentiments in market dynamics, there is a lack of consensus in delineating the structural aspect of market sentiments. This research is an attempt to address this gap. The study explores the role of irrational investors’ sentiments in determining stock market volatility. By employing monthly data on market-related implicit indices, we constructed an irrational sentiment index using principal component analysis. This sentiment index was modelled in the GARCH and Granger causality framework to analyse its contribution to volatility. The results showed that irrational sentiment significantly causes excess market volatility. Moreover, the study indicates that the asymmetrical aspects of an inefficient market contribute to excess volatility and returns. The findings are crucial for retail investors as well as portfolio managers seeking to make an optimum portfolio to maximise profits.

The present study examines hedging effectiveness of futures contracts in India by using variance reduction approach and risk-return approach by applying eight econometric models. It is observed that OLS hedge ratio generates highest hedging effectiveness using variance reduction approach, whereas Naïve hedge ratio generates highest hedging effectiveness using risk-return approach. Overall, it is observed that time-invariant hedging model generates superior hedging effectiveness as compared to time-variant hedging model.

Abstract This study investigates the volatility in daily stock returns for Total Nigeria Plc using nine variants of GARCH models: sGARCH, girGARCH, eGARCH, iGARCH, aGARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH along with value at risk estimation and backtesting. We use daily data for Total Nigeria Plc returns for the period January 2, 2001 to May 8, 2017, and conclude that eGARCH and sGARCH perform better for normal innovations while NGARCH performs better for student t innovations. This investigation of the volatility, VaR, and backtesting of the daily stock price of Total Nigeria Plc is important as most previous studies covering the Nigerian stock market have not paid much attention to the application of backtesting as a primary approach. We found from the results of the estimations that the persistence of the GARCH models are stable except for few cases for which iGARCH and eGARCH were unstable. Additionally, for student t innovation, the sGARCH and girGARCH models failed to converge; the mean reverting number of days for returns differed from model to model. From the analysis of VaR and its backtesting, this study recommends shareholders and investors continue their business with Total Nigeria Plc because possible losses may be overcome in the future by improvements in stock prices. Furthermore, risk was reflected by significant up and down movement in the stock price at a 99% confidence level, suggesting that high risk brings a high return.

This study examines how the Nigerian Stock Exchange (NSE) is responding to the COVID-19 pandemic in the form of risk-return relationship and volatility. Panel data analyses of GARCH-in-mean and Threshold GARCH were estimated on three error distributional assumptions. All Share Index (ASI) from January 2020 to December 2020 for ten stock market indices on the NSE. Findings indicate that the cross-section return of the ten stock market indices returns exhibit a positive risk-return relationship during COVID-19 and the impact of bad news was found to have no significant impact on return volatility on the NSE. This indicates that the policy response during the pandemic is adequate to cushion the negative impact of COVID-19, which should be sustained.

The Nigerian stock market capitalization in 2007 was N 13.181 trillion but due to the meltdown, it reduced to N 7.030 trillion in 2009, indicating over 40% loss of investor’s value. The government, through Securities and Exchange Commission (SEC) introduced policies to stem the tide of the crisis. Therefore, this study evaluate volatility persistence of return in the market during pre and post meltdown. The mean reverting and half-life volatility shock of the GARCH model under three error distribution was employed. Finding indicates that return on the exchange exhibit high volatility magnitude after the meltdown but very low volatility magnitude before the meltdown. The generalized error distribution give the best estimate for pre and post meltdown. The study recommend the need to strictly monitor, restrict and regulate desperately optimistic noise (rumour) traders (investors) in the market, shorting to make money.

The authors undertook to examine 720 monthly observations of activity in 15 Arab stock markets over four years (from 2014 to 2017) to identify the dynamic linkages among those markets. To achieve this, several forms of the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model were utilized. Both panel and individual stationarity, in addition to cointegration tests, were employed to highlight the interaction between these markets. The results suggest that Arab stock markets have weak linkages with the exception of those of the Gulf Cooperation Council (GCC). The authors also find out that the TARCH, EGARCH, PARCH, and Component GARCH (1,1) models are suitable in terms of passing the econometric analysis tests. Nevertheless, they conclude that the EGARCH model is the most appropriate for capturing the cross-market dynamic linkages, thereby outperforming the other GARCH specifications under study. The empirical findings bear special implications for economic literature regarding linkages of stock markets in the Arab world.

Recurrent stock market fall and rise sequel by COVID-19, rising global inflation, increase in Fed interest rates, the unprecedented meltdown of technology stocks, fear of trade wars, tightening of governments’ fiscal policies call for a new trend in international investing. It is time for the investors to rethink, rebalance and reset their investment strategies to position and protect their portfolios during and post-pandemic period. This paper attempts to forecast the gold prices for the post-pandemic era and explores whether gold will serve as a decisive hedge during this transition period. The techniques of ARCH, GARCH, E-GARCH, A-PARCH, and GARCH-M is employed in forecasting the conditional volatility of gold spot price from Multi Commodity Exchange (MCX) of India. A total of 3631 observations were collected from the daily spot prices of gold from January 2009 to December 2022. The findings show that the gold prices in India are highly persistent similar to other emerging markets and that gold will remain a safe haven for investors and institutional investors in the post-pandemic period. This paper is the first of its kind to forecast gold prices for the post-pandemic period. The forecast price of 10-gram gold is expected to trade for 65,948 ₹ in the Indian MCX by 2026 if the gold prices behold its previous momentum. This forecast will help the investors to plan their portfolio diversification for the post-pandemic period.