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1
Zevallos, Mauricio, and Carlos del Carpio. "Metal Returns, Stock Returns and Stock Market Volatility." Economia 38, no. 75 (August 1, 2015): 101–22. http://dx.doi.org/10.18800/economia.201501.003.
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Given the extensive participation of mining stocks in the Peruvian stock market, the Lima Stock Exchange (BVL) provides an ideal setting for exploring both the impact of metal returns on mining stock returns and stock market volatility, and the comovements between mining stock returns and metal returns. This research is a first attempt to explore these issues using international metal prices and the prices of the most important mining stocks on the BVL and the IGBVL index. To achieve this, we use univariate GARCH models to model individual volatilities, and the Exponentially Weighted Moving Average (EWMA) method and multivariate GARCH models with time-varying correlations to model comovements in returns. We found that Peruvian mining stock volatilities mimic the behavior of metal volatilities and that there are important correlation levels between metals and mining stock returns. In addition, we found time-varying correlations with distinctive behavior in different periods, with rises potentially related to international and local historical events.
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Shkolnyk, Inna, Serhiy Frolov, Volodymyr Orlov, Viktoriia Dziuba, and Yevgen Balatskyi. "Influence of world stock markets on the development of the stock market in Ukraine." Investment Management and Financial Innovations 18, no. 4 (November 24, 2021): 223–40. http://dx.doi.org/10.21511/imfi.18(4).2021.20.
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Viewing the development of the stock market in Ukraine, the economy, which world financial organizations characterize as small and open, is largely determined by the trends formed by the global stock markets and leading stock exchanges. Therefore, the study aims to analyze Ukraine’s stock market, the world stock market, stock markets in the regions, and to assess their mutual influence. The study uses the data of the World Federation of Exchanges and National Securities and Stock Market Commission (Ukraine) from 2015 to 2020. Stock market performance forecasts are built using triple exponential smoothing. Based on pairwise correlation coefficients, the existence of a significant dependence in the development of the world stock market on the development of the American stock market was determined. Regarding the Ukrainian stock exchanges, only SE “PFTS” demonstrated its dependence on the US stock market. The results of the regression model based on an exponentially smoothed series of trading volumes in all markets showed that variations in the volume of trading on the world stock market are due to the situation on the US stock markets. Trading volume dynamics on Ukrainian stock exchanges such as SE “PFTS” and SE “Perspektiva” is almost 50% determined by the development of stock markets in the American region. Although Ukraine is geographically located in Europe, the results show a lack of significant links and the impacts of stock markets in this region on the major Ukrainian stock exchanges and the stock market as a whole.
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Chi, Wei, Robert Brooks, Emawtee Bissoondoyal-Bheenick, and Xueli Tang. "Classifying Chinese bull and bear markets: indices and individual stocks." Studies in Economics and Finance 33, no. 4 (October 3, 2016): 509–31. http://dx.doi.org/10.1108/sef-01-2015-0036.
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Purpose This paper aims to investigate Chinese bull and bear markets. The Chinese stock market has experienced a long period of bear cycle from early 2000 until 2006, and then it fluctuated greatly until 2010. However, the cyclical behaviour of stock markets during this period is less well established. This paper aims to answer the question why the Chinese stock market experienced a long duration of bear market and what factors would have impacted this cyclical behaviour. Design/methodology/approach By comparing the intervals of bull and bear markets between stocks and indices based on a Markov switching model, this paper examines whether different industries or A- and B-share markets could lead to different stock market cyclical behaviour and whether firm size can determine the relationship between the firm stock cycles on the market cycles. Findings This paper finds a high degree of overlapping of bear cycles between stocks and indices and a high level of overlapping between the bear market and a fraction of stock with increasing stock prices. This leads to the conclusion that the stock performance and trading behaviour are widely diversified. Furthermore, the paper finds that the same industry may have different overlapping intervals of bull or bear cycles in the Shanghai and Shenzhen stock markets. Firms with different sizes could have different overlapping intervals with bull or bear cycles. Originality/value This paper fills the literature gap by establishing the cyclical behaviour of stock markets.
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Fu, Maggie Mei-Zhi, Kokkiang Tan, Ahmad Nadzri Rose, and Banafsheh Samadi. "Spillover Effect of Chinese Export on New ASEAN-5 Stock Markets using Markov Regime Switching Model." International Journal of Advanced Business Studies 2, no. 1 (March 1, 2023): 53–64. http://dx.doi.org/10.59857/raod1747.
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China as the second largest economy supposes to produce spillover effect on the emerging market economies especially the ASEAN nations. The main objective of this paper is to study the spillover effect of the Chinese export on the new ASEAN-5 stock markets (Indonesia, Malaysia, Philippines, Thailand, and Vietnam). In this paper, multivariate Markov-Switching Intercept Autoregressive Heteroscedasticity (MSIAH) model is employed to analyze the linkage between Chinese export and the new ASEAN-5 stock markets over the sample period of August 2000 to December 2018. The monthly data have been analysed using EViews. Their relationship is also strong and positive. The findings report that the spillover effects of China export on new ASEAN-5 stock markets is significant. There is positive relationship between China export and the stock markets for both regimes. The conclusion can be made is China exports should be one of the important factors in determining the stock prices in new ASEAN-5 stock markets. Investors should alert China export information when investing in new ASEAN-5 stock market. New ASEAN-5 are important emerging economies in Asia Pacific region and China is a rising economic power, but there is very least literature to study the Spillover effect of China export on stock market in new ASEAN-5.
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Karolyi, G. Andrew, and Ying Wu. "A New Partial-Segmentation Approach to Modeling International Stock Returns." Journal of Financial and Quantitative Analysis 53, no. 2 (March 19, 2018): 507–46. http://dx.doi.org/10.1017/s0022109017001016.
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We propose a new multi-factor model for international stock returns that includes size, value, and momentum factor portfolios and that builds them in a partial-segmentation capital market framework. Accounting for externalities driven by the incomplete accessibility to stocks and stock markets, our model not only captures strong common variation in international stock returns but also achieves low pricing errors and rejection rates relative to pure segmentation and pure integration models. This partial-segmentation approach is evaluated using monthly returns for over 37,000 stocks from 46 developed and emerging market countries over 2 decades and for a wide variety of test assets.
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Han, Shi-Zhuan, Li Zhang, Guang-Yu Han, and Lei Wang. "The Three-factor Model and China’s Multiple Stock Markets." Journal of International Commerce, Economics and Policy 10, no. 03 (October 2019): 1950016. http://dx.doi.org/10.1142/s1793993319500169.
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This paper aims at discussing the applicability of the three-factor model in China’s multiple security markets. The monthly returns of Shenzhen Main Board Market, Shanghai Stock Market, GEM Securities Market and Small and Medium Board Securities Market from January 2012 to December 2016 are selected as samples. The following conclusions are drawn: the three-factor model is applicable in Shenzhen Main Board Market, that is, the change of stock return is proportional to market factor, book-to-market ratio factor, and inversely proportional to scale factor. Moreover, in terms of the explanatory power of the change of stock return, the market factor is the highest, the scale factor is the second, and the book-to-market ratio factor is the lowest. But in the other three markets, the two-factors model that excludes the ratio of book market value can explain the change of stock return better. In addition, the explanatory power of market factor is better than scale factor.
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Olotu, Samuel Ibukun. "A multivariate LSTM-based deep learning model for stock market prediction." Applied and Computational Engineering 2, no. 1 (March 22, 2023): 965–73. http://dx.doi.org/10.54254/2755-2721/2/20220602.
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Stocks represent ownership in a company and a proportionate claim on its assets and earn-ings. Investors trade stocks via an exchange by buying at a price and selling at a higher price. Due to market volatility forecast it is a necessity for trading to determine the direc-tion of the stock price in order to maximize profit and minimize loss. Traditional methods of stock price predictions include technical and fundamental analysis. The technical deals with historical price movement while fundamental analysis uses the relationship between financial information about the company. However, these predictions methods sometimes fail to yield desired result sometimes due to the influence of factors such as national poli-cies, global and regional economics, psychological, human among many. This work propos-es a prediction model for stock market using LSTM algorithm. Multivariate time series stock price data is obtained from Nigerian Stock Exchange Index to implement the model. The experimental result of the technique is measured using MAPE, MAE, MSE and rRMSE performance metrics. The accuracy of the result shows that the proposed system outper-forms existing traditional and deep learning methods.
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Baumöhl, Eduard, Mária Farkašovská, and Tomáš Výrost. "Stock Market Integration: DCC MV-GARCH Model." Politická ekonomie 58, no. 4 (August 1, 2010): 488–503. http://dx.doi.org/10.18267/j.polek.743.
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Moolman, E., and C. Du Toit. "An econometric model of the South African stock market." South African Journal of Economic and Management Sciences 8, no. 1 (January 13, 2015): 77–91. http://dx.doi.org/10.4102/sajems.v8i1.1285.
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A wealth of literature exists concerning the modelling of stock markets, as well as the examination of the relationshiop between share price and various economic factors, both theoretically and empirically. However, most studies use data for developed countries in their analyses, while the literature moselling emerging stock markets in general, and the south African stock market in particular, is quite sparse. This study develops a structural theoretically founded model of the South African stock market that is estimated using co-integration and error-correction techniques. These techniques respectively estimate the long-term equilibrium or intrinsic value of the stock market, and the short-term fluctuations around the quilibrium level. According to the results, share prices are co-integrated with the variables dictated by the expected present value model of asset price determination. The short-term fluctuations are determined by various factors such as interest rates, a risk premium, the exchange rate, foreign stock market adn other variables.
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Fatima, Nudrat, Muhammad Waqas, Rameez Hassan, Ahmad Fraz, and Muhammad Arif. "Cash to Price Ratio & Stock Returns: Evidence from Emerging Markets." International Journal of Economics and Finance 9, no. 11 (October 23, 2017): 153. http://dx.doi.org/10.5539/ijef.v9n11p153.
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This study examines the impact of size premium and value premium on average return in emerging economies i.e. Pakistan, India and China equity markets for the period from June 2000 to June 2015 by using three factors model. This study predicts the significance and positive relationship between value premium(C/P Ratio) and stock return for all non-financial companies listed on Karachi stock exchange, Bombay stock exchange and Shanghai stock exchange on the basis of market Capitalization. The regression results of the study illustrate that size premium predict returns more for small firms than big firms while market premium found significantly positive with stock returns in Pakistan, India, and China. Value premium is found positive for all created portfolios. Therefore, it can be concluded that value effect is present in three emerging markets. High C/P ratio outperforms the low C/P ratio stocks. In this study C/P ratio (value premium) integrated with size and market premium to check whether it can predict stock returns of small and large firms for high or low C/P ratio. The finding is similar that the positive relationship of value premium and stock return and the negative relationship of size premium and stock return. The explanatory power of Fama and French three-factor model is greater than CAPM for all three equity markets, so, the asset pricing model can facilitate investors in efficient portfolio diversification for getting enhanced returns.
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(Pal), Suparna Nandy, and Arup Kr Chattopadhyay. "‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects." Journal of Emerging Market Finance 18, no. 2_suppl (June 21, 2019): S183—S212. http://dx.doi.org/10.1177/0972652719846321.
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The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multivariate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17
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Jarrett, Jeffrey E., and Janne Schilling. "DAILY VARIATION AND PREDICTING STOCK MARKET RETURNS FOR THE FRANKFURTER BÖRSE (STOCK MARKET)." Journal of Business Economics and Management 9, no. 3 (September 30, 2008): 189–98. http://dx.doi.org/10.3846/1611-1699.2008.9.189-198.
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In this article we test the random walk hypothesis in the German daily stock prices by means of a unit root test and the development of an ARIMA model for prediction. The results show that the time series of daily stock returns for a stratified random sample of German firms listed on the stock exchange of Frankfurt exhibit unit roots. Also, we find that one may predict changes in the returns to these listed stocks. These time series exhibit properties which are forecast able and provide the intelligent data analysts’ methods to better predict the directive of individual stock returns for listed German firms. The results of this study, though different from most other studies of other stock markets, indicate the Frankfurt stock market behaves in similar ways to North American, other European and Asian markets previously studied in the same manner.
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Zeng, Hongjun, and Ran Lu. "High-frequency volatility connectedness and time-frequency correlation among Chinese stock and major commodity markets around COVID-19." Investment Management and Financial Innovations 19, no. 2 (June 23, 2022): 260–73. http://dx.doi.org/10.21511/imfi.19(2).2022.23.
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This study examines the connectedness and time-frequency correlation of price volatility across the Chinese stock market and major commodity markets. This paper applies a DCC-GARCH-based volatility connectedness model and the cross-wavelet transform to examine the transmission of risk patterns in these markets before and during the COVID-19 outbreak, as well as the leading lag relationship and synergistic movements between different time domains. First, the findings of the DCC-GARCH connectedness model show dynamic total spillovers are stronger after the COVID-19 outbreak. Chinese stocks and corn have been net spillovers in the system throughout the sample period, but the Chinese market plays the role of a net receiver of volatility relative to other markets (net pairwise directional connectedness) in the system as a whole. In terms of wavelet results, there is some connection to the connectedness results, with all commodity markets, except soybeans and wheat, showing significant dependence on Chinese equities in the medium/long term following the COVID-19 outbreak. Secondly, the medium-to long-term frequency of the crude oil market and copper market are highly dependent on the Chinese stock market, especially after the COVID-19 outbreak. Meanwhile, the copper market is the main source of risk for the Chinese stock market, while the wheat market sends the least shocks to the Chinese stock market. The findings of this paper will have a direct impact on a number of important decisions made by investors and policymakers.
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Watanapalachaikul, Sethapong, and Sardar M. N. Islam. "Rational Speculative Bubbles in the Thai Stock Market: Econometric Tests and Implications." Review of Pacific Basin Financial Markets and Policies 10, no. 01 (March 2007): 1–13. http://dx.doi.org/10.1142/s0219091507000921.
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Understanding of factors like economic fundamentals or bubbles that normally determine the returns of stock in any emerging market such as the Thai stock market is essential for academic, investment planning and public policy reasons. An empirical study of the existence of rational speculative bubbles in the Thai stock market is undertaken by using the Weibull Hazard model. The conventional Weibull Hazard model is used as a benchmark model for other speculative bubble models. Empirical results suggest the presence of rational speculative bubbles in the Thai stock market, especially during the pre-crisis period. While rational speculative bubbles were not present immediately after the post-crisis period, some were observed a few years after the crisis. A possible explanation for such a result concerning rational speculative behaviour and bubbles in the emerging stock markets could be attributed to the presence of market imperfections in emerging stock markets, requiring institutional and policy developments to ensure efficient operation of the stock market.
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Gaytan, Jesus Cuauhtemoc Tellez, Aqila Rafiuddin, Gyanendra Singh Sisodia, Gouher Ahmed, and CH Paramaiah. "Pass-through Effects of Oil Prices on LATAM Emerging Stocks before and during COVID-19: An Evidence from a Wavelet -VAR Analysis." International Journal of Energy Economics and Policy 13, no. 1 (January 22, 2023): 529–43. http://dx.doi.org/10.32479/ijeep.13761.
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Vector Auto regression model (VAR) a time -varying parameter is applied to study the effect of oil price shocks on the returns of stocks in the LATAM (Latin American) markets. Coherent Wavelet analysis highlights possibilities of connectedness of the oil price and LATAM stock markets through the presence of different patterns in a time series. The structural demand shocks standard deviations during the COVID-19 era remain high and the pass-through effects on stock returns due to oil prices differ for different time frames. The fundamental linkages are demonstrated due to oil market specific demand. The main motive of the research work is to identify the influence of oil price on stocks and identify the fundamental source of contagion. A random effects model is applied to the panel data of LATAM markets with the Global stock market index, MSCI (Morgan Stanley Capital International World Index), domestic money market rates and currency exchange rates during the period of study, 15 March 2019 to 31 July 2021 with 684 observations of controlled non-observed characteristics from individual country. The findings of this research recommend the pass-through effect of the oil prices on the stock market returns are based on time frequency. The contribution of this paper helps the policy makers to restore the confidence amongst the investors in the stock markets and strategies to be adopted by the investors to mitigate the risk by ideal portfolio management.
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EFUNTADE, Olubunmi Omotayo, and Alani Olusegun, FCIB, ACA EFUNTADE. "Assessing Literatures on the Dependence of Stock Market Development on Upstream Oil Royalty Revenue and Systematic Risk Factors: Highlighting the Relevance of Dutch Disease Theory and Capital Asset Pricing Model." INTERNATIONAL JOURNAL OF SOCIAL SCIENCES AND MANAGEMENT RESEARCH 8, no. 3 (October 5, 2022): 1–30. http://dx.doi.org/10.56201/ijssmr.v8.no3.2022.pg1.30.
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The paper assessed past literatures on the dependence of stock market financial development on upstream oil royalty revenue account and systematic risk factors and evaluating the relevance of Dutch Disease theory and capital asset pricing model. Theoretical approach supports the role of oil royalty revenue vis-à-vis oil price volatility, foreign exchange rate risk, financial liquidity risk and interest rate risk in determining the performance and financial development of stock market. It is well documented in research that stock prices react to information on oil revenue, crude oil production, monetary policy interest rate, foreign exchange rate uncertainty and diaspora cash remittances. Traditional valuation of stock is based on capital asset pricing model but the modern relevance is in doubt due to anomalous performance of investors and behavioural sentiments. Stock markets of emerging economies over the past few decades have witnessed remarkable growth as indicated by the value and volume of trade in the markets along with the level of capital inflows from developed markets, thereby providing numerous opportunities for investments. The development of the stock market is vital as it provides more opportunities for greater mobilization of funds from with and diaspora remittances and better efficiency in resource allocation. Nevertheless, investment returns in the stock market of developing economies continue to be more reactive to changes in economic fundamentals due to their fragile and volatile nature. This makes them even more unpredictable and unstable unlike the stock markets of developed economies, which are known to be more stable. Stock market financial development is not symptomatic of Dutch Diseases based on the fact that Oil-induced Dutch Disease assumes an oil sector which largely contributes to the economy while so many vital systematic risk factors affects stock market development. Optimistically this study along with other previous and future studies will increase the knowledge about Dutch Disease and will contribute to lessen the harmful effects that Dutch Disease causes. In spite of the common perception that oil is extremely important for financial and economic dynamics, there is, unexpectedly, a dearth of research on how oil revenue and prices influence stock market financial development.
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Koldanov, A. P., P. A. Koldanov, and D. P. Semenov. "Confidence set for connected stocks of stock market." Journal of the New Economic Association 50, no. 2 (2021): 12–34. http://dx.doi.org/10.31737/2221-2264-2021-50-2-1.
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The problem of analysis of pairwise connections between stocks of financial market by observations on stock returns is considered. Such problem arise in stock market network analysis. It is assumed that joint distribution of stock returns belongs to the wide class of elliptical distributions. Classical Pearson correlation, Fechner correlation and Kendall correlation are used as measure of dependence. The construction problems of sets of stocks with strong connections between its returns are investigated. The construction problems of sets of stocks with strong connections between its returns are investigated. To construct such sets the multiple hypotheses testing procedures on values of correlations are used. The properties of these statistical procedures are investigated by simulations. The simulation results show that procedures based on individual Fechner and Kendall tests lead to such sets of stocks with given confidence probability unlike procedure based on Pearson individual tests which do not control the confidence probability. At the same time it is emphasized that for Student distribution the constructed set is nearly the same to the confidence set. The procedure of consistency testing with elliptical model is proposed and exemplified. The peculiarities of the model are discussed.
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Bundala, Ntogwa N. "Homo-Hetero Pairing Regression Model: An Econometric Predictive Model of Homo Paired Data." International Journal of Finance Research 3, no. 2 (July 31, 2022): 147–86. http://dx.doi.org/10.47747/ijfr.v3i2.792.
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The study aimed to examine the technical and fundamental hypotheses in NYSE, NASDAQ and S&P 500 stock exchange markets. The main determinants (variables) that were examined were stock trading volumes, closing stock prices and stock information available in the stock exchange market. The 240 days, 197 days and 253 days data of closing stock prices and trading volumes at NYSE, S&P500 and NASDAQ stock exchange markets were systematically collected from June 2021 to June 2022. The data was analysed by using the Homo-Hetero Pairing (HHP) Regression Model. This model was developed to detect the linear and non-linear behaviour of data. The study evidenced that both the technical and fundamental hypotheses in NYSE, S&P500 and NASDAQ stock exchange markets are defined by the inverse and S-curved models in two distinctive pairing classes called the positive-positive pairing (PPP) class and the negative-positive pairing (NPP) class. The study concluded that the optimal prediction of the stock price or return is achieved by the fundamentalists in the stock exchange markets. The study recommends that stock investors should priorities the use of the fundamental hypothesis to make their portfolio investment decision. Moreover, the study recommends the application of the HHP regression model in financial markets, economics, psychology, sociology, and medicine studies. In addition, the HHP regression model is recommended for the prediction of water waves in the investigation of hydrodynamic and erosion-accretion processes
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Lai Cao Mai, Phuong. "Corruption and stock market development in EAP countries." Investment Management and Financial Innovations 17, no. 2 (July 1, 2020): 266–76. http://dx.doi.org/10.21511/imfi.17(2).2020.21.
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Using macroeconomic factors as control variables, this paper examines the impact of corruption on the development of the stock market in East Asia and the Pacific (EAP) from 2008 to 2018. The research model uses GMM techniques to estimate panel data on two sub-sets of data, including five developed markets and seven emerging markets, and a dataset of both market groups. The market capitalization and the stock transaction value relative to GDP represent the development of the stock market, and the corruption control index represents the corruption factor. The empirical results found that corruption has a positive impact on the EAP stock market capitalization with the entire sample data set, which positively affects both size of the market capitalization value and value of stock transactions in underdeveloped markets. However, it is not statistically significant in explaining the development of developed stock markets. Besides, macroeconomic factors such as inflation, interest rates, savings, and credit affect some stock markets at EAP. Compared to previous studies, the article’s results found that corruption affects stock market capitalization and has a positive impact on stock liquidity in underdeveloped stock markets. Corruption affects more underdeveloped stock markets than developed stock markets. This may be due to the implicit relationship of economic benefits between large enterprises and officials in underdeveloped markets.
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Panda, Ajaya Kumar, and Swagatika Nanda. "Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies." International Journal of Managerial Finance 14, no. 2 (April 3, 2018): 245–62. http://dx.doi.org/10.1108/ijmf-11-2016-0206.
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Purpose The purpose of this paper is to capture the pattern of return volatility and information spillover and the extent of conditional correlation among the stock markets of leading South American economies. It also examines the connectedness of market returns within the region. Design/methodology/approach The time series properties of weekly stock market returns of benchmark indices spanning from the second week of 1995 to the fourth week of December 2015 are analyzed. Using univariate auto-regressive conditional heteroscedastic, generalized auto-regressive conditional heteroscedastic, and dynamic conditional correlation multivariate GARCH model approaches, the study finds evidence of returns and volatility linkages along with the degree of connectedness among the markets. Findings The findings of this study are consistent with increasing market connectedness among a group of leading South American economies. Stocks exhibit relatively fewer asymmetries in conditional correlations in addition to conditional volatility; yet, the asymmetry is relatively less apparent in integrated markets. The results demonstrate that co-movements are higher toward the end of the sample period than in the early phase. The stock markets of Argentina, Brazil, Chile, and Peru are closely and strongly connected within the region followed by Colombia, whereas Venezuela is least connected with the group. Practical implications The implication is that foreign investors may benefit from the reduction of the risk by adding the stocks to their investment portfolio. Originality/value The unique features of the paper include a large sample of national stock returns with updated time series data set that reveals the time series properties and empirical evidence on volatility testing. Unlike other studies, this paper uncovers the relation between the stock markets within the same region facing the same market condition.
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Wang, Kuan-Min, and Hung-Cheng Lai. "Which global stock indices trigger stronger contagion risk in the Vietnamese stock market? Evidence using a bivariate analysis." Panoeconomicus 60, no. 4 (2013): 473–97. http://dx.doi.org/10.2298/pan1304473w.
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This paper extends recent investigations into risk contagion effects on stock markets to the Vietnamese stock market. Daily data spanning October 9, 2006 to May 3, 2012 are sourced to empirically validate the contagion effects between stock markets in Vietnam, and China, Japan, Singapore, and the US. To facilitate the validation of contagion effects with market-related coefficients, this paper constructs a bivariate EGARCH model of dynamic conditional correlation coefficients. Using the correlation contagion test and Dungey et al.?s (2005) contagion test, we find contagion effects between the Vietnamese and four other stock markets, namely Japan, Singapore, China, and the US. Second, we show that the Japanese stock market causes stronger contagion risk in the Vietnamese stock market compared to the stock markets of China, Singapore, and the US. Finally, we show that the Chinese and US stock markets cause weaker contagion effects in the Vietnamese stock market because of stronger interdependence effects between the former two markets.
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Almasarweh, Mohammad, and S. AL Wadi. "ARIMA Model in Predicting Banking Stock Market Data." Modern Applied Science 12, no. 11 (October 29, 2018): 309. http://dx.doi.org/10.5539/mas.v12n11p309.
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Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.
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Bekiros, Stelios D. "A neurofuzzy model for stock market trading." Applied Economics Letters 14, no. 1 (January 20, 2007): 53–57. http://dx.doi.org/10.1080/13504850500425717.
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Bijoy, Kumar. "Stock and Currency Market Linkages: An Empirical Analysis from Emerging Economies." International Journal of Professional Business Review 8, no. 8 (August 9, 2023): e03357. http://dx.doi.org/10.26668/businessreview/2023.v8i8.3357.
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Purpose: The Causality relationship between forex and stock market in any economy is dependent on its economic fundamentals. This study examines return and volatility linkages between stock and currency markets for 28 emerging economies weekly MSCI stock index values (in local currency) and foreign exchange rates (indirect quotes) from 1988 to 2019. Theoretical Framework: The understanding of the relationship between forex and stock markets through return and volatility spillover will help in predicting behavior of one market on account of the knowledge of movements in another market. Impact of global financial crisis (GFC) on this relationship is another dimension of research. This study finds the causal relationship between forex and stock markets through return and volatility spillover for all emerging economies along with the effect of GFC on the relationship. Design/Methodology/Approach: The empirical analysis is conducted for the total period and three sub-periods namely pre-global financial crisis, crisis, and post-crisis periods by using the Granger Causality test (Granger 1969) followed by Vector Auto Regression (VAR) model and finally the Dynamic Conditional Correlations (DCC), a multivariate model proposed by Engle (2002). The volatility linkages are studied by employing BEKK-GARCH (Baba, Engle, Kraft, & Kroner, 1990) Findings: It is found that return spillovers are predominantly from Stock to forex markets during the pre-crisis and crisis period but from forex to stock market in the post-crisis period. The increasing presence of return relationships from 10 countries in the pre-crisis period to 19 countries during the crisis period, implying a contagion effect. The BEKK-GARCH result confirm that volatility spillovers are observed throughout from forex to stock markets. Research, practical & social implications: More extensive return and volatility associations between stock and forex market after the global financial crisis confirm the increasing importance of economic fundamentals. Return linkages exhibit contagion against the decoupling effect observed in volatility spillovers during the crisis period. Originality/Value: Based on empirical observations, the study attempts to provide important policy implications for Policy makers, global investors, and academic community.
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Li, Lili, Shan Leng, Jun Yang, and Mei Yu. "Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/1285768.
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We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7) and China. The quantile autoregression model (QAR) enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.
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Yang, Menglong, Qiang Zhang, Adan Yi, and Peng Peng. "Geopolitical Risk and Stock Market Volatility in Emerging Economies: Evidence from GARCH-MIDAS Model." Discrete Dynamics in Nature and Society 2021 (September 23, 2021): 1–17. http://dx.doi.org/10.1155/2021/1159358.
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Previous studies have found that geopolitical risk (GPR) caused by geopolitical events such as terrorist attacks can affect the movements of asset prices. However, the studies on whether and how these influences can explain and predict the volatility of stock returns in emerging markets are scant and emerging. By using the data from China’s CSI 300 index, we provide some evidence on whether and how the GPR factors can explain and forecast the volatility of stock returns in emerging economies. We employed the GARCH-MIDAS model and the model confidence set (MCS) to investigate the mechanism of GPR’s impact on the China stock market, and we considered the GPR index, geopolitical action index, geopolitical threat index, and different country-specific GPR indices. The empirical results suggest that except for a few emerging economies such as Mexico, Argentina, Russia, India, South Africa, Thailand, Israel, and Ukraine, the global and most of the regional GPR have a significant impact on China’s stock market. This paper provides some evidence for the different effects of GPR from different countries on China’s stock market volatility. As for predictive potential, GPRAct (geopolitical action index) has the best predictive power among all six types of GPR indices. Considering that GPR is usually unanticipated, these findings shed light on the role of the GPR factors in explaining and forecasting the volatility of China’s market returns.
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Su, Ziyi, Chenyu Xu, and Yutong Zheng. "Optimal Investment Portfolio under Different Models with Various Constraints Especially Considers COVID-19 Period." BCP Business & Management 16 (December 26, 2021): 214–22. http://dx.doi.org/10.54691/bcpbm.v16i.305.
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The outbreak of COVID-19 at the end of 2019 had a severe impact on global economic markets, with the U.S. stock market experiencing four circuit breakers in one month. As of July 2021, the real GDP of the United States has significantly outpaced the growth rate of the world's advanced economies. In order to study how investors invest in the stock market after the U.S. stock market experience circuit breaker, this paper selects six stocks as the research object using the monthly closing prices from May, 2001 to May, 2021 as sample data, and calculates the optimal portfolio by Markowitz model and Index model. Through calculation and constraint of different conditions, we obtain the variance and Sharpe ratio of the six selected sample stocks under the two conditions of minimum variance portfolio and maximum Sharpe ratio portfolio respectively. In the portfolio based on Markowitz model and Index model, we can draw a conclusion that Procter & Gamble Co. accounts for a larger proportion under different constraints. Different constraints will also lead to different results. In most cases, a board index is included, and the optimization constraints brought by it can make the portfolio return under Markowitz model and Index model reach the maximum. If the constraints of FINRA regulation T are taken into account, the Sharpe ratio values can be higher.
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Teodorovic, Natasa. "Liquidity, price impact and trade informativeness: Evidence from the London stock exchange." Ekonomski anali 56, no. 188 (2011): 91–123. http://dx.doi.org/10.2298/eka1188091t.
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The rapid development of electronic trading has significantly changed stock exchange markets. Electronic systems providing trading processes have defined a new stock market environment. Such a new environment requires trading process redefinition (generally defined as algorithmic trading), as well as redefinition of well known microstructure hypotheses. This paper conducts standard Hasbrouck?s (1991a, 1991b) market microstructure time series analysis to examine adverse selection and information asymmetry issues on diverse liquidity leveled stocks listed on the London Stock Exchange, which is a market with a significant algorithmic trading share. Based on the results obtained from the considered sample, this paper suggests that the contribution of unexpected trade in the volatility of the efficient price is larger for intensively traded stocks, arguing that Hasbrouck?s (1991a, 1991b) model recognizes algorithmic trading as an unexpected trade, i.e. as a trade caused by superior information.
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Ren, Zhiyuan. "What might happen to the global stock market after Brexit?" Studies in Economics and Finance 39, no. 2 (February 3, 2022): 177–92. http://dx.doi.org/10.1108/sef-09-2020-0392.
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Purpose The stock market is vulnerable to various exogenous factors, and its fluctuations can reflect the effects of political, economic and market factors. The purpose of this paper is therefore to choose the stock market as a representative to analyze the potential impact of the Brexit event on global financial markets and how to prevent the spread of risks across global financial markets. Design/methodology/approach This study chooses the auto-regressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model to fit the financial series and uses it as the marginal distribution model to establish the vine copula model. The maximum spanning tree algorithm is used to select the optimal rattan structure model and pair-copula function. According to the final ARMA-GARCH-R-vine copula model, the tail correlation coefficients of the UK, France, Germany, USA and China stock markets are calculated and used to analyze their dependence structure. Findings The negative impact of the Brexit event on the British stock market is greater and is more likely to be transmitted to France and Germany. China and the USA are less likely to be impacted by the Brexit incident. The US financial market is more closely linked to France, and it may benefit from the Brexit incident due to the impact of the exchange rate. Although the Chinese stock market is directly connected to the British stock market, due to the existence of national macro-controls and other factors, it will be less affected by the Brexit incident. The main impact comes from the dual devaluation pressure on the RMB. Originality/value This paper selects the optimal combination model based on actual data, and the results obtained can accurately reflect the interdependence between relevant stock markets and can guide risk aversion in the financial investment field.
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TAJ EL-DIN, SEIF EL-DIN. "Towards an Islamic Model of Stock Market." Journal of King Abdulaziz University-Islamic Economics 14, no. 1 (2002): 3–29. http://dx.doi.org/10.4197/islec.14-1.1.
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Tchereni, Betchani, and Songezo Mpini. "Monetary policy shocks and stock market volatility in emerging markets." Risk Governance and Control: Financial Markets and Institutions 10, no. 3 (2020): 50–61. http://dx.doi.org/10.22495/rgcv10i3p4.
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This paper examines the effect of monetary policy decisions on stock markets in emerging economies particularly South Africa for the period 2000Q1 to 2016Q4. This is important as the monetary authorities would understand how their decisions may cause reactions to the stock market. Monetary policy directly shocks money supply and repo rate and indirectly GDP and inflation among many macroeconomic variables. A hypothesis that stock markets do not respond to monetary policy determinations is formulated and tested using a two-stage approach by employing first the vector error correction model to determine the long-run relationship of the variables and secondly GARCH (1, 2) model to determine the volatility. And the results suggest that about 5.2% variations in the Johannesburg Stock Exchange (JSE) volatility are due to monetary policy shocks. Overall, there is a negative relationship between M2 and stock market volatility. However, there is a positive link between repo rate and JSE volatility, which is not economically preferable because variations in repo rate influence the aggregate demand of investment on securities. The study recommends that the Monetary Policy Committee an expansionary monetary policy of keeping the repo rate lower must be pursued in order to increase borrowing that makes the public to have money to make transactions in securities on the financial market.
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Salisu, Afees A., Rangan Gupta, and Riza Demirer. "Oil Price Uncertainty Shocks and Global Equity Markets: Evidence from a GVAR Model." Journal of Risk and Financial Management 15, no. 8 (August 9, 2022): 355. http://dx.doi.org/10.3390/jrfm15080355.
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This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while simultaneously accounting for individual-country peculiarities. Utilising a recently developed model-free, robust estimate of oil price uncertainty, we document a statistically significant and negative effect of uncertainty shocks emanating from oil prices on the large majority of global stock markets, with the adverse effect of oil price uncertainty shocks found to be stronger for emerging economies as well as net oil-exporting nations. Interestingly, however, global stock markets exhibit a great deal of heterogeneity in their recovery following oil uncertainty shocks as some experience rapid corrections in stock valuations while others suffer from extended slumps. While the results are sensitive to the oil uncertainty measure utilised, they suggest that country diversification in the face of rising oil market uncertainty can still be beneficial for global investors as global stock markets exhibit a rather heterogeneous pattern in their recovery rates against oil market shocks.
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Guo, Hui. "Limited Stock Market Participation and Asset Prices in a Dynamic Economy." Journal of Financial and Quantitative Analysis 39, no. 3 (September 2004): 495–516. http://dx.doi.org/10.1017/s0022109000004002.
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AbstractThis paper presents a consumption-based model that explains the equity premium puzzle through two channels. First, because of borrowing constraints, the shareholder cannot completely diversify his income risk and requires a sizable risk premium on stocks. Second, because of limited stock market participation, the precautionary saving demand lowers the risk-free rate but not stock return and generates a substantial liquidity premium. This model also replicates many other salient features of the data, including the first two moments of the risk-free rate, excess stock volatility, stock return predictability, and the unstable relation between stock volatility and the dividend yield.
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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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Santi Singagerda, Faurani, Linda Septarina, and Anuar Sanusi. "The volatility model of the ASEAN Stock Indexes." Investment Management and Financial Innovations 16, no. 1 (March 18, 2019): 226–38. http://dx.doi.org/10.21511/imfi.16(1).2019.18.
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This research study examines the characteristics of the Association of Southeast Asian Nations (ASEAN) volatility of stock indexes. The following models are used in this research: Generalized Autoregressive Conditional Heteroscedasticity (GARCH), Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Fractionally Integrated Generalized Autoregressive Conditional Heteroscedasticity (FIGARCH), Glosten Jaganathan Runkle Generalized Autoregressive Conditional Heteroscedasticity (GJR-GARCH), and Multifractal Model of Asset Return (MMAR). The research also used the data from the ASEAN country members’ (the Philippines, Indonesia, Malaysia, Singapore, and Thailand) stock indexes for the period from January 2002 until 31 January 2016 to determine the suitable model.Meanwhile, the results of the MMAR parameter showed that the returns of the countries have a characteristic called long-term memory. The authors found that the scaling exponents are associated with the characteristics of the specific markets including the ASEAN member countries and can be used to differentiate markets in their stage of development. Finally, the simulated data are compared with the original data by scaling function where most of the stock markets of the selected ASEAN countries have long-term memory with the scaling behavior of information asymmetry. Some of the countries such as the Philippines and Indonesia have their own alternative models using GARCH and EGARCH due to the possibility of leverage. Generally, MMAR is the best model for use in ASEAN market, because this model considered Hurst exponent as a parameter of long-term memory that indicates persistent behavior.
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Ji, Xiuping, Sujuan Wang, Honggen Xiao, Naipeng Bu, and Xiaonan Lin. "Contagion Effect of Financial Markets in Crisis: An Analysis Based on the DCC–MGARCH Model." Mathematics 10, no. 11 (May 25, 2022): 1819. http://dx.doi.org/10.3390/math10111819.
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Global crises have created unprecedented challenges for communities and economies across the world, triggering turmoil in global finance and economy. This study adopts the dynamic conditional correlation multiple generalized autoregressive conditional heteroskedasticity (DCC–MGARCH) model to explore contagion effects across financial markets in crisis. The main findings are as follows: (1) the financial crisis and COVID-19 pandemic intensified the connection between the Chinese and US stock markets in the short term; (2) the dynamic conditional correlations (DCCs) during the COVID-19 pandemic are higher than those during the 2008 financial crisis owing to the further opening of the Chinese capital market, and financial institutions’ investments in the European market are higher than those in the American markets; (3) a stepwise increase is observed in the dynamic conditional correlation between the returns on the S&P 500 Index and SSEC during and after the onset of a destructive crisis; and (4) a unidirectional contagion effect exists between the Chinese market and US market, and the Hong Kong stock market contributes to the risk spillover. Effective transmission channels of external negative shocks may be investors’ sentiments, financial institutions, and the RMB exchange rate in the stock markets. This study provides useful suggestions to authorities formulating financial regulations and investors diversifying risk investments.
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Gokcan, Suleyman. "Dynamic model of stock market integration between emerging and developed markets." International Advances in Economic Research 3, no. 3 (August 1997): 330. http://dx.doi.org/10.1007/bf02294931.
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Audrino, Francesco, Robert Fernholz, and Roberto G. Ferretti. "A Forecasting Model for Stock Market Diversity." Annals of Finance 3, no. 2 (June 10, 2006): 213–40. http://dx.doi.org/10.1007/s10436-006-0046-y.
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Amini, Sasan, Mohammad Nazaripour, and Mohamad Karimi Poya. "Review of Accounting and Economic Standards in Predicting Stock Returns in Tehran Stock Exchange." International Letters of Social and Humanistic Sciences 40 (September 2014): 82–94. http://dx.doi.org/10.18052/www.scipress.com/ilshs.40.82.
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Share return is one of those complicated abstraction which is interested by investors and decision makers the share return prediction is a vital issue which has involved the financial analyzers mind to itself. Various theories include capital assets pricing model (CAPM), factorial models (FM), Arbitrage model, Technical model (TM), and fundamental analyses (FA) for purpose of share return prediction and recognition have been discussed. The share return fundamental analyze is a function of massive economical condition, Industry position, and particular conditions of the firm. The particular conditions of the firm are consist of financial position and performance which presented in the essential financial statement from. The accounting science acclaims it prepares useful information for decision makers, so one of the information usefulness criteria of accounting and economical information usefulness through return share choice for prediction. Communication creation between economical and accounting criteria is the other goal, thus in the case of this result obtaining it can use of economical and accounting analyses for economical return and value extraction. The research location zone includes of accepted firms in Tehran exchange market, the temporality zone is the gap between 1386 and 1390, too. The research is consist of two independent varieties (economical criteria and accounting criteria), dependent variety (share return). This research is consisting of a direct hypothesis and 6 indirect ones. in the consideration of presented statistical analyses and total summery of research hypothesis assessment, stood over research independent varieties meaningful relation with share return prediction in existing firm performance measurement in Tehran capital exchange market during 2007 and 2011 is existed, between this research proportion is return of assets, earning per share, economical add value ratio, retained earning ratio with positive quantity market add value with negative quantity which statistically shows a meaningful relation with active firms share return in Tehran market. The economical add value ratio has the biggest relation with the share return prediction. So the first hypothesis in this research is (return of assets ratio), secondly (earning per share), thirdly (economical add value), fourthly (market add value) and sixthly (retained earnings) being emphasized.
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Gay, Robert D. "Effect Of Macroeconomic Variables On Stock Market Returns For Four Emerging Economies: Brazil, Russia, India, And China." International Business & Economics Research Journal (IBER) 15, no. 3 (May 2, 2016): 119–26. http://dx.doi.org/10.19030/iber.v15i3.9676.
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The relationship between share prices and macroeconomic variables is well documented for the United States and other major economies. However, what is the relationship between share prices and economic activity in emerging economies? The goal of this study is to investigate the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. Although no significant relationship was found between respective exchange rate and oil price on the stock market index prices of either BRIC country, this may be due to the influence other domestic and international macroeconomic factors on stock market returns, warranting further research. Also, there was no significant relationship found between present and past stock market returns, suggesting the markets of Brazil, Russia, India, and China exhibit the weak-form of market efficiency.
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Nisha, Nabila. "Stock Market and Macroeconomic Behavior." International Journal of Applied Behavioral Economics 5, no. 2 (April 2016): 12–30. http://dx.doi.org/10.4018/ijabe.2016040102.
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An impressive body of research has documented that movement in stock prices are highly sensitive to changes in the macroeconomic variables of an economy. Past empirical studies have examined this relationship across different stock markets by either outlining the influence of only domestic factors or a few global variables. A recent phenomenon has been the shift of academic interest to the emerging economies to investigate this presumed linkage by focusing more on global factors due to the trend of globalization. The aim of this paper is therefore to examine the influence of only global macroeconomic factors upon stock returns in the emerging stock market of Pakistan. By employing Vector Error Correction Model (VECM), findings indicate that significant influence of the global macroeconomic factors of the international interest rates and the world price index is observed, which implies a gradual integration of KSE towards the global financial markets. Limitations and implications for practice and research are also discussed.
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Pruchnicka-Grabias, Izabela. "Interdependence between WTI Crude Oil Prices and the US Equity Market." International Journal of Energy Economics and Policy 12, no. 2 (March 20, 2022): 226–32. http://dx.doi.org/10.32479/ijeep.12675.
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The author checks the cointegration between WTI oil market and the US market of stocks represented by the S&P index. As it turns out not to exist, short-term relations are investigated. The study confirms that crude oil market significantly influences the stock market in the short run, however it does not give an unambigous answer if this impact is made by oil itself or together with the GBP/USD currency rate. Furthermore, the relation does not go in another direction which means that the stock market has no impact on the oil market. The Unrestricted Vector Autoregression model is built. The author uses weekly data and the research period is from April 1990 to May 2021. The study implies that stable crude oil prices are desirable in order not to destabilize stock markets whose instability threatens the real economy. Conclusions are vital for a wide group of entities such as policy makers, authorities, institutional and individual investors, as well as other financial market participants.
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Lin, Shu-Shian. "INVESTIGATION OF FORECASTED RISK INTERRELATIONSHIP: BASE ON GARCH MODEL, CAUSALITY IN CHINA MARKETS." Journal of Business Economics and Management 15, no. 5 (November 27, 2014): 853–61. http://dx.doi.org/10.3846/16111699.2013.839474.
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This paper used data from the Shenzhen and Shanghai stock markets to simulate the adjusted volatility, and applied time series methods to realize the relationships of the volatilities between the two markets. The unit root test, and co-integration analysis to show whether it exists equilibrium relationship. The result showed that it presented the co-integrated vectors between the volatilities of Shanghai and Shenzhen Stock Exchanges during the research period, and it made the regression more meaningful. Finally, it also showed that the volatility exerted one way influence between these two markets. It significantly rejected for a null hypothesis of Shanghai stock market does not granger caused Shenzhen stock market, and the results of simulated volatilities were consistent with the results in reality.
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Youssef, Manel, and Khaled Mokni. "Do Crude Oil Prices Drive the Relationship between Stock Markets of Oil-Importing and Oil-Exporting Countries?" Economies 7, no. 3 (July 10, 2019): 70. http://dx.doi.org/10.3390/economies7030070.
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The impact that oil market shocks have on stock markets of oil-related economies has several implications for both domestic and foreign investors. Thus, we investigate the role of the oil market in deriving the dynamic linkage between stock markets of oil-exporting and oil-importing countries. We employed a DCC-FIGARCH model to assess the dynamic relationship between these markets over the period between 2000 and 2018. Our findings report the following regularities: First, the oil-stock markets’ relationship and that between oil-importing and oil-exporting countries’ stock markets themselves is time-varying. Moreover, we note that the response of stock market returns to oil price changes in oil-importing countries changes is more pronounced than for oil-exporting countries during periods of turmoil. Second, the oil-stock dynamic correlations tend to change as a result of the origin of oil prices shocks stemming from the period of global turmoil or changes in the global business cycle. Third, oil prices significantly drive the relationship between oil-importing and oil-exporting countries’ stock markets in both high and low oil-stock correlation regimes.
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G.C., Surya Bahadur. "Volatility Analysis of Nepalese Stock Market." Journal of Nepalese Business Studies 5, no. 1 (July 26, 2009): 76–84. http://dx.doi.org/10.3126/jnbs.v5i1.2085.
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Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements. Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, it is important to understand the pattern of stock market volatility. In the paper, the volatility of the Nepalese stock market is modeled using daily return series consisting of 1297 observations from July 2003 to Feb 2009 and different classes of estimators and volatility models. The results indicate that the most appropriate model for volatility modeling in Nepalese market, where no significant asymmetry in the conditional volatility of returns was captured, is GARCH(1,1). The study revealed strong evidence of time-varying volatility, a tendency of the periods of high and low volatility to cluster and a high persistence and predictability of volatility in the Nepalese stock market.Key words: Conditional heteroskedasticity, ARCH, GARCH, volatility clustering, leverage effect, Nepalese Stock MarketThe Journal of Nepalese Business Studies Vol. V, No. 1, 2008, December Page: 76-84
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Nguyen, Canh Phuc, Thanh Dinh Su, Udomsak Wongchoti, and Christophe Schinckus. "The spillover effects of economic policy uncertainty on financial markets: a time-varying analysis." Studies in Economics and Finance 37, no. 3 (June 12, 2020): 513–43. http://dx.doi.org/10.1108/sef-07-2019-0262.
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Purpose This study aims to examine the spillover effects of trans-Atlantic macroeconomic uncertainties on the local stock market returns in the USA and eight selected European countries, namely, Germany, France, Spain, Italy, Greece, Ireland, Sweden and the UK, during the 2000-2019 period. Design/methodology/approach This paper applies the dynamic conditional correlation multivariate GARCH model (i.e. multivariate generalized autoregressive conditional heteroskedasticity model or DCC MGARCH) to examine the potential existence of the spillover from the uncertainty of the USA to EU stock markets and vice versa. To capture different dynamic relationships between multiple time-series variables following different regimes, this paper applies the Markov switching model to the stock returns of both the USA and the eight major stock markets. Findings The increases in US uncertainty have significant negative impacts on all EU stock returns, whereas only the increases in the uncertainties of Spain, Ireland, Sweden and the UK have significant negative impacts on US stock returns. Notably, the economic policy uncertainty (EPU) in the USA has a dynamic effect on the European stock markets. In a bear market (State 1), the increases in the EPU of the USA and EU have significant negative impacts on EU stock returns in most cases. However, only the increase in US EPU has significant negative impacts on EU stock returns in bull markets (State 2). Reciprocally, the increases in the EU EPUs of Germany, Spain and the UK have significant impacts on US stock returns in bear market. Originality/value The observations challenge the conventional wisdom according to which only larger economies can lead the smaller counterparts. The findings also highlight the stronger dependence of the US stock market on international macroeconomic uncertainty.
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GULKO, LES. "THE ENTROPY THEORY OF STOCK OPTION PRICING." International Journal of Theoretical and Applied Finance 02, no. 03 (July 1999): 331–55. http://dx.doi.org/10.1142/s0219024999000182.
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An informationally efficient price keeps investors as a group in the state of maximum uncertainty about the next price change. The Entropy Pricing Theory (EPT) captures this intuition and suggests that, in informationally efficient markets, perfectly uncertain market beliefs must prevail. When the entropy functional is used to index the market uncertainty, then the entropy-maximizing market beliefs must prevail. The EPT resolves the ambiguity of asset valuation in incomplete markets, notably, the valuation of derivative securities. We use the EPT to derive a new stock option pricing model that is similar to Black–Scholes' with the lognormal distribution replaced by a gamma distribution. Unlike the Black–Scholes model, the gamma model does not restrict the dynamics of the stock price or the short-term interest rate. Option replication based on the gamma model accounts for random changes in the stock price, price volatility and interest rates.
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Yuvaraj, K., Dr J. Sreerambabu, and S. Kalidasan. "Trading View API and Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 978–81. http://dx.doi.org/10.22214/ijraset.2022.46313.
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Abstract: Stock market may be a market that permits seamless exchange of shopping for and commercialism of company stocks. each stock market has their own index price. Index is that the average price that's calculated by combining many stocks. Everyday billions of bucks ar listed on the exchange, ANd behind every greenback is an capitalist hoping to profit in a method or another. Entire corporations rise and fall daily supported the behaviour of the market. ought to AN capitalist be able to accurately predict market movements, it offers a tantalizing guarantees of wealth and influence.This helps in representing the complete securities market and predicting the market’s movement over time. The Equity market will have a profound impact on folks and also the country’s economy as an entire. Therefore, predicting the stock trends in an efficient manner will minimize the danger of investment and maximize profit.In our paper, we tend to ar victimisation the statistic prognostication methodology for predicting and visualizing the predictions. Our focus for prediction are going to be supported the technical analysis victimisation historic knowledge and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively within the field of finance and economic science because it is understood to be sturdy, economical and contains a robust potential for short share prediction
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Hua, Chang-I. "International Real Estate Review." International Real Estate Review 20, no. 4 (December 31, 2017): 397–416. http://dx.doi.org/10.53383/100248.
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This paper presents a structural model of nine equations that connect the unobserved housing service and the observed house transaction markets. Endogenous variables include two prices, supply, demand, stock of houses for sale on the market, average time on the market, stock of all houses, total vacant houses, and average house size. The search process of households for houses generates a stochastic process which results in an uncleared stock of houses on the market. The friction cost is specifically measured. The model should improve many practices in housing market research, and may be extended to other durable goods markets and beyond.
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Hadi Utomo, Sugeng, Dwi Wulandari, Bagus Shandy Narmaditya, Puji Handayati, and Suryati Ishak. "Macroeconomic factors and LQ45 stock price index: evidence from Indonesia." Investment Management and Financial Innovations 16, no. 3 (October 2, 2019): 251–59. http://dx.doi.org/10.21511/imfi.16(3).2019.23.
Full textAbstract:
This paper provides the relationship between macroeconomic variables, including exchange rate, BI rate and inflation, and stocks performance, particulary bluechip stocks listed in LQ45 index in Indonesia Stock Exchange. The study particularly gives insights on bluechip stocks listed in LQ45 stock price index in Indonesia Stock Exchange between 2015 and 2017. The data were obtained from various sources during the period, including the Indonesia Stock Exchange (IDX), the Central Bank of Indonesia (BI), and the Ministry of Trade. This study followed a Vector Error Correction Model (VECM) attempting to estimate the relationship between variables both in the short term and in the long term. The findings of the study showed that in the long run, exchange rate, BI rate and inflation have a negative impact on stock market performance, particularly on LQ45 index in Indonesia Stock Exchange. It implies that an increase in macroeconomic variables results in the decline of stock market performance. Meanwhile, in the short run, two variables, namely the exchange rate and inflation, positively affect stock market performance in Indonesia. On the contrary, the relationship between BI rate and stock market performance showed a negative correlation. These findings have significant implication for the understanding of how macroeconomic variables affect the stock market performance, particularly LQ45 price index in Indonesia Stock Exchange.