Search

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. [ABSTRACT FROM AUTHOR]

Risk parity portfolio optimization, using expected shortfall as the risk measure, is investigated when asset returns are fat‐tailed and heteroscedastic with regime switching dynamic correlations. The conditional return distribution is modeled by an elliptical multi‐variate generalized hyperbolic distribution, allowing for fast parameter estimation via an expectation‐maximization algorithm, and a semi‐closed form of the risk contributions. A new method for efficient computation of non‐Gaussian risk parity weights sidesteps the need for numerical simulations or Cornish–Fisher‐type approximations. Accounting for fat‐tailed returns, the risk parity allocation is less sensitive to volatility shocks, thereby generating lower portfolio turnover, in particular during market turmoils such as the global financial crisis or the COVID shock. While risk parity portfolios are rather robust to the misuse of the Gaussian distribution, a sophisticated time series model can improve risk‐adjusted returns, strongly reduces drawdowns during periods of market stress and enables to use a holistic risk model for portfolio and risk management. [ABSTRACT FROM AUTHOR]

This paper examines the effects of the COVID-19 pandemic on the volatility of the TUNINDEX stock market. Our main goal is to decipher how this worldwide health epidemic has influenced Tunisia's financial stability. A thorough empirical approach was employed, leveraging sophisticated econometric techniques such as GARCH and VECM to scrutinize daily market data. The analysis uncovers a notable escalation in TUNINDEX volatility during the pandemic, emphasizing the critical role of investors' sentiment, economic, and monetary interventions in mitigating the crisis. These insights shed light on the dilemmas and prospects facing investors, portfolio managers, and policymakers in navigating market volatility amidst pandemic uncertainty. [ABSTRACT FROM AUTHOR]

This study advances the understanding of the Preferred Habitat Model's capacity to shed light on the inter-market transfer of mean returns and the diffusion of price volatility in Pakistani investment markets. It examines the extent to which returns in one market exert a systematic influence on returns across others under the potential sway of interest rate policy shifts, USD exchange rate volatility, and domestic inflation trends. Employing a methodological arsenal that includes the GARCH process, enhanced by Dynamic Conditional Correlations (DCC), as well as the Markov Switching Model, this research assesses the propagation of mean returns and volatility across markets. The analysis uncovers significant linkages between monetary policy and stock market indices, underscoring the profound impact of monetary policy on cross-market performance transmission. These insights are pivotal for regulators overseeing the nuanced interaction between monetary policy and market performance. They are crucial for local and international investors interested in developing economies, especially in Pakistan's markets. [ABSTRACT FROM AUTHOR]

Purpose: The increasing globalization and technological advancements have increased the information spillover on stock markets from various variables. However, there is a dearth of a comprehensive review of how stock market volatility is influenced by macro and firm-level factors. Therefore, this study aims to fill this gap by systematically reviewing the major factors impacting stock market volatility. Design/methodology/approach: This study uses a combination of bibliometric and systematic literature review techniques. A data set of 54 articles published in quality journals from the Australian Business Deans Council (ABDC) list is gathered from the Scopus database. This data set is used to determine the leading contributors and contributions. The content analysis of these articles sheds light on the factors influencing market volatility and the potential research directions in this subject area. Findings: The findings show that researchers in this sector are becoming more interested in studying the association of stock markets with "cryptocurrencies" and "bitcoin" during "COVID-19." The outcomes of this study indicate that most studies found oil prices, policy uncertainty and investor sentiments have a significant impact on market volatility. However, there were mixed results on the impact of institutional flows and algorithmic trading on stock volatility, and a consensus cannot be established. This study also identifies the gaps and paves the way for future research in this subject area. Originality/value: This paper fills the gap in the existing literature by comprehensively reviewing the articles on major factors impacting stock market volatility highlighting the theoretical relationship and empirical results. [ABSTRACT FROM AUTHOR]

Understanding which factors influence exchange rate movements is important for understanding economic development, trade patterns, investment decisions, and for designing economic policies. In this study, we allow for monetary, domestic and global financial variables to assess the relevant importance of each of the variables to exchange rate volatility in the case of Turkey. The paper investigates the dynamics of exchange rate volatility of the Turkish lira in a complementary perspective by employing both Generalized autoregressive conditional heteroskedasticity (GARCH) method and Lyapunov exponents over the period from March 1, 2019 to November 11, 2021. Firstly, decomposing the impact of domestic and global financial variables, the results of the GARCH model indicate that the exchange rate volatility is affected by Volatility index (VIX) and Credit default swaps (CDS). This result suggests that the exchange rate shocks experienced are mainly caused by global factors, therefore policymakers should focus on volatility spillovers caused by global financial markets. Secondly, detected positive maximal Lyapunov exponent shows that complexity in foreign exchange markets has been increased, market expectations lead to multiple-equilibria and diverging volatility eventually will generate recurrent spikes in currency value. These complementary findings have important implications for interventions of Central banks and preventing systemic risks, as well as portfolio and risk management. [ABSTRACT FROM AUTHOR]

The volatility spill-over effect of the commodity futures markets has been a matter of debate ever since the establishment of futures markets in India. The apprehensions regarding the price destabilizing role of futures trade called for overcautious regulatory supervision. The actions like suspension of contracts, bans on futures trade in certain commodities, and restrictions on trade volumes were very frequent; making the growth of the commodity futures market a chequered one. The Expert Committee appointed in 2007 to examine the effect of futures trade on commodity prices failed to make any categorical remark partly on account of the absence of long-term data. A commodity-specific analysis is much warranted as the price effect of the futures trade varies across the markets. The paper examined the volatility spill-over effect of the rubber futures market using a bivariate GARCH model with BEKK parameterization and found that there is no positive volatility spill-over from futures to spot. The weak linkage between futures trade and futures price variation further strengthens the finding. The absence of volatility spill-over from futures to spot in the case of rubber is due to the failure of the futures market to lead the spot market in pricing the commodity. [ABSTRACT FROM AUTHOR]

This research compares modeling and forecasting the volatility of the IHSG, N225, and BSESN30 capital market indices using the GARCH variation model against the GARCH-fractional cointegration variation. The data used is secondary data obtained from www.investing.com from 01/01/2012 to 04/30/2023. Based on the performance measurement using the sMAPE criterion, the best model for forecasting the period 05/01/2023 to 05/31/2023 is the std-ALLGARCH (1,2)-fractional cointegration model for IHSG, the std-ALLGARCH(1,1) model for N225, and the sstd-ALLGARCH (1,2) model for BSESN30. This empirical finding means that the Japanese and Indian capital markets affect the volatility of the Indonesian capital market. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to advance knowledge on the direct impact of the investor's protection level on the stock market volatility, that is, whether investor's protection is an important stock market volatility determinant. Design/methodology/approach: A panel data was estimated using a sample of 48 countries, from 2006 to 2018, totalizing 31,808 observations. To measure stock market volatility and the investor protection level, a generalized autoregressive conditional heteroskedasticity model and the World Bank Doing Business investor protection index were used, respectively. Findings: The results evidence that the protection of investors' rights reduces the stock market volatility. This result indicates that a high level of investor protection, which is the result of a better quality of laws and policies in place that protect investor's rights, promotes the country as a "safe haven." Practical implications: The relationship that the authors intend to analyze becomes important, given that investor protection will give outsiders guarantees on the materialization of their investments. This study contributes important knowledge for investors and for the establishment of government policies as a way of attracting investment. Originality/value: Although there have been a few studies addressing this relationship, to the knowledge, none of them directly analyses the influence of investor protection on the stock market volatility. [ABSTRACT FROM AUTHOR]

This paper conducts an extensive analysis of Bitcoin return series, with a primary focus on three volatility metrics: historical volatility (calculated as the sample standard deviation), forecasted volatility (derived from GARCH-type models), and implied volatility (computed from the emerging Bitcoin options market). These measures of volatility serve as indicators of market expectations for conditional volatility and are compared to elucidate their differences and similarities. The central finding of this study underscores a notably high expected level of volatility, both on a daily and annual basis, across all the methodologies employed. However, it's important to emphasize the potential challenges stemming from suboptimal liquidity in the Bitcoin options market. These liquidity constraints may lead to discrepancies in the computed values of implied volatility, particularly in scenarios involving extreme money or maturity. This analysis provides valuable insights into Bitcoin's volatility landscape, shedding light on the unique characteristics and dynamics of this cryptocurrency within the context of financial markets. [ABSTRACT FROM AUTHOR]

Exchange rate volatility has created many uncertainties and thus hampering bilateral trade flows among countries. These uncertainties are enormous and are affecting exporters’ and importers’ decisions regarding trade due to their risks and the resultant negative effect on profit in most countries. Indonesia and the United States were not an exception in this regard. Trade policies that are linked to uncertainties caused by exchange rate fluctuation are needed to stimulate trade among countries. To this end, this study analysed the effect of positive and negative changes in exchange rate volatility and the COVID-19 pandemic on the IndonesiaUS bilateral commodity trade flow. A time series of data comprising 30 commodity exports and imports at harmonized system 2-digit code were scrutinized for the period 2010:M01- 2021:M01. Our empirical strategies using the GARCH modelling approach, linear and nonlinear autoregressive distributing lag models revealed that: (i) linear ARDL exchange rate volatility asserts a positive effect on 9 commodity exports and 6 commodity imports from the US, (ii) in nonlinear ARDL 12 commodity exports and 8 commodity imports were positively affected by the exchange rate volatility. These findings indicate that exporters and importers tend not to care about the risk associated with exchange rate volatility, and (iii) the finding further revealed that in the export of commodities HS71, HS94, and HS95 there is evidence of asymmetric effect that differed in coefficient, sign, size, and duration. The practical significance of these findings is that policymakers should sidestep the strategy of a one-sided exchange rate policy to accommodate different risk behaviour as exhibited by exporters and importers. This would help maintain the stability of the exchange rate and promote trade flows. [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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper studies patterns of volatilities and their spillovers across six major segments of Indian financial market applying univariate GARCH model and estimating the Diebold and Yilmaz (DY) volatility spillover index. The study found increasing integration of financial market segments over time and that equity, bank index and corporate bond segments are net contributors while money, gsec and forex segments are net receivers of volatility. The study highlights the need for a healthy banking sector and the increasing significance of corporate bond market volatility and suggests segment specific policy responses to tackle financial market volatilities. [ABSTRACT FROM AUTHOR]

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

This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices. [ABSTRACT FROM AUTHOR]

This paper analyzed the price dynamics and volatility among tomato-producing (Solan and Ludhiana) and consuming (Delhi and Bangalore) markets using monthly price data from April 2005 to March 2023. The Cudda Della-Valle index and seasonality index were used to assess the price dynamics of the tomato market. Correlation analysis, Regression analysis, Vector Error Correction Model, and Granger causality were used to analyze the market integration. The GARCH model is used to measure the price volatility in the selected markets. Results showed that the maximum instability in tomato prices was in May (53 to 77 %), and the seasonality index showed that farmers received more than average prices between July and November. The significance of the Engle-Granger tau-statistic revealed that all the selected markets were well integrated into the long run. The speed of adjustment was highest in the Delhi market (56 %) followed by Bangalore (55 %) and Solan (27 %). The Bangalore market was the key market that influenced the price of all other markets by the Granger Causality test. Further, GARCH (1,1) model is used to estimate price volatility in the selected market. The price volatility was high in the Solan market and low in all the selected markets. [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.)

This 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. [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.)

This study examines the impact of the COVID-19 pandemic on stock market volatility for stock indices of a few MENA countries. The analysis consists of several elements: first, we assess the impact of this infectious disease on the standard deviations of stock market variables; second, we analyze the influence of the number of new daily cases and the number of new daily deaths on the standard deviations of these returns; and third, we assess the impact of the coronavirus on stock market volatility for these market variables using GARCH models. We find that the minimum value for most indices occurred in March 2020. Additionally, the results of the regression analysis reveal that the COVID-19 dummy variable, the number of daily new cases, and the number of new daily deaths have all had a significant negative impact on stock market volatility. Finally, the GARCH model reveals that the coefficients of COVID-19 endemic in the conditional variance equation had a significant positive impact on the conditional variance for stock indices, further indicating that the COVID-19 outbreak increased market volatility. [ABSTRACT FROM AUTHOR]

An oil-importing country is anxious about large oil imports. Importing countries must pay enormous foreign exchanges, which can suffer from the balance of payments. The long-term economic stability of countries like Pakistan and a rise in oil prices may be impacted. With changes in oil prices, oil importers in Pakistan see their economic strength fluctuating in developing economies. Many industries are connected with oil, and they are directly linked because petroleum is a prime production source. This study found that shocks in oil prices affected by variations in demand and risk oil prices affected by variations in demand and risk significantly contribute to changes in exchange rates. In contrast, shocks in supplies have almost no impact. The link between oil pricing shocks and exchange rates has increased significantly following the global financial crisis. The results depict that oil price shocks do not explain the change in exchange rate volatility, but this study documents a significant exchange-rate volatility connection. This research investigates how oil price shock aggressively impacts exchange-rate variations in Pakistan's economy. In this research, the method defined the connection between oil prices and currency rates. Data was collected from relevant websites from 1 January 2013 to 31 May 2021. Appropriate econometric techniques were used to achieve the research goal concerned. Finally, the research results helped the concerned stakeholders to adopt policies to balance oil shocks and changes in exchange rates in Pakistan. Subsidies on oil prices also affect the market and expenditure of the Government and tighten the deficit; therefore, oil prices must be marketed accordingly. [ABSTRACT FROM AUTHOR]

The metal markets have become extremely competitive and highly volatile due to financial globalisation. Therefore, in this study, the Value-at-Risk (VaR) and expected shortfall (ES) are estimated for the metal markets. A two-stage dynamic extreme value theory (EVT) method has been adopted along with the GARCH (1, 1) model to identify the pre-specified threshold for the metal market by using high-frequency returns data of 15-minute intervals ranging from 1st January, 2018 to 24th September, 2021, which provides accurate information about metal market volatility and tail distribution. Moreover, the empirical findings confirm the presence of a high level of volatility persistence in the metal market, especially in the financial returns of gold. Furthermore, silver metal returns exhibit the highest VaR compared to other metals in the market. The empirical results could assist financial investors and portfolio managers to minimise and control the potential risk in the market. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico 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.)

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. [ABSTRACT FROM AUTHOR]

Across the globe, COVID-19 has disrupted the financial markets, making them more volatile. Thus, this paper examines the market volatility and asymmetric behavior of Bitcoin, EUR, S&P 500 index, Gold, Crude Oil, and Sugar during the COVID-19 pandemic. We applied the GARCH (1, 1), GJR-GARCH (1, 1), and EGARCH (1, 1) econometric models on the daily time series returns data ranging from 27 November 2018 to 15 June 2021. The empirical findings show a high level of volatility persistence in all the financial markets during the COVID-19 pandemic. Moreover, the Crude Oil and S&P 500 index shows significant positive asymmetric behavior during the pandemic. Apart from this, the results also reveal that EGARCH is the most appropriate model to capture the volatilities of the financial markets before the COVID-19 pandemic, whereas during the COVID-19 period and for the whole period, each GARCH family evenly models the volatile behavior of the six financial markets. This study provides financial investors and policymakers with useful insight into adopting effective strategies for constructing portfolios during crises in the future. [ABSTRACT FROM AUTHOR]

In a first of its kind, this paper discovers the long-run relationship between investor sentiment and Indian stock market volatility over the period 2010 to 2021, using monthly data. Twenty-two variables have been identified and used as proxy for investor sentiment. Then, using principal component analysis, the first 11 principal components with an eigen value of more than one have been selected and used as sentiment sub-indices, which represent the sentiments of Indian investors. The volatility of the S&P BSE 500 index has been measured using the GARCH model. We have applied the auto-regressive distributed lag (ARDL) model to document the relationship between sentiment and volatility. Further analysis has been done using ARDL modelling, by taking sentiment sub-indices as independent variables and the S&P BSE 500 Index GARCH volatility as the dependent variable. The value of was found to be 0.752. The results show that most of the sentiment sub-indices have a negative impact on the Indian stock market volatility in the long run. Thus, it can be concluded that in the long run, when sentiment is positive, volatility is negative, and vice versa. This study would be a valuable addition to the existing body of literature on the subject, besides being useful to regulators, policy makers, and investors. Regulators and policy makers should watch out for the impact of fluctuations in selected sentiment sub-indices on volatility in the stock market. Investors can search for arbitrage opportunities in the market on the basis of selected indices. [ABSTRACT FROM AUTHOR]

Electricity markets are becoming increasingly interconnected, in part, to deal with unpredictability and imbalances in electricity prices. This paper assesses how electricity flows and generation from wind and solar photovoltaic (PV) power impact the volatility and mean of day-ahead electricity prices in Spain. It finds there is a merit order effect from wind and solar PV power at most times of the day, which varies in magnitude for each of the 24 h. Electricity production from wind power is also found to play a clear role in increasing price volatility at all hours. Electricity inflow is shown to decrease both the mean and volatility of the electricity prices. The volatility of electricity prices is revealed to be highly persistent, and thus prone to large transmission shocks. This volatility is also susceptible to new shocks, which may indicate a tendency by market participants to overreact to unexpected shocks in electricity prices, possibly due to the relatively small size of the Iberian electricity market. [ABSTRACT FROM AUTHOR]

During the recent COVID-19 outbreak, the crude oil market experienced enormous price fluctuations. A large number of researchers contended the volatility observed in oil market as unprecedented and it was immediately attributed to the pandemic owing to its globally devastating nature. Whether or not this attribution is justified, is the major question we have raised in this paper. We perform the comparative analysis of the volatility spasms of oil market during the COVID-19 pandemic (COVID-19), the Global financial crisis of 2008 (GFC) and the SARS outbreak of 2002–2004 (SARS). Preliminary investigation is conducted using two proxies of market sentiment which are oil price returns and oil price spread. For further investigations we apply symmetric GARCH (1,1) and the asymmetric GJR-GARCH (1,1) models. Our results based on skewness and kurtosis, indicate an extremely high degree of fat tail risk implying COVID-19 crisis as low probability yet high severity event a.k.a. black swan event. Our results further confirm the presence of volatility clustering (GARCH effect) along with the highest degree of asymmetry during COVID-19. These facts collectively make COVID-19 crisis more uncertain and pessimistic compared to the GFC and SARS. [ABSTRACT FROM AUTHOR]

In this paper, we analyse prominent calendar anomalies for the Egyptian stock market. We also evaluate if there is any volatility clustering in the sample market and the reaction of volatility to positive and negative shocks (news) in the Egyptian stock market. The closing prices of ten indices of the Egyptian Stock Exchange have been examined over the period 2012-2019. The calendar anomalies pertaining to the day-of-the-week effect, Halloween effect, trading-month effect, and month-of-the-year effect have been analysed. Dummy variable regression technique is used to test the calendar anomalies. Further, the GARCH family of models, including GARCH-M and T-GARCH techniques have been utilised to test for the nature of volatility clustering. The results validate that day-of-the-week anomaly is strongly observed in the data. However, the other anomalies have not been observed. The results also confirm the presence of volatility clustering. The results finally show that negative shocks result in more volatility clustering than positive shocks. There has been little research pertaining to understanding the nature of volatility clustering for the Egyptian markets. Thus, to enrich the literature for the emerging markets and contribute to the area, we conduct a comprehensive study on calendar anomalies for the Egyptian market. [ABSTRACT FROM AUTHOR]

The 2008 financial crisis has highlighted the lack of precision in the market risk metrics that financial institutions must report to the regulator. The use of Machine Learning techniques in stock markets and the treasury (Front–Back Office) of financial institutions is a key tool for optimizing its own resources, internal processes and risk measures. We propose a hybrid methodology to better capture the volatility of market risk factors with Value-at-risk models in periods of stress, but also in periods of stability compared to traditional metrics. This hybrid model uses different types of artificial neural networks and traditional metrics to perform the optimal forecast of volatility applied to the main market risk nodes of the Spanish stock market. We use data from the following main market risk factors: returns on Santander Bank shares, the Spanish Stock Market Index, Euro/Dollar exchange rates and the index that measures the total return performance of a funded long credit position in the on-the-run iTraxx Crossover 5-Year-Index. Our contribution is a hybrid model that combines correct sequential pattern learnings with an improved prediction performance in the volatility of market risk factors. Our findings show that the Support Vector Machine and the Long-Short-Term Memory Model present better prediction results in all factors in the stability periods. Therefore, the proposed method is promising for application in risk management systems. • Financial institutions need to optimize volatility forecasts. • Volatility forecasts are needed to efficiently estimate market risk metrics. • We propose a hybrid methodology to capture the volatility of market risk factors. • Our hybrid model combines artificial neural networks and traditional metrics. • Our model improves prediction performance in the volatility of market risk factors. [ABSTRACT FROM AUTHOR]

The financial market is a complex dynamic system full of noise. Volatility is an important factor in portfolio allocation and risk management. The rapid development of artificial intelligence makes it possible to predict the volatility of the financial market more accurately. In this paper, we attempt to model the volatility of specific risk for stocks. We use GARCH as a baseline model and explore the effectiveness of LSTM. We propose to pack the specific risk factors of all stocks at each timestamp and feed to the LSTM model, so that the implicit connections among stocks will be modeled. From the empirical experiments on BARRA official data, we observe that the performances of LSTM are obviously better than that of GARCH, in terms of both MAE and MSE. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that ignoring structural breaks in volatility in financial asset returns can result in overestimation of volatility spillover among markets. This paper examines volatility spillover among major US equity sectors (i.e. Financial, Technology, Energy, Health, Consumer and Industrial) with bivariate GARCH models utilizing daily data from April 2006 to March 2021 after adjusting for volatility breaks. I find significantly less volatility spillover between sector returns after adjusting for detected volatility breaks into a bivariate GARCH model. I also show that after adding volatility breaks into a model the estimated hedge ratios change significantly and show considerably less variability over time, which can result in substantial savings in portfolio rebalancing costs. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Purpose: The purpose of this study is to empirically examine the impact of the novel coronavirus (COVID-19) on Egyptian stock market returns and volatility between July 2018 and June 2021. Design/methodology/approach: This study utilizes a generalized autoregressive conditional heteroskedasticity (GARCH) model to examine the impact of COVID-19 on two basic stock market indices (EGX30 and EGX100). In addition, the heteroskedasticity corrected model (HCM) was employed to differentiate between the effects of each subsequent wave of the pandemic. Findings: The results of the GARCH model revealed that all COVID-19 variables have a significant impact on the daily returns of EGX100, but an insignificant impact on that of EGX30. The mortality rate and transmission speed increased the market volatility of EGX30 daily returns. The results of the HCM confirmed that the Egyptian stock market reacted more nervously to the first wave than to the second, while the impact was not detected in the third wave. Practical implications: This study provides useful insights to investors and policymakers in handling the negative influence of unanticipated events. To retain economic stability, the Egyptian government can impose fiscal stimuli and consider policies to combat the impact of the pandemic. Originality/value: This study is one of the first attempts to differentiate between the effects of subsequent waves of the pandemic on the stock market in Egypt, one of the largest economies in Africa. [ABSTRACT FROM AUTHOR]

This study attempts to associate the relationship between the volatile oil price and its impact on the stock market prices. Oil prices are increasing in the recent days and it is important to check whether this has any influence over the Indian economy and that of movements of stocks. This study uses the GARCH model to assist changes where the volatility is time dependent and volatility is subject to increase and decrease in the same series. The major objective of the paper is to use GARCH model to estimate the volatility of the daily returns with respect to the oil price of National Stock Exchange. This models was done by using the data from March 2016 to February 2021. [ABSTRACT FROM AUTHOR]

Brazil's rise as a global powerhouse producer of soybeans and corn over the past 15 years has fundamentally changed global markets in these commodities. Brazil's rise is arguably due to the development of varieties of soybean and corn adapted to climates within Brazil, allowing farmers to double‐crop corn after soybeans in the same year. Corn and soybean market participants increasingly look to Brazil for fundamental price information, and studies have shown that the two markets have become cointegrated. However little is known about how much volatility from each market spills over to the other. In this article, we measure volatility spillover ratios between U.S. and Brazilian first crop corn, second‐crop corn, and soybeans. We find that linkages between the two countries increased after double‐cropping corn after soybeans expanded, volatility spillover magnitudes expanded, and the direction of volatility spillovers flipped from U.S. volatility spilling over to Brazil before double cropping, to Brazil spilling over to U.S. after double cropping. [ABSTRACT FROM AUTHOR]

For economists and investors, it is necessary to understand the random and nonlinear pattern of the stock market volatility. High volatility directly affects the financial market that leads to unpredictability. China–Pakistan Economic Corridor attracts economists and investors worldwide. Therefore, predicting the volatility of the stock markets related to CPEC is important. In this study we consider the most important stock markets lying on the route of CPEC, namely KSE 100 (Pakistan), SSE 100 (China), TADAWUL (Kingdom of Saudi Arabia), KASE (Kazakhstan), KLSE (Malaysia), BIST (Turkey), MOEX (Russia), FTSE (United Kingdom) and CAC40 (France). The daily returns of stock market indices consist of 1706 observations from December 2014 to July 2021. After the confirmation from the ARCH effect test, family GARCH models are employed, among them, based on AIC and BIC criteria, GARCH (1,1), EGARCH (1,1), and GARCH-M (1,1) are found suitable to forecast the volatility. The empirical study also suggests that the out-of-sample forecast GARCH-M (1,1) model is more appropriate as it has a minimum value of MAE, MSE, RMSE, MAPE, TheilU1, and Theil U2 among all the studied GARCH models. Furthermore, it is also found that the KSE-100 and SSE-100 have moderate and slow market average returns even though both stock markets are found to be the least risk-returns markets. [ABSTRACT FROM AUTHOR]

Recently, to account for low‐frequency market dynamics, several volatility models, employing high‐frequency financial data, have been developed. However, in financial markets, we often observe that financial volatility processes depend on economic states, so they have a state heterogeneous structure. In this article, to study state heterogeneous market dynamics based on high‐frequency data, we introduce a novel volatility model based on a continuous Itô diffusion process whose intraday instantaneous volatility process evolves depending on the exogenous state variable, as well as its integrated volatility. We call it the state heterogeneous GARCH‐Itô (SG‐Itô) model. We suggest a quasi‐likelihood estimation procedure with the realized volatility proxy and establish its asymptotic behaviors. Moreover, to test the low‐frequency state heterogeneity, we develop a Wald test‐type hypothesis testing procedure. The results of empirical studies suggest the existence of leverage, investor attention, market illiquidity, stock market comovement, and post‐holiday effect in S&P 500 index volatility. [ABSTRACT FROM AUTHOR]

Purpose: The paper tests the overconfidence bias and volatility on the Ghana Stock Exchange (GSE) during the pre-Covid-19 pandemic and Covid-19 pandemic period. Design/methodology/approach: The study employs pairwise Granger causality to test the presence of overconfidence bias on the Ghana stock market as well as GARCH (1,1) and GJR-GARCH (1, 1) models to understand whether overconfidence bias contributed to volatility during pre-Covid-19 pandemic and Covid-19 pandemic period. The pre-Covid-19 pandemic period spans from January, 2019 to December, 2019, and Covid-19 pandemic period spans from January, 2020 to December, 2020. Findings: The paper finds a unidirectional Granger causality running from weekly market returns to weekly trading volume during the Covid-19 pandemic period. These results indicate the presence of overconfidence bias on the Ghana stock market during the Covid-19 pandemic period. Finally, the conditional variance estimation results showed that excessive trading of overconfident market players significantly contributes to the weekly volatility observed during the Covid-19 pandemic period. Research limitations/implications: The empirical findings demonstrate that market participants on the GSE exhibit conditional irrationality in their investment decisions during the Covid-19 pandemic period. This implies investors overreact to private information and underreact to available public information and as a result become overconfident in their investment decisions. Practical implications: Findings from this paper show that there is evidence of overconfidence bias among market players on the GSE. Therefore, investors, financial advisors and other market players should be educated on overconfidence bias and its negative effect on their investment decisions so as to minimize it, especially during the pandemic period. Originality/value: This study is a maiden one that underscores investors' overconfidence bias in the wake of a pandemic in the Ghanaian stock market. It is a precursor to the overconfidence bias discourse and encourages the testing of other behavioral biases aside what is understudied during the Covid-19 pandemic period in Ghana. [ABSTRACT FROM AUTHOR]

Research background: Volatility in agricultural prices is a concern for producers and other stakeholders along the food chain for a developing country like India where a significant percentage of population is directly dependent on agriculture for their livelihoods. Price volatility can have a long run impact on the income of producers as it makes it difficult to plan production for future. It affects economically weaker consumers who spend a major portion of their income on food items. Therefore, it is essential to study the determinants of volatility of agricultural commodities. Purpose of the article: The objective of the study is to identify the determinants of volatility in the futures market traded at NCDEX. Methods: Eight agricultural commodities which are traded at NCDEX is selected for the study based on trading volume and open interest. The volatility in the futures prices of the selected commodities were tested for presence heteroscedasticity. The futures price volatility was first estimated using conditional heteroscedasticity models and then regressed with identified variables like time to maturity, volume, open interest, and past volatility. Findings & Value added: From the results it was observed that for five commodities the relationship between time to maturity and volatility is negative and significant. This means as the contract nears maturity, volatility increases which support the presence of Samuelson hypothesis. Further it was found that there is positive and significant relationship between volume and volatility for all the commodities supporting the mixed distribution hypothesis. The relation between open interest and volatility was negative and significant for all the commodities except for soya oil and turmeric. Past volatility has positive and significant relationship with present day volatility that means if the past volatility is high then it is expected that present volatility will also be high. The findings support in risk management of agricultural commodities. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

The study aims to identify the dependence between MENA Sukuk, GCC Sukuk, and Nifty 50 Shariah indices. Further, it finds out volatility patterns among the same indices before and during the COVID-19 pandemic. Daily data (April 2017 to November 2020) of the indices are analyzed using the GARCH model and C-Vine Copula approach. It is done by modelling the returns of the indices. The outcomes may provide a prodigious advantage for portfolio makers and stockholders towards investment policies throughout financial catastrophes like the COVID19 pandemic. In the pre-Covid period- C Vine copula shows that the Sukuk have strong dependence while the Shariah index has rather weak dependence, for MENA Sukuk and Nifty Shariah 50 it increased by a great margin in during- COVID 19 periods. GCC Sukuk and Nifty Shariah indices are positively correlated to each other in comparison to the returns of Mena Sukuk. The results of the GARCH model show asymmetrical co movements for losses and gains. Moreover, conditioned on MENA Sukuk, the GCC Sukuk and Nifty Shariah 50 had a higher negative degree of dependence within and throughout the COVID-19 period. The findings show substantial high and low tail dependency among the Sukuk and Shariah markets before Covid19. GCC Sukuk and Nifty Shariah 50 indices are highly positively correlated to each other in returns of MENA Sukuk. We also find negative dependency among the GCC Sukuk and Nifty Shariah 50 during Covid19 with MENA Sukuk. The outcomes are varied due to the time variation copulas that shows dependency diverges over time for all variables. Furthermore, this study would be helpful to find out the significance of ethical finance functions safe havens for world investors by using the copula model. [ABSTRACT FROM AUTHOR]

The present study examines at modelling and forecasting the volatility of the Nifty, Sensex, Nifty Bank index, and five banking stocks in the Indian stock market, including HDFC Bank, ICICI Bank, Axis Bank, Kotak Bank, and SBI Bank, using regular data from January 26th, 2019 to January 25th, 2021, spanning both the period before and after the COVID-19 pandemic. The first positive case in India was recorded on January 27, 2020. The Nifty, Sensex, Nifty Bank index and five banking stocks: HDFC Bank, ICICI Bank, Axis Bank, Kotak Bank, and SBI Bank return volatility for the Indian stock market are investigated using GARCH, EGARCH, and TGARCH models. The aim of this study is to look at the Indian stock market's volatility characteristics, such as clustering volatility, leverage impact, and risk premium. Positive and negative shocks have different effects on the volatility of the Nifty, Sensex, Nifty bank index, and five bank stock returns in the TGRACH(1,1) model, which means that negative news causes more volatility than positive news. Hence, the effect of news on the Indian stock market during the Covid-19 pandemic is asymmetric. [ABSTRACT FROM AUTHOR]

Investigating the impact of volatility spillover among various markets has been the subject matter of numerous research. This study investigates the dynamic relationship between the Bombay Stock Exchange index (SENSEX) and the small and medium enterprises (SME) stock index (BSE SME) in India. The study uses univariate autoregressive conditional heteroskedasticity (ARCH)/generalised autoregressive conditional heteroskedasticity (GARCH) models to model the time-varying volatility of the BSE SME market and multivariate BEKK-GARCH analysis to model the volatility of the SENSEX and BSE SME Index considering the existence of some linkages between them. The study is based on the daily stock indices data ranging from 16 August 2012 to 31 March 2021. Furthermore, the study reveals statistically significant internal volatility spillovers in the SME stock market and the cross-volatility transmission between the two indices. It affirms statistically significant volatility and return spillover between the main market index, SENSEX and SME index, BSE SME. The findings of this research have important implications for the diversification process. It provides crucial signals to investors, portfolio managers and policymakers, especially when there has been much impetus and promotion from the Indian government and emerging foreign investments in India's SMEs in recent years. [ABSTRACT FROM AUTHOR]

Purpose: Several empirical studies have proven that emerging countries are attractive destinations for Foreign Institutional Investors (FIIs) because of high expected returns, weak market efficiency and high growth that make them attractive destination for diversification of funds. But higher expected returns come coupled with high risk arising from political and economic instability. This study aims to compare the linear (symmetric) and non-linear (asymmetric) Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models in forecasting the volatility of top five major emerging countries among E7, that is, China, India, Indonesia, Brazil and Mexico. Design/methodology/approach: The volatility of financial markets of five major emerging countries has been empirically investigated for a period of two decades from January 2000 to December 2019 using univariate volatility models including GARCH 1, 1, Exponential Generalized Autoregressive Conditional Heteroscedasticity (E-GARCH 1, 1) and Threshold Generalized Autoregressive Conditional Heteroscedasticity (T-GARCH-1, 1) models. Further, to examine time-varying volatility, the distinctions of structural break have been captured in view of the global financial crisis of 2008. Thus, the period under the study has been segregated into pre- and post-crisis, that is, January 2001–December 2008 and January 2009–December 2019, respectively. Findings: The findings indicate that GARCH (1, 1) model is superior to non-linear GARCH models for forecasting volatility because the effect of leverage is insignificant. China has been considered as most volatile, whereas India is volatile but positively skewed and Indonesia is the least volatile country. The results can help investors in better international diversification of their portfolio and identifying best suitable hedging opportunities. Practical implications: This study can help investors to construct a more risk-adjusted returns international portfolio. Further, it adds to the scant literature available on the inconclusive debate on the choice of linear versus non-linear models to forecast market volatility. Originality/value: Earlier studies related to univariate volatility models are mostly applications of the models. Only few studies have considered the robustness while applying the models. However, none of the studies to the best of the authors' searches have considered these models for identifying the diversification opportunity among the emerging countries. Hence, this study is able to derive diversification and hedging opportunities by applying wide ranges of the statistical applications and models, that is, descriptive, correlations and univariate volatility models. It makes the study more rigorous and unique compared to the previous literature. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic has caused significant losses and an increase in the level of risk in the financial markets and global economy. Thus in this study, we model the crypto market volatility behavior during the COVID-19 crisis. GARCH (1, 1) and GJR-GARCH (1, 1) were applied to model the volatility clustering and leverage effects in the intraday day (15-minute interval) returns of Bitcoin, Ethereum, and Litcoin ranging from 11th April 2019 to 8th February 2021. The empirical findings from GARCH (1, 1) model indicates the presence of volatility clustering in the crypto market. Moreover, the results of the GJR-GARCH (1, 1) indicate the presence of leverage effects in the financial returns series of all three crypto currencies. Furthermore, the excess kurtosis confirms the existence of fat-tail phenomena in the crypto market. Overall, the findings from this study showed that in times of COVID-19 pandemic the crypto market returns series showed volatility persistence, fat-tail phenomena, and leverage effects. These outcomes provide a better understanding for financial investors to invest rationally and cautiously during pandemic times. [ABSTRACT FROM AUTHOR]