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Magner Pulgar, Nicolás, Esteban José Antonio Terán Sánchez, and Vicente Alfonso Guzmán Muñoz. "Stock Market Synchronization and Stock Volatility: The Case of an Emerging Market." Revista Mexicana de Economía y Finanzas 17, no. 3 (May 24, 2022): 1–22. http://dx.doi.org/10.21919/remef.v17i3.747.
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The purpose of this paper is to study the effect of stock market synchronization on the volatility of its component assets. For this objective, we calculate the stock market's synchronization using the Minimum Spanning Tree Length (MSTL) network analysis method. Then, we implement forecasting tests in and out the sample to assess the forecasting power on the stock market's synchronization to predict the individual stock realized volatility. Additionally, we test a VAR and a forecast error variance decomposition analysis to study Granger causality's presence on volatility. Our results show that synchronization within a market exists and changes over time. Our main results show that an increase in synchronization causes an increase in financial assets' realized volatility in the following month. Our results made it possible to study financial markets' synchronization and take a systemic risk approach to improve investment management. Our main idea was that the stock markets' synchronization positively correlates with financial assets' volatility. The greater the synchronization, the greater the volatility in the following period. This study offers a new approach to study the stock market volatility.
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Acuña, Andrés, and Cristián Pinto. "Eficiencia del mercado accionario Chileno: un enfoque dinámico usando test de volatilidad." Lecturas de Economía, no. 70 (September 11, 2009): 39–61. http://dx.doi.org/10.17533/udea.le.n70a2254.
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En este artículo estudiamos la eficiencia del Mercado Accionario Chileno (MAC). Para su comprobación usamos un modelo de equilibrio parcial que representa la manera como se forma el precio de los activos financieros. Contrastamos la volatilidad observada en los precios de las acciones y la volatilidad esperada en un modelo de mercado accionario eficiente. El análisis estadístico comprende datos de frecuencia mensual de títulos transados en la Bolsa de Comercio de Santiago de Chile en el periodo 1987-2007. Utilizando tests de volatilidad, encontramos evidencia de exceso de volatilidad en los precios del mercado accionario chileno; no podemos vincular el exceso de volatilidad a la existencia de una burbuja especulativa racional, y tampoco a un exceso de volatilidad en la tasa de descuento. Palabras clave: eficiencia, mercado accionario, valoración de activos, CAPM. Clasificación JEL: D53, G14 Abstract: This article studies the Chilean Stock Market's efficiency. To corroborate efficiency, we use a partial equilibrium model for financial asset pricing. We contrast between observed and expected Chilean stock price volatility under an efficient stock market framework. For the statistical analysis, we use monthly data for Chilean Stock Market prices from 1987 to 2007. Performing volatility tests, we find evidence of excess volatility in Chilean stock market prices. We cannot link this stock price excess volatility to the existence of a rational speculative bubble, nor to discount rate's excess volatility. Keywords: efficiency, stock market, asset pricing, CAPM. JEL Classification: D53, G14 Résumé: Dans cet article nous étudions l'efficience du marché actionnaire chilien (MAC). Pour ce faire, nous utilisons un modèle d'équilibre partiel qui représente la manière dont le prix des actifs financiers est déterminé. Nous contrastons la volatilité observée dans les prix des actions et la volatilité attendue à l'intérieur d'un modèle de marché actionnaire efficient. L'analyse statistique comprend un ensemble de données de fréquence mensuelle des titres échangés à la Bourse de Commerce de Santiago du Chili pour la période 1987-2007. En utilisant des tests de volatilité, nous montrons qu'il existe un excès de volatilité dans les prix du marché actionnaire chilien; sans qu'il soit posible lier cet excès de volatilité ni à l'existence d'une bulle spéculative rationnelle, ni au taux d'escompte. Mots clé: efficience, marché actionnaire, évaluation d'actifs, CAPM. Classification JEL : D53, G14
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Ogbulu, Onyemachi Maxwell. "Oil Price Volatility, Exchange Rate Movements and Stock Market Reaction: The Nigerian Experience (1985-2017)." American Finance & Banking Review 3, no. 1 (November 12, 2018): 12–25. http://dx.doi.org/10.46281/amfbr.v3i1.200.
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Given the observed volatility in crude oil prices in the international oil market and the role which oil and gas play in the Nigerian economy, this paper is an attempt to investigate the impact of crude oil prices and foreign exchange rate movements on stock market prices in Nigeria. In addition, the paper examined whether there is any volatility pass-through between the dollar price of Nigerian crude oil, foreign exchange rate of the Naira and stock market prices respectively. Data employed for the study are monthly values of the Nigerian Stock Exchange (NSE) All-Share Index (ASI), Dollar price of Nigerian Crude Oil (DPO) and the Official Exchange Rate of the Naira to the US Dollar (FXR) from January, 1985 to August, 2017. The methodology adopted for the study include the ADF unit root tests, Johansen co-integration tests, the ECM technique, Granger causality tests, variance decomposition as well as the GARCH(1,1) to model the volatility relationships among the variables. Findings reveal that there is one long-run dynamic co-integrating relationship among the variables ASI, DPO and FXR while the ECM results indicate that Crude oil price (DPO) significantly impact on Stock market prices. The Granger causality test reports a bi-directional causality relationship between ASI and DPO and a unidirectional causality running from FXR to ASI. The ARCH-GARCH volatility analysis demonstrates vividly that stock market prices in the NSE exhibit ARCH effect with a significant and positive first order ARCH term. The GARCH term is also positive and significant indicating that previous month’s stock market price volatility significantly influences current stock market volatility in the NSE. In addition, findings show that the volatility of dollar price of Nigerian oil (DPO) in the world oil market is significantly transmitted to the volatility of stock market prices in Nigeria. The pass-through effect of the volatility of exchange rate (FXR) to the volatility of stock market prices is also positive and significant. These findings offer significant informational signal to policy makers, portfolio managers/advisors and the investing public in achieving optimal asset and portfolio profile.
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Mecagni, Mauro, and Maged Sawky Sourial. "The Egyptian Stock Market: Efficiency Tests and Volatility Effects." IMF Working Papers 99, no. 48 (1999): 1. http://dx.doi.org/10.5089/9781451846720.001.
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Nguyen, Hien Thu, and Nghi Dinh Le. "TESTING THE GARCH MODEL IN THE VIETNAMESE STOCK MARKET." Science and Technology Development Journal 13, no. 4 (December 30, 2010): 5–14. http://dx.doi.org/10.32508/stdj.v13i4.2182.
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An important factor of interest of investors on stock markets is investment risk. Risk can undergo a quantitative process through volatility, be measured by conditional variance of stock returns. GARCH is an effective and popularly used model for volatility effect on stock returns. This study tests the GARCH model and analyzes other aspects of volatility on stock returns on the two stock markets of Vietnam. In addition, the study provides evidence of the existence of GARCH effect on Vietnamese stock markets. Besides, the study also assesses price margin policy, trading volume and leverage effects on volatility of stock returns.
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Bhuva, Krunal K., and Vijay H. Vyas. "Expiry day Impact on return on Indian Stock market (NSE)- an Empirical Study." Journal of Management and Science 1, no. 3 (December 30, 2013): 402–9. http://dx.doi.org/10.26524/jms.2013.45.
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Derivative products are alleged to have a sharp affect on the stock market in various ways ever since their inception in June 2000. Currently, derivative trading constitutes approximately 90% of the total turnover of the NSE (National Stock Exchange). Launching of derivatives and their expiration (last Thursday of every month) in the Indian stock market has been perceived to have direct corollary on the return, volatility, efficiency and marketability of the stock market. This paper tries to analyze empirically the expiration day effect of stock derivatives on underlying securities. This study tests the presence of the last Thursday of the montheffect on stock market volatility by using the S&P 500 market index during the period of January 2012 and December 2012 and sample companies which are trading on derivative market. The findings show that the last Thursday of the month effect on stock market volatility is not present in volatility and return equations.
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Leblang, David, and Bumba Mukherjee. "Presidential Elections and the Stock Market: Comparing Markov-Switching and Fractionally Integrated GARCH Models of Volatility." Political Analysis 12, no. 3 (2004): 296–322. http://dx.doi.org/10.1093/pan/mph020.
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Existing research on electoral politics and financial markets predicts that when investors expect left parties—Democrats (US), Labor (UK)—to win elections, market volatility increases. In addition, current econometric research on stock market volatility suggests that Markov-switching models provide more accurate volatility forecasts and fit stock price volatility data better than linear or nonlinear GARCH (generalized autoregressive conditional heteroskedasticity) models. Contrary to the existing literature, we argue here that when traders anticipate that the Democratic candidate will win the presidential election, stock market volatility decreases. Using two data sets from the 2000 U.S. presidential election, we test our claim by estimating several GARCH, exponential GARCH (EGARCH), fractionally integrated exponential GARCH (FIEGARCH), and Markov-switching models. We also conduct extensive forecasting tests—including RMSE and MAE statistics as well as realized volatility regressions—to evaluate these competing statistical models. Results from forecasting tests show, in contrast to prevailing claims, that GARCH and EGARCH models provide substantially more accurate forecasts than the Markov-switching models. Estimates from all the statistical models support our key prediction that stock market volatility decreases when traders anticipate a Democratic victory.
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Wanyama, Dr David W. "EFFECT OF STOCK MARKET VOLATILITY ON THE GROWTH OF CORPORATE BOND MARKET IN KENYA." International Journal of Finance 2, no. 2 (February 5, 2017): 76. http://dx.doi.org/10.47941/ijf.57.
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Purpose: The purpose of this study was to analyze how stock market volatility influences the growth of corporate bond market in Kenya.Methodology: The study used descriptive and causal research designs. Secondary data was used. The sample of the study consisted of daily and monthly time series covering six years beginning January 2009 to December 2014. Unit root tests using Augmented Dickey-Fuller (ADF) and Phillips-Perron tests were done. The study used Eviews econometric software to facilitate empirical analysis of data.Results: Regression of coefficients results shows that stock market volatility and corporate bonds are positively and significant related (r=0.000023, p=0.0001).Unique Contribution to Theory, Practice and Policy: The study recommended that Policy makers should be aware of and monitor the level of stock market volatility that is appropriate for promoting the growth of the corporate bond markets and indeed other financial markets.
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GIL-ALANA, LUIS A. "FRACTIONAL INTEGRATION IN THE STOCK MARKET VOLATILITY SERIES." International Journal of Theoretical and Applied Finance 05, no. 08 (December 2002): 775–83. http://dx.doi.org/10.1142/s0219024902001663.
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In this article we model the stock market volatility in the US, the UK, France, Germany and Japan by means of using fractionally integrated techniques. The results, based on the tests of Robinson [24] show that the volatility series can be well described in terms of I(d) statistical processes, with d higher than 0.5 but smaller than 1, implying thus nonstationary but mean-reverting behaviour.
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Badshah, Koerniadi, and Kolari. "Testing the Information-Based Trading Hypothesis in the Option Market: Evidence from Share Repurchases." Journal of Risk and Financial Management 12, no. 4 (November 29, 2019): 179. http://dx.doi.org/10.3390/jrfm12040179.
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The informed options trading hypothesis posits that option prices lead stock prices. In this paper, we extended the research on this hypothesis to open-market share repurchases. Empirical tests showed that the implied volatility spread was not significantly related to buy-and-hold abnormal stock returns. However, further evidence reveal a significant relationship between implied volatility spread and subsequent stock return volatility around open-market share repurchase events. We concluded that option traders have private information on the volatility of stock returns and superior information processing ability that accounts for prescient pricing behavior in options relative to stocks.
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Demirer, Riza, Rangan Gupta, Zhihui Lv, and Wing-Keung Wong. "Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests." Sustainability 11, no. 2 (January 11, 2019): 351. http://dx.doi.org/10.3390/su11020351.
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We employ bivariate and multivariate nonlinear causality tests to document causality from equity return dispersion to stock market volatility and excess returns, even after controlling for the state of the economy. Expansionary (contractionary) market states are associated with a low (high) level of equity return dispersion, indicating asymmetries in the relationship between return dispersion and economic conditions. Our findings indicate that both return dispersion and business conditions are valid joint forecasters of stock market volatility and excess returns and that return dispersion possesses incremental information regarding future stock return dynamics beyond that which can be explained by the state of the economy.
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Hoshi, Takeo. "Stock market rationality and price volatility: Tests using Japanese data." Journal of the Japanese and International Economies 1, no. 4 (December 1987): 441–62. http://dx.doi.org/10.1016/0889-1583(87)90009-8.
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Ayadi, O. Felix, Lloyd P. Blenman, and C. Pat Obi. "Stock Return Characteristics In A Thin Incipient Stock Market." Journal of Applied Business Research (JABR) 14, no. 3 (August 31, 2011): 113. http://dx.doi.org/10.19030/jabr.v14i3.5709.
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<span>This paper examines the distributional properties of stock returns in the Nigerian stock market. Because emerging stock markets present several institutional, political and economic barriers, we hypothesize that the structural adjustment program begun in 1986 resulted in a sustained increase in the variability of stock returns. Conventional variance homogeneity tests could not reject the hypothesis of changing volatility in the security returns process. However, the Lagrange multiplier test reveals the presence of autoregressive conditional heteroscedasticity (ARCH) effect in the stock returns.</span>
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Sarfraz, Sania, Mumtaz Ahmad, and Muhammad Husnain. "Long Run and Short Run Co movement among Oil Prices and Stock Market Liquidity: Evidence from the Emerging Equity Market of Pakistan." Sustainable Business and Society in Emerging Economies 3, no. 3 (September 30, 2021): 231–41. http://dx.doi.org/10.26710/sbsee.v3i3.1913.
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Abstract: The objective of this study is to examine the long run and short run relationship between oil prices and stock market liquidity in Pakistan stock exchange.
Design/Methodology/Approach: The sample spans 10 years from 2010 to 2019. We use auto-regressive distributed lag (ARDL) to examine long-term and short-term relationships between oil prices, exchange rate, stock market index, market volatility and inflation and stock market liquidity. We use normality checks, serial correlation tests, heteroscedasticity tests, and CUSM models to assess model stability.
Findings: Result shows that there exist a long-term negative association between exchange rate and inflation, but a positive relationship is revealed between oil prices, stock returns, and market volatility. These conclusions hold for three sectors i.e. automobile, cement and sugar.
Implications/Originality/Value: This study extends the existing debate on the relationship between macroeconomic variables and stock market liquidity to the emerging equity market. For this, it uses three proxies for stock market liquidity: Amihud liquidity, average trading volume, and trading volume average.
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Kaur, Harvinder. "Time Varying Volatility in the Indian Stock Market." Vikalpa: The Journal for Decision Makers 29, no. 4 (October 2004): 25–42. http://dx.doi.org/10.1177/0256090920040403.
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This paper investigates the nature and characteristics of stock market volatility in India. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. Various volatility estimators and diagnostic tests indicate volatility clustering, i.e., shocks to the volatility process persist and the response to news arrival is asymmetrical, meaning that the impact of good and bad news is not the same. Suitable volatility forecast models are used for Sensex and Nifty returns to show that: The ‘day-of-the-week effect’ or the ‘weekend effect’ and the ‘January effect’ are not present while the return and volatility do show intra-week and intra-year seasonality. The return and volatility on various weekdays have somewhat changed after the introduction of rolling settlements in December 1999. There is mixed evidence of return and volatility spillover between the US and Indian markets. The empirical findings would be useful to investors, stock exchange administrators and policy makers as these provide evidence of time varying nature of stock market volatility in India. Specifically, they need to consider the following findings of the study: For both the indices, among the months, February exhibits highest volatility and corresponding highest return. The month of March also exhibits significantly higher volatility but the magnitude is lesser as compared to February. This implies that, during these two months, the conditional volatility tends to increase. This phenomenon could be attributed to probably the most significant economic event of the year, viz., presentation of the Union Budget. The investors, therefore, should keep away from the market during March after having booked profits in February itself. The surveillance regime at the stock exchanges around the Budget should be stricter to keep excessive volatility under check. Similarly, the month of December gives high positive returns without high volatility and, therefore, offers good opportunity to the investors to make safe returns on Sensex and Nifty. On the contrary, in the month of September, i.e., the time when the third quarter corporate results are announced, volatility is higher but corresponding returns are lower. The situation is, therefore, not conducive to investors. The ‘weekend effect’ or the ‘Monday effect’ is not present. For other weekdays, the ‘higher (lower) the risk, higher (lower) the return’ dictum does not hold consistently and Wednesday provides higher returns with lower volatility making it a good day to invest. The domestic investors and the stock exchange administrators do not need to lose sleep over gyrations in the major US markets since there is no conclusive evidence of consistent relationship between the US and the domestic markets. The volatility forecast models presented for Sensex and Nifty can be used to forecast future volatility of these indices.
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Aouadi, Amal, Mohamed Arouri, and Frederic Teulon. "Investor Following and Volatility: A GARCH Approach." Journal of Applied Business Research (JABR) 31, no. 3 (May 1, 2015): 765. http://dx.doi.org/10.19030/jabr.v31i3.9201.
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n this paper, we aim to investigate whether investor following is a determinant of the stock market volatility. To measure investor following, we use Google Insights for search freshly introduced to the financial literature. The latter records the online search traffic for any keyword submitted to Google since 2004. Thanks to an extensive database, we focus precisely on the French stock market unlike previous works, which have focused largely on the US stock market. Notably, our findings support strong significant effects of investor following as measured by online search behavior on the conditional volatility estimated from GARCH (1,1) Market model. Our results are robust to additional tests.
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Khan, Anila Rafique, Muhammad Waqas, and Arshad Hassan. "Market Volatility and Momentum: Evidence from Pakistani Stock Exchange." Sukkur IBA Journal of Management and Business 4, no. 1 (May 31, 2017): 82. http://dx.doi.org/10.30537/sijmb.v4i1.105.
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This study explores the relationship between market volatility and momentum profitability. This study indicates that market state volatility has significant power to forecast momentum payoffs, especially in negative market states. The results are the context in the presence of market state and business cycle variables. Market premium is significant and negative. Market volatility is also found negatively influencing momentum profits. Volatility is divided into volatility in the positive market and volatility in the negative market. Both are significantly and negatively influencing momentum profits. Vol+ and Vol- both have negative signs; Vol- is dominant in terms of the magnitudes of the coefficient and the t-statistics. Business cycle effect measured by term and yield is not found significant. Non-linearity has not been observed regarding the term. Results are found robust for market adjusted momentum payoff. The study also explores the impact of market state, volatility and business cycles on the return of loser and winner portfolio. This study reports that returns of the loser portfolios are explained by market component, whereas volatility is found to be insignificant. The macroeconomic variables TERM, TERM2 and YLD show signs of statistical significance. Market factor is significantly and positively influencing winner portfolios. The results indicate that volatile markets forecast low returns on winner stocks. Return dispersion used to measures cross-sectional is also found significant. The study recommends that investors should devise investment and momentum strategies on the basis of the volatility of stocks and the business cycle. The tests of this study show that volatile down markets forecast low momentum payoffs. The time-series predictability of momentum is asymmetric, which arises from loser stocks.
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Çevik, Emrah, Erdal Atukeren, and Turhan Korkmaz. "Oil Prices and Global Stock Markets: A Time-Varying Causality-In-Mean and Causality-in-Variance Analysis." Energies 11, no. 10 (October 21, 2018): 2848. http://dx.doi.org/10.3390/en11102848.
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This study examines the Granger-causal relationships between oil price movements and global stock returns by using time-varying Granger-causality tests in mean and in variance. We use the daily returns from Morgan Stanley Capital International (MSCI) G7 and the MSCI Emerging Stock Market Indexes to distinguish between the effects of daily oil price movements on G7 countries’ and emerging market countries’ stock markets. We further divide the emerging markets into two groups as oil-exporting and oil-importing countries. For the oil market, we use both the West Texas Intermediate (WTI) and Brent oil daily price movements. While the Granger-causality-in-mean tests indicate a causal link from WTI oil prices and G7 countries’ stock returns to MSCI emerging countries’ stock returns, the Granger-causality-in-variance tests suggest no causal link from global oil market prices to stock market returns. Nonetheless, a causal link from the G7 countries’ stock returns to the MSCI emerging countries’ stock returns is detected. In addition, G7 countries’ stock market volatility is found to Granger-cause Brent oil price volatility. The time-varying Granger-causality-in-mean and Granger-causality-in-variance tests present new and further insights. A causal relationship between oil price changes and G7 countries’ stock returns is found for some periods during and after the global financial crisis. Time-varying Granger-causality-in-variance test results indicate evidence of causal linkages among oil prices and global stock market returns that are specific only to certain time periods. We also find that there might be a difference between the movements in Brent and WTI oil prices with respect to their Granger-causal effects on oil-importing emerging markets’ stock returns—especially after the global financial crisis. Our results provide further evidence that the effects of oil price movements on stock returns might be different depending on the volatility in the stock markets.
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Lesmana, Intan Surya, and Siti Saadah. "Pandemic and Indonesia Stock Market Performance." Ilomata International Journal of Management 2, no. 4 (October 31, 2021): 254–62. http://dx.doi.org/10.52728/ijjm.v2i4.263.
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This study aims to analyze the impact of the COVID-19 pandemic on Indonesia’s stock market performance. Considering the characteristics of daily stock return data that shows the characteristics of volatility clustering, the analytical method used is to develop a heteroscedastic model specification whose parameters are estimated using the maximum likelihood method. Based on data from March 2020 to January 2021, this study finds that the Exponential-GARCH asymmetric model is the best model compared to the Standard-GARCH symmetric model or the asymmetric Threshold-GARCH model. The inference analysis conducted on the Exponential-GARCH asymmetric model in this study shows that the stock market's performance that is significantly affected by this pandemic is the volatility of its returns. Stock price volatility is one of the important variables in stock market performance. This study produces empirical findings that government policies on social restrictions contribute significantly to suppressing stock market volatility. As for government policies in mitigating the risk of the spread of the epidemic, in this study, it is measured through a stringency index. This index was released by the Oxford COVID-19 Government Response Tracker (OxCGRT) which monitors the government's response to the coronavirus in 160 countries and is a parameter that evaluates the policies taken by a country's government based on nine metrics. This index does not measure the effectiveness of a country's government response, but only the level of tightness. However, the results of the tests carried out in this study did not find a significant impact of pandemic indicators, the number of cases, and the number of daily deaths related to COVID-19 on stock returns.
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Souffargi, Wafa, and Adel Boubaker. "Structural Breaks, Asymmetry and Persistence of Stock Market Volatility: Evidence from Post-Revolution Tunisia." International Journal of Economics and Finance 14, no. 9 (August 22, 2022): 51. http://dx.doi.org/10.5539/ijef.v14n9p51.
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This paper analyses the impact of political uncertainty on the volatility of the Tunisian stock market from November 2010 to February 2016. In particular, it examines structural breaks in the variance by using the Iterated Cumulative Sums of Squares (ICSS) and modified ICSS algorithms. Asymmetric GARCH models are then extended by taking account regime shifts. Our results suggest that Tunisian stock market volatility is sensitive to local and political events. Large shifts coincide with civil uprisings and periods of political turbulence during the democratic transition and argue that the relationship between volatility and returns reflects the common effects of political factors. Diagnostic tests emphasize the asymmetric volatility response to news. However, there is no evidence that taking into account regime shifts reduces the volatility persistence which leads to think that the Tunisian stock market is well controlled and supervised.
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Zaghouani Chakroun, Amal, and Dorra Mezzez Hmaied. "Evidence on aggregate volatility risk premium for the French stock market." Managerial Finance 46, no. 1 (October 31, 2019): 72–91. http://dx.doi.org/10.1108/mf-11-2018-0535.
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Purpose The purpose of this paper is to examine alternative six- and seven-factor equity pricing models directed at capturing a new factor, aggregate volatility, in addition to market, size, book to market, profitability, investment premiums of the Fama and French (2015) and Fama and French’s (2018) aggregate volatility augmented model. Design/methodology/approach The models are tested using a time series regression and Fama and Macbeth’s (1973) methodology. Findings The authors show that both six- and seven-factor models best explain average excess returns on the French stock market. In fact, the authors outperform Fama and French’s (2018) model. The authors use sensitivity of aggregate volatility of a stock VCAC as a proxy to construct the aggregate volatility risk factor. The spanning tests suggest that Fama and French’s (1993, 2015, 2018) and Carhart’s (1997) models do not explain the aggregate volatility risk factor FVCAC. The results show that the FVCAC factor earns significant αs across the different multifactor models and even after controlling for the exposure to all the other in Fama and French’s (2018) model. The asset pricing tests show that it is systematically priced. In fact, the authors find a significant and negative (positive) relation between the aggregate volatility risk factor and the excess returns in the French stock market when it is rising (falling), in addition, periods with downward market movements tend to coincide with high volatility. Originality/value The authors contribute to the related literature in several ways. First, the authors test two new empirical six- and seven-factor model and the authors compare them to Fama and French’s (2018) model. Second, the authors give new evidence about the VCAC, using it for the first time to the authors’ knowledge, to construct a volatility risk premium.
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Omet, Ghassan, Mohammad Khasawneh, and Jamal Khasawneh. "Efficiency tests and volatility effects: evidence from the Jordanian stock market." Applied Economics Letters 9, no. 12 (October 2002): 817–21. http://dx.doi.org/10.1080/13504850210161931.
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Brancaccio, Emiliano, and Damiano Buonaguidi. "Stock Market Volatility Tests: A Classical-Keynesian Alternative to Mainstream Interpretations." International Journal of Political Economy 48, no. 3 (July 3, 2019): 253–74. http://dx.doi.org/10.1080/08911916.2019.1655954.
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Salisu, Afees A., Kazeem O. Isah, and Alberto Assandri. "Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach." Review of Economic Analysis 11, no. 2 (December 7, 2019): 255–83. http://dx.doi.org/10.15353/rea.v11i2.1628.
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This study examines probable dynamic spillover transmissions between the Nigerian stock and money markets using the multivariate volatility framework that simultaneously accounts for both returns and shock spillovers. Based on relevant pre-tests, the VARMA-CCC-GARCH framework is selected and consequently employed to model the spillovers. The study finds significant cross-market return and shock spillovers between the two markets. Thus, a shock to one market is more likely to spill over to the other market. It is also observed that shocks have persistent effects on stock market volatility but transitory effects on money market volatility. In other words, shocks to the money market die out over time while shocks to stock market tend to persist over time. In addition, including lagged own shocks and lagged own conditional variance when forecasting the future volatility of both return series may enhance their forecast performance. An alternative approach proposed by Diebold and Yilmaz (2012) is also employed for robustness and the results are consistent with those obtained from the VARMA-CCC-GARCH model.
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Carlston, Benjamin. "Can stock market liquidity and volatility predict business cycles?" Studies in Economics and Finance 35, no. 1 (March 5, 2018): 81–96. http://dx.doi.org/10.1108/sef-05-2016-0131.
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Purpose The purpose of this paper is to predict real gross domestic product (GDP) growth and business cycles by using information from both liquidity and volatility measures. Design/methodology/approach The paper estimates liquidity and volatility measures from over 5,000 NYSE rms and extracts a common factor, which the paper calls uncertainty. In-sample and out-of-sample forecasting tests are used to determine the ability of the uncertainty factor to predict growth in real GDP, industrial production, consumer price index, real consumption and changes in real investment. Findings The paper finds that on average, positive shocks to the uncertainty factor occur in the quarters preceding and at the beginning of a recession. During the quarters toward the end of recessions, there are negative shocks to uncertainty on average. Originality/value Previous research has explored using either liquidity or volatility to forecast economic activity. The paper bridges the two branches of research and finds a link to real GDP growth and business cycles.
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Singh, Amanjot, and Parneet Kaur. "Stock Market Linkages: Evidence From The US, China And India During The Subprime Crisis." Timisoara Journal of Economics and Business 8, no. 1 (June 1, 2015): 137–62. http://dx.doi.org/10.1515/tjeb-2015-0012.
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AbstractThe Subprime crisis spillovered the returns and volatility from the US stock market to the other integrated economies. The present study attempts to analyze the stock market linkages between the US, India and China, especially during the US subprime Crisis. The technique of Tri-Variate Vector Autoregression and the Spillover Index has been employed so as to analyze the relations during the time period 2007 to 2009. To estimate the time varying risk parameters, the technique of Threshold Generalized Autoregressive Conditional Heteroskedastic [TGARCH (1,1)] model has been used. A uni-directional causality has been observed from the US market to the Indian and Chinese market, whereas another unidirectional causality has also been spotted running from the Chinese market to the Indian market in the context of stock market returns during the crisis period. A unidirectional volatility spillover from the US to the Indian market and from the Indian to the Chinese market has been found to be significant. As per the volatility Spillover Index, the cross market impact on the volatility reduces over a time period 2007-2009, due to the increased impact of the past volatility and the presence of 'leverage effect'. The falling returns added to the volatility in the respective markets. The efficient tests of causality inspired by Hill (2007) reported an indirect impact of the US market volatility on the Chinese market via Indian. The portfolio managers should discount this information well ahead of time to maintain the portfolio values by taking positions in futures and options market.
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Padungsaksawasdi, Chaiyuth. "On the dynamic relationship between gold investor sentiment index and stock market." International Journal of Managerial Finance 16, no. 3 (December 6, 2019): 372–92. http://dx.doi.org/10.1108/ijmf-11-2018-0334.
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Purpose Considering the unique data of the gold investor sentiment index in Thailand, the purpose of this paper is to investigate the bivariate dynamic relationship between the gold investor sentiment index and stock market return, as well as that between the gold investor sentiment index and stock market volatility, using the panel vector autoregression (PVAR) methodology. The author presents and discusses the findings both for the full sample and at the industry level. The results support prior literature that stocks in different industries do not react similarly to investor sentiment. Design/methodology/approach The PVAR methodology with the GMM estimation is found to be superior to other static panel methodologies due to considering both unobservable time-invariant and time-variant factors, as well as being suitable for relatively short time periods. The panel data approach improves the statistical power of the tests and ensures more reliable results. Findings In general, a negative and unidirectional association from gold investor sentiment to stock returns is observed. However, the gold sentiment-stock realized volatility relationship is negative and bidirectional, and there exists a greater impact of a stock’s realized volatility on gold investor sentiment. Importantly, evidence at the industry level is stronger than that at the aggregate level in both return and volatility cases, confirming the role of gold investor sentiment in the Thai stock market. The capital flow effect and the contagion effect explain the gold sentiment-stock return relationship and the gold sentiment-stock volatility relationship, respectively. Research limitations/implications The gold price sentiment index can be used as a factor for stock return predictability and stock realized volatility predictability in the Thai equity market. Practical implications Practitioners and traders can employ the gold price sentiment index to make a profit in the stock market in Thailand. Originality/value This is the first paper to use panel data to investigate the relationships between the gold investor sentiment and stock returns and between the gold investor sentiment and stocks’ realized volatility, respectively.
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Mupondo, Ndava Constantine. "Liquidity, Trading Activity, and Stock Price Volatility." Finance & Economics Review 4, no. 2 (November 2, 2022): 12–25. http://dx.doi.org/10.38157/fer.v4i2.482.
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Purpose: While the bulk of previous research focused on security-level volatility and the relationship of its determinants, the current study considers the relationship between the number of trades, lagged absolute returns, trading volume, bid-ask spread, and price volatility on the Zimbabwe stock market. Methods: The study applied Hausman's (1982) tests of the specification. The parameters and elasticity of explanatory variables have been estimated by utilizing the Generalized Method of Moments (GMM) procedure in a five-equation structural model. The data were obtained from a web-based financial market platform, Investing.com for the period between 2009 and 2021. Results: Results show that inflation had a positive relationship with stock price volatility, which provided a hedge against inflation. There exists an indistinguishable difference between the random effects (RE) and fixed effects (FE) results and those obtained using the Pooled Ordinary Least Squares (POLS) on the total sample reflecting a cohesion of these findings. Implications: Understanding the relationship between inflation and market risk (volatility) can be beneficial to the investor in selecting the appropriate and most convenient investment strategy. From a policy-making perspective, strategic policy measures employed towards reducing inflation would certainly reduce stock market volatility and boost investor confidence.
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Minh Huong, Le Thi. "The Role of World Oil Price in the Movements of the Asian Stock Market." International Journal of Innovation and Economic Development 6, no. 2 (2020): 7–18. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.62.2001.
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This article contributed insight into the cross-border role of oil on Asia’s largest stock markets. The research conducted using VAR, GARCH_BEKK (1,1), and related tests such as stationarity, correlation, and causality tests in the analysis. The results obtained suggest that the time series of data ensure conditions for analysis. Asian stock prices are inversely related to oil prices in a correlation. At the same time, in considered stock markets, the Korean stock market and world oil prices appear to have a causal relationship with each other. Moreover, the tests of profitability and volatility in oil prices also indicate a link with the Korean stock market during the research period.
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Kamstra, Mark J., Lisa A. Kramer, and Maurice D. Levi. "Effects of Daylight-Saving Time Changes on Stock Market Returns and Stock Market Volatility: Rebuttal." Psychological Reports 112, no. 1 (February 2013): 89–99. http://dx.doi.org/10.2466/17.01.pr0.112.1.89-99.
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In a 2011 reply to our 2010 comment in this journal, Berument and Dogen maintained their challenge to the existence of the negative daylight-saving effect in stock returns reported by Kamstra, Kramer, and Levi in 2000. Unfortunately, in their reply, Berument and Dogen ignored all of the points raised in the comment, failing even to cite the Kamstra, et al. comment. Berument and Dogen continued to use inappropriate estimation techniques, over-parameterized models, and low-power tests and perhaps most surprisingly even failed to replicate results they themselves reported in their previous paper, written by Berument, Dogen, and Onar in 2010. The findings reported by Berument and Dogen, as well as by Berument, Dogen, and Onar, are neither well-supported nor well-reasoned. We maintain our original objections to their analysis, highlight new serious empirical and theoretical problems, and emphasize that there remains statistically significant evidence of an economically large negative daylight-saving effect in U.S. stock returns. The issues raised in this rebuttal extend beyond the daylight-saving effect itself, touching on methodological points that arise more generally when deciding how to model financial returns data.
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LIU, Hsiang-Hsi, and Sheng-Hung CHEN. "NONLINEAR RELATIONSHIPS AND VOLATILITY SPILLOVERS AMONG HOUSE PRICES, INTEREST RATES AND STOCK MARKET PRICES." International Journal of Strategic Property Management 20, no. 4 (December 14, 2016): 371–83. http://dx.doi.org/10.3846/1648715x.2016.1191557.
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This paper addresses the interaction between interest rates and the significant increases in both Taiwanese house and stock market prices seen in recent years. Changes in house prices impact banks’ nonperforming loans, whereas changes in interest rates directly influence the ability of individuals and businesses to pay loan interest, accentuating the co-movements between house and stock mar-ket prices. We investigate the nonlinear relations and volatility spillovers among house prices, interest rates and stock market prices using monthly data from January 1985 to March 2009 for Taiwan. We find that the Smooth Transition Vector Error Correction GARCH (STVEC-GARCH) model has the best forecasting ability based on goodness of fit tests while showing a nonlinear and co-integrated relation among the three variables. Specifically, house price leads stock market returns when the interest rate is led by either house price or stock market returns. The volatility of stock market returns has significant impacts on interest rates, implying that borrowers should be aware of stock market fluctuations and thus strengthen their risk management because of unexpected changes.
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Wanyama, Dr David W. "EFFECT OF STOCK MARKET DEVELOPMENT ON THE GROWTH OF CORPORATE BOND MARKET IN KENYA." International Journal of Finance 2, no. 2 (February 5, 2017): 16. http://dx.doi.org/10.47941/ijf.54.
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Purpose: The purpose of this study was to analyze how stock market development influences the growth of corporate bond market in Kenya.Methodology: The study used descriptive and causal research designs. Secondary data was used. The sample of the study consisted of daily and monthly time series covering six years beginning January 2009 to December 2014. Unit root tests using Augmented Dickey-Fuller (ADF) and Phillips-Perron tests were done. The study used Eviews econometric software to facilitate empirical analysis of data.Results: Regression of coefficients results shows that Stock market size and corporate bonds are positively and significant related (r=0.029, p=0.002), stock market liquidity and corporate bonds are positively and significant related (r=8.291, p=0.0008), Stock Market Concentration and corporate bonds are positively and significant related (r=0.014, p=0.017). Regression of coefficients results shows that Stock Market Volatility and corporate bonds are positively and significant related (r=0.000023, p=0.0001).Unique Contribution to Theory, Practice and Policy: This study recommends study recommends for Policy makers to come up with measures to enhance the liquidity of the stock market which will in turn encourage investment in corporate bonds. The study recommends that concerted efforts should be made to improve market concentration in the corporate bonds market so that it can operate optimally. Policy makers should be aware of and monitor the level of stock market volatility that is appropriate for promoting the growth of the corporate bond markets and indeed other financial markets. Policy makers in Kenya should find ways and means of increasing the size of the stock market to reap the aforementioned benefits.
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Alikhanov, Abdulla. "To What Extent are Stock Returns Driven by Mean and Volatility Spillover Effects? – Evidence from Eight European Stock Markets." Review of Economic Perspectives 13, no. 1 (March 1, 2013): 3–29. http://dx.doi.org/10.2478/v10135-012-0013-7.
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Abstract The paper investigates mean and volatility spillover effects from the U.S and EU stock markets as well as oil price market into national stock markets of eight European countries. The study finds strong indication of volatility spillover effects from the US-global, EU-regional, and the world factor oil towards individual stock markets. While both mean and volatility spillover transmissions from the US are found to be significant, EU mean spillover effects are negligible. To evaluate the magnitude of volatility spillovers, the variance ratios are also computed and the results draw to attention that the individual emerging countries’ stock returns are mostly influenced by the U.S volatility spillovers rather than EU or oil markets. Additionally, examination of only global and regional stock markets spillover transmissions into European stock markets also confirms the dominating presence of the U.S spillover transmissions. Furthermore, I also implement asymmetric tests on stock returns of eight markets. The stock market returns of Hungary, Poland, Russia and the Ukraine are found to respond asymmetrically to negative and positive shocks in the US stock returns. The weak evidence of asymmetric effects with respect to oil market shocks is found only in the case of Russia and the quantified variance ratios indicate that presence of oil market shocks are relatively higher for Russia. Moreover, a model with dummy variable confirms the effect of European Union enlargement on stock returns only for Romania. Finally, a conditional model suggests that the spillover effects are partially explained by instrumental macroeconomic variables, out of which exchange rate fluctuations play the key role in explaining the spillover parameters rather than total trade to GDP ratios in most investigated countries.
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Insaidoo, Michael, Lilian Arthur, Samuel Amoako, and Francis Kwaw Andoh. "Stock market performance and COVID-19 pandemic: evidence from a developing economy." Journal of Chinese Economic and Foreign Trade Studies 14, no. 1 (January 15, 2021): 60–73. http://dx.doi.org/10.1108/jcefts-08-2020-0055.
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Purpose The purpose of this study is to assess the extent to which the Ghana stock market performance has been impacted by the novel COVID-19 pandemic. Design/methodology/approach The study used the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model, by using daily time series data from 2 January 2015 to 13 October 2020. Both pre-estimation (Augmented Dickey-Fuller and Phillips-Perron) and post-estimation tests (Jarque-Bera) were conducted to validate the results. Findings While the study shows a statistically insignificant negative relationship between the COVID-19 pandemic and the Ghana stock returns, the results confirm that the COVID-19 pandemic has occasioned an increase in the Ghana stock returns volatility by 8.23%. Furthermore, the study confirmed the presence of volatility clustering and asymmetric effect, with the latter implying that worthy news tends to affect volatility more than unwelcome news of equal size. Practical implications To dampen uncertainties that trigger stock market volatility, the government should surgically target worse affected COVID-19 pandemic businesses and households to check the drop in profits and demand. Rigidities associated with stock market operations must be addressed to make it attractive to investors even in the midst of a pandemic. Originality/value This paper is a pioneer attempt at assessing the extent to which a developing economy stock market has been impacted by the novel COVID-19 pandemic using the EGARCH model.
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Mateev, Miroslav. "Volatility relation between credit default swap and stock market: new empirical tests." Journal of Economics and Finance 43, no. 4 (January 3, 2019): 681–712. http://dx.doi.org/10.1007/s12197-018-9467-5.
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Neenu, C., and T. Mohamed Nishad. "Asymmetric Volatility and Leverage Effect in Stock Market: A Bibliometric Review." Review of Finance and Banking 14, no. 1 (June 30, 2022): 21–34. http://dx.doi.org/10.24818/rfb.22.14.01.02.
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This paper aims to provide quantitative statistics and a comprehensive review of the key ináuential and intellectual structure of asymmetric volatility and leverage e§ect in the stock market. This study reviews 271 articles and review papers published in scientiÖc journals indexed by the Scopus database from 1994 to 2021. VOSviewer and Excel software are used to analyse the collected data and apply the bibliometric tests such as citation analysis of documents, source, authors, institutions and countries, Co-occurrence analysis of author keywords, co-authorship and Bibliographic coupling of authors, source and countries. By analysing the growth in this topic, during the Örst 10 years, the publication in this topic was considerably less. After that, until 2020, the growth is remarkable. Further, the study identiÖes the most ináuential and impactful authors, journals, institutions, and countries to asymmetric volatility and leverage e§ect in the stock market. To the best of the authorís knowledge, this is one of the Örst papers to address the literature of asymmetric volatility and leverage e§ect in the stock market from a bibliometric aspect. It helps researchers and other academicians to explore and build a quantitative base regarding the scientiÖc development of asymmetric volatility and leverage e§ect in the stock market.
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Chiou, Wan-Jiun Paul, Alice C. Lee, and Cheng-Few Lee. "Variation in Stock Return Risks: An International Comparison." Review of Pacific Basin Financial Markets and Policies 12, no. 02 (June 2009): 245–66. http://dx.doi.org/10.1142/s0219091509001666.
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Using returns of 4,916 stocks from 22 developed countries and 15 developing countries, this study examines the relative magnitude of conditional volatility and the international market systematic risk of stock prices in countries at different developmental stages and in various geographical areas. Consistent with the finding of Bekaert et al. (2008), the results of non-parametric Mann-Whitney tests suggest that the stock prices in emerging markets are riskier than the ones in developed countries, measured by both conditional volatility and global beta. Our empirical findings also support the geographical variation in stock risks. Specifically, the equity values in Southeast Asia, South Europe, and Latin America are more volatile than the rest of the world. Similar results can be found in the country-level tests. The time-series analysis suggests that the stock returns in high risk countries tend to be less volatile but the conditional volatility of stock return in less risky countries leans to increase.
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Bhama, Vandana. "Macroeconomic variables, COVID-19 and the Indian stock market performance." Investment Management and Financial Innovations 19, no. 3 (July 12, 2022): 28–37. http://dx.doi.org/10.21511/imfi.19(3).2022.03.
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India witnessed the first major wave of COVID-19 in 2020. The second major wave during April 2021 caused a higher number of infected cases across the country. These waves of COVID-19, rising cases and lockdown announcements severely impacted the Indian economy. Moreover, huge volatility was observed in the prices of oil and exchange rates during the similar period. Thus, this study tests the effect of selected macroeconomic variables and the COVID-19 pandemic on the performance of the Indian stock market. Using co-integration and the vector error correction model on the NIFTY 100 firms, the findings suggest co-integration and long-term association among variables. The Indian stock market experienced an inverse connection with the exchange rate volatility; the coefficient value is 57.582. The exchange rates rose heavily (with a value of Indian rupee being 76.95 against US dollar) with the onset of COVID-19 cases. Further, these cases do hurt the sentiments of the stock market; however, the relationship is relatively infirm (the value is 0.22) as compared to that of the exchange rate. The accumulated major negative influence of COVID-19 on the economy had a weak impact on the stock market. In conclusion, it should be noted that after the first wave, businesses were more prepared and therefore incorporated the required changes that saw them through the second wave. AcknowledgmentThe infrastructural support provided by the FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.
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Lee, Hsiu-Chuan, Chih-Hsiang Hsu, and Cheng-Yi Chien. "Spillovers of international interest rate swap markets and stock market volatility." Managerial Finance 42, no. 10 (October 10, 2016): 943–62. http://dx.doi.org/10.1108/mf-08-2015-0221.
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Purpose The purpose of this paper is to investigate volatility spillovers across the interest rate swap markets of the G7 economies, and then the authors investigate whether spillovers of swap markets contain useful information to explain subsequent stock price movements. Design/methodology/approach This study uses the short- and long-term swap spread volatility of the G7 countries to explore the spillover effects of international swap markets, and then investigates the relationship between swap and stock markets. The authors use the generalized VAR approach suggested by Diebold and Yilmaz (2012) to study spillovers of international swap markets. The Granger-causality tests are employed to examine the linkage of interest rate swap and stock markets. Findings This paper shows that a moderate spillover effect exists for the short- and long-term swap markets. Moreover, the results show that the short- and long-term swap markets of France and Germany have a larger impact on other countries’ swap markets than that of other countries’ swap markets on the French and German swap markets. Finally, the results indicate that the total volatility spillovers for the long-term swap markets have a larger influence on the total volatility spillover index of stock markets and the global stock market volatility than that of the short-term swap markets. Originality/value Prior literature has used impulse response and variance decomposition analyses to investigate international swap markets linkages. However, the results depend on the ordering of variables. This study uses the framework of Diebold and Yilmaz (2012) to overcome the ordering issue, and thus the authors can compute directional spillovers. This paper is the first study to explore the linkage of the total volatility spillover of swap markets and the stock markets.
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Muhammad, Sagheer, Adnan Akhtar, and Nasir Sultan. "Shock Dependence and Volatility Transmission Between Crude Oil and Stock Markets: Evidence from Pakistan." Lahore Journal of Business 5, no. 1 (September 1, 2016): 1–14. http://dx.doi.org/10.35536/ljb.2016.v5.i1.a1.
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This paper investigates shock dependence and volatility transmission between the crude oil and equity markets, based on crude oil returns and stock index returns for the period 2 January 2009 to 27 January 2014. We employ the bivariate BEKK-GARCH (1, 1) model developed by Engle and Kroner (1995) as well as the Engle and Granger (1987) cointegration and unit root tests. These parameterization tools are more flexible and innovative than other specifications, which often give counter-intuitive results. The results of the cointegration test reject the notion of a long-run relationship between the crude oil market and stock market. The results of the BEKK-GARCH model suggest that shocks and volatility created in the oil market have a significant effect on the Pakistan Stock Exchange. They also reveal bidirectional shock persistence and a unidirectional volatility spillover between crude oil prices and Pakistani equity prices. These empirical findings can help predict price movements in each market efficiently. The empirical results are also important for policymakers involved in shock prevention and for portfolio managers seeking optimal portfolio allocation.
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Mahboob Ali, Muhammad, Aviral Kumar Tiwari, and Naveed Raza. "Impact of return on long-memory data set of volatility of Dhaka Stock Exchange market with the role of financial institutions: an empirical analysis." Banks and Bank Systems 12, no. 3 (August 29, 2017): 48–60. http://dx.doi.org/10.21511/bbs.12(3).2017.04.
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The current study intends to empirically test a relationship between long-memory features in returns and volatility of Dhaka Stock Exchange market. As such, the study uses the ARFIMA-FIGARCH and FIPARCH structure for the daily data ranging from 15 December 2003 to July 31, 2013 of Dhaka Stock Exchange market index, i.e., DSE General Index (DGEN). The observed indication assembled from long-memory tests supports the occurrence of long memory in Bangladesh stock returns. The study aims at doing research work with long-memory data set, as it provides a superior strategy, as well as gives real picture with short-memory data set. Moreover, the backup indication for existence of long memory in both return and volatility denies the efficient market hypothesis of Fama (1970) that the future return and volatility values are unpredictable. Extra measures ought to be given for the smooth functioning of the Dhaka Stock Exchange market so that both individual and institutional investors can get congenial atmosphere to invest. Authors’ suggested that Bangladesh Bank must play vital role as share market of Bangladesh is dominated by banking shares and in case of other listed shares of the Dhaka Stock Exchange, market authority should deal with transparently and fairly so that the market can be transformed into strong efficient market. This requires suitable directives, groundwork, removing malpractices and also implementation of investors’ friendly decisions. Further, fiscal policy of the country should be pro investor friendly, as well as monetary policy should work as complementary towards investment at stock exchange market as suggested by the authors.
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Gopal, Suresh. "Causal Relationship between Gold, Crude Oil & US Dollar Rates and S&P BSE 100 in India: An Experimental Study." GIS Business 11, no. 5 (October 24, 2016): 41–50. http://dx.doi.org/10.26643/gis.v11i5.3414.
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In the present globalized business scenario, volatility in gold price, international crude oil price, and US Dollar exchange rate are likely to stimulate uncertainty in stock market conditions globally. The degree of uncertainty in stock market is high in the case of developing nations like India. Therefore, the study of causal relationship of gold, crude oil, and US Dollar rates with the stock market indices (S&P BSE 100) in India is more appropriate. The researchers have analyzed these macroeconomic variables along with the S&P BSE 100 with the help of econometric tools viz. Augmented Dickey-Fuller Test for Unit-Root, Johansen Co-Integration Test, Pairwise Granger Causality Tests, Vector Auto Regression Modeling, Variance Decomposition test, and Impulse responses analysis. The econometric research software called EVIEWS 6 was used to apply all those tools successfully. The result shows that there is a high impression in the Indian stock market due to the volatility happens in the described macroeconomic factors.
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Ahmed, Fakrul. "Assessment of Capital Market Efficiency in COVID-19." European Journal of Business and Management Research 6, no. 3 (May 6, 2021): 42–46. http://dx.doi.org/10.24018/ejbmr.2021.6.3.839.
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The study tries to focus on the efficiency of the capital market through investigating the randomness of return series of Dhaka Stock Exchange of Bangladesh. Due to COVID-19 pandemic the worldwide capital market faces higher volatility than before. The study finds the week form of efficiency level of Bangladesh capital market. Special focus on Run test, Auto correlation test, predictability of tock return using ARIMA model the weekend effect anomaly and momentum strategy investing. The study found that the hypothesis of randomness of the stock returns are rejected for stock price index changes by using random walk tests, normality of return distributions, runs test and at different lags using ARIMA and the momentum tests which assert Dhaka Stock Exchange is not efficient even in the weak form.
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WU, Maoguo, and Xin LUO. "An Empirical Analysis of Stock Price Risk in Chinese Growth Enterprises Market - A GARCH-VaR Approach." European Scientific Journal, ESJ 12, no. 22 (August 30, 2016): 341. http://dx.doi.org/10.19044/esj.2016.v12n22p341.
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The aim of Growth Enterprises Market (GEM) is to provide financing channels to burgeoning and high-technology companies which cannot be listed in the main board. GEM is a supplement to the main board. As an emerging securities market, GEM shows a unique volatility compared with the main board. The volatility of GEM has connections and differences with the main board market. Studying the price volatility of GEM contributes directly to the healthy growth of GEM and the main board. This paper investigates the risk characteristics of GEM and provides several measures to deal with the risk. In this paper, VaR based on GARCH model is utilized for empirical tests. Therefore, this paper studies the characteristics and the extent of volatility risk of GEM stock price systematically.
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GUANGXI, CAO, HAN YAN, and CUI WEIJUN. "TIME-VARYING LONG MEMORIES OF THE CHINESE CURRENCY AND STOCK MARKETS BASED ON THE HURST EXPONENT." Fluctuation and Noise Letters 13, no. 01 (March 2014): 1450007. http://dx.doi.org/10.1142/s0219477514500072.
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Based on the daily return and volatility series of the Chinese yuan (RMB)/US dollar (USD) exchange rate and the Shanghai Stock Composite Index, the time-varying long memories of the Chinese currency and stock markets are investigated by comprehensively using the rescaled range (R/S), the modified R/S, and the detrended fluctuation analysis methods. According to the results drawn: (1) the efficiency of the Chinese currency market has not improved significantly, whereas the efficiency of the Chinese stock market has improved steadily, (2) volatility series presents longer memory than return series either in the Chinese currency or stock market and (3) the time-varying Hurst exponent of the Chinese currency market is sensitive to the reform that enhances the flexibility of the RMB exchange rate. Moreover, we find that short-term bidirectional Granger causal relationship exists, but no long-run equilibrium relationship between the time-varying Hurst exponents of the Chinese currency and stock markets was found based on the Granger causality and cointegration tests, respectively.
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Ali, Peter. "Analysis of Volatility Spillover in African Stock Markets: Evidence from Nigeria, Ghana, and South Africa." AFRE (Accounting and Financial Review) 5, no. 1 (February 20, 2022): 64–71. http://dx.doi.org/10.26905/afr.v5i1.7547.
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The purpose of this paper was to analyze stock market return volatility spillover between in Sub-Sahara markets using Nigeria, Ghana and South Africa monthly data from January 2000 to December 2017. Preliminary analyses from descriptive statistics show that show mean monthly returns are positive for all the stock markets. Skewness coefficients show that the stock returns and interest rates distribution of all Sub-Sahara Africa stock markets are negatively skewed but inflation rate is positively skewed for Nigeria and South Africa, and flat for Ghana. Excess kurtoses are positive for all the stock markets and macroeconomic indicators, and Jarque-Bera statistics indicate the stock markets’ series and macroeconomic indicators are not normally distributed. The Unit roots tests results indicate that all the stock markets and macroeconomic indicators are first difference stationary. The results of multivariate BEKK-GARCH (1,1) model show evidence of volatility spillover in Sub-Sahara Africa stock markets. We therefore recommend amongst others that stock market authorities should formulate and implement policies that would mitigate any negative effect of stock return volatility on the wealth of retail investors so as to sustain investors’ confidence in the African stock markets. This will eliminate the destabilising impact on the investors’ confidence on the markets.
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Zhang, Jing, Ya-Ming Zhuang, and Jia-Bao Liu. "An Empirical Analysis of Oil and Stock Markets’ Volatility Based on the DGC-MSV-t Model." Journal of Mathematics 2021 (December 31, 2021): 1–7. http://dx.doi.org/10.1155/2021/6270525.
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We investigate the spillover effect between crude oil future prices, crude oil spot prices, and stock index by using the multivariate stochastic volatility model. These tests between each market show the significant Granger causes of spillover effect. More and more evidences show that the crude oil price has been affected by other financial markets. The oil future played an important role in the energy market. WTI and Brent oil future have more spillover effect than INE oil future. The result shows that S&P stock market is more sensitive to the oil price than Shanghai stock market. The cross-market spillover effect we found can give some advices for the investor of oil and stock market. DIC test shows that DGC-MSV-t is considered effective and more accurate.
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Choudhry, Taufiq, Fotios I. Papadimitriou, and Sarosh Shabi. "Stock market volatility and business cycle: Evidence from linear and nonlinear causality tests." Journal of Banking & Finance 66 (May 2016): 89–101. http://dx.doi.org/10.1016/j.jbankfin.2016.02.005.
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Ahmad, Wasim, and Sanjay Sehgal. "Regime shifts and volatility in BRIICKS stock markets: an asset allocation perspective." International Journal of Emerging Markets 10, no. 3 (July 20, 2015): 383–408. http://dx.doi.org/10.1108/ijoem-02-2013-0022.
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Purpose – The purpose of this paper is to examine the regime shifts and stock market volatility in the stock market returns of seven emerging economies popularly called as “BRIICKS” which stands for Brazil, Russia, India, Indonesia, China, South Korea and South Africa, over the period from February 1996 to January 2012 by applying Markov regime switching (MS) in mean-variance model. Design/methodology/approach – The authors apply MS model developed by Hamilton (1989) using its mean-variance switching framework on the monthly returns data of BRIICKS stock markets. Further, the estimated probabilities along with variances have been used to calculate the time-varying volatility. The authors also examine market synchronization and portfolio diversification possibilities in sample markets by calculating the Logit transformation based cross-market correlations and Sharpe ratios. Findings – The applied model finds two regimes in each of these markets. The estimated results also helped in formulating the asset allocation strategies based on market synchronization and Sharpe ratio. The results suggest that BRIICKS is not a homogeneous asset class and each market should be independently evaluated in terms of its regime-switching behavior, volatility persistence and level of synchronization with other emerging markets. The study finally concludes that Russia, India and China as the best assets to invest within this emerging market basket which can be pooled with a mature market portfolio to achieve further benefits of risk diversification. Research limitations/implications – The study does not provide macroeconomic and financial explanations of the observed differences in dynamics among sample emerging stock markets. The study does not examine these markets under multivariate framework. Practical implications – The results highlight the role of regime shifts and stock market volatility in the asset allocation and risk management. This study has important implications for international asset allocation and stock market regulation by way of identifying and recognizing the differences on regimes and on the dynamics of the swings which can be very useful in the field of portfolio and public financial management. Originality/value – The paper is novel in employing tests of MS under mean-variance framework to examine the regime shifts and volatility switching behavior in seven promising BRIICKS stock market. Further, using MS model, the authors analyze the duration (persistence) of each identified regime across sample markets. The empirical results of MS model have been used for making portfolio allocation strategies and also examine the synchronization across markets. All these aspects of stock market regime have been largely ignored by the existing studies in emerging market context particularly the BRIICKS markets.
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Akbar, Muhammad, Aima Tahir, and Syeda Faiza Urooj. "Stand Still and Do Nothing: COVID-19 and Stock Returns and Volatility." NICE Research Journal 13, no. 4 (December 30, 2020): 82–94. http://dx.doi.org/10.51239/nrjss.v13i4.234.
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We examine the intraday returns and volatility in the US equity market amid the COVID-19 pandemic crisis. Our empirical results suggest an increase in volatility over time with mostly negative returns and higher volatility in the last trading session of the day. Our Univariate analysis reveals structural break(s) since the first trading halt in March 2020 and that failure to account for this may lead to biased and unstable conditional estimates. Allowing for time-varying conditional variance and conditional correlation, our dynamic conditional correlation tests suggest that COVID-19 cases and deaths are jointly related to stock returns and realised volatility.