Asian Stock Indices Research Articles

Examining Cryptocurrency Dynamics with Asian Equity Indices and Commodities: A Wavelet Coherence Analysis

A wide range of interest has been shown in this field by investors and decision-makers due to the correlation between cryptocurrencies, Asian stock indexes, and commodities in the global financial market through partitioning the data and applying the wavelet analysis approach to analyze the described movement of various variables. Moreover, this study seeks to provide information to fintech financiers and policymakers as well about the examined variables. Time-series data was obtained from 01st January 2019 to 31st December 2023 and analyzed from multiple internet sources about commodities, Asian Stock indices, and cryptocurrency prices. To find specific patterns and trends in a certain area, research used the wavelet approach to analyze the data and separate each series into a separate frequency band. The findings showed that there is a highly significant relationship among commodities, Asian stock indexes, and cryptocurrency in different frequency bands. On the other hand, a negative correlation was found in low frequency bands between the prices of commodities and cryptocurrency, whilst a positive correlation was found in high frequency bands between cryptocurrency and Asian Stock indices.

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

Risk Spillover of Russia-Ukraine War and Oil Price on Asian Islamic Stocks and Cryptocurrency: A Quantile Connectedness Approach

This paper makes an initial attempt to investigate the risk spillover of the Russia-Ukraine war and oil price on Asian Islamic Stocks and bitcoin. We apply quantile-based connectedness measures using daily return data covering four Asian Islamic stock indices–oil, gold, bitcoin, and war panic–from February 1, 2022, to July 15, 2022. The results indicate higher connectedness in the upper and lower quantiles compared to the middle quantile, which implies that return shocks react more sharply during high war panic.

The effect of Hungry Ghost Festival (HGF) on stock market returns and volatility: An empirical analysis of Asian stock markets

This study examines the effect of the Hungry Ghost Festival (HGF) on stock returns and volatility in five Asian stock indices. Shanghai demonstrated the highest average return and dispersion, while Singapore had the lowest. During the HGF, all countries’ kurtosis spiked, except for Philippines and Shanghai, signalling a high-risk investment with high returns. Finally, the current volatility of daily stock returns persists across countries and is influenced by previous shocks. Investment during the HGF may appear riskier and more volatile in some countries, but there is inconclusive evidence to conclude that the HGF had a significant effect on all markets.

Asian Stock Index Price Prediction Analysis Using Comparison of Split Data Training and Data Testing

This study implements stock index price predictions using the LSTM method, where one of the processes in data management before running with the LSTM method is data split. This study also looks for the most appropriate split data ratio in predicting stock index prices to minimize error rates and differences in forecasted prices and original prices because in previous studies there were several rules of thumb in dividing data, so it is necessary to compare the most appropriate ratios in this research. Based on the evaluation process, the error value was found from nine split data ratios that were run by five ratios which produced a predictive graph line shape that resembled the validation line. Three datasets, namely split data ratios of 80:20, 70:30, and 60:40, are the ratios that get the lowest error values based on the RMSE, MSE, MAPE, and MAE values in the five stock index datasets. The three ratios are then compared again by looking at the average percentage difference between the validation price and the predicted price for the next working day, and it is found that the ratio of 80:20 is the most suitable split data ratio for predicting the stock index price for the next working day, with a level of difference in the average value between the original price and the predicted price on the stock index of 1.3%. While the ratio of 70:30 has an average predicted value of five stock index datasets of 1.9% and a ratio of 60:40 of 1.8%.

Co-Movement of Digital Currencies and Asian Stock Indexes: A Wavelets Based Multiresolution Analysis

The paper examines the co-movement relationship between Cryptocurrency and Asian major indices through Wavelet Coherence multiresolution technique and the Granger causality for result robustness. The indices data of Singapore and Japan stock market was used from January 2018 to December 2022, the outcome of our study shows great of co-relationship in the movement between Asian two major listed equity market indices and Cryptocurrencies investments. Moreover, the study shows that Japan stock market found to be more positively correlated with existing cryptocurrencies in comparison to Singapore stock market.

Effect of Macro-Economic, Commodity Market, and Asian Stock Index on Composite Stock Price Index, 2018-2022

The purpose of this research is to analyze effect of macro-economic, commodity market, and asian stock index on composite stock price index, 2018 to 2022. This type of research is descriptive quantitative. The type of data in this study is time series data taken from January 2018 to December 2022 so that a target population of 60 (5 years x 12) monthly report data is obtained for the sample data in this study. This is due to research that the sample used uses census research techniques. Data collection techniques through library research and field survey. The data analysis used in the study uses error correction model (ECM). The results show that in long and short term, inflation has a negative and significant effect on composite stock price index. Exchange rate has a negative and insignificant effect on composite stock price index. Interest rate has a negative and significant effect on composite stock price index. World oil price has a negative and significant effect on composite stock price index. Gold price has a negative and significant effect on composite stock price index. Nikkei 225 has a positive and insignificant effect on composite stock price index. KLCI has a negative and insignificant effect on composite stock price index. KOSPI has a negative and insignificant effect on composite stock price index. Hang seng in long term has a negative and insignificant effect on composite stock price index, but in short term has a positive and insignificant effect on composite stock price index. Keywords: Macro-Economic, Commodity Market, Asian Stock Index, Composite Stock Price Index

Comovement of stock markets pre- and post-COVID-19 pandemic: a study of Asian markets

PurposeThe study aims is to explore the cointegration level among major Asian stock indices from pre- COVID-19 to post COVID-19 times.Design/methodology/approachJohansen cointegration test is employed to know the long run relationship among the stock market indices of Hong Kong, Indonesia, Malaysia, Korea, India, Japan, China, Taiwan, Israel and South Korea. The empirical testing was done to analyze whether any significant change has been induced by the COVID-19 pandemic on the cointegrating relationship of the selected markets or not. Through statistics of trace test and maximum eigen value, total number of cointegrating equations present among all the indices during different study periods were analyzed.FindingsThe presence of cointegration was found during all the sample periods and the findings suggests that the selected stock markets are associated with each other in general. During COVID-19 crisis period the cointegration level was reduced and again it regained its original level in the next year and again reduced in the subsequent next year. So, the cointegrating relationship among selected stock market indices remains dynamic and no evidence of impact of COVID-19 on this dynamism was found.Originality/valueThe study has explored the level of cointegration among the major stock indices of Asian nations in the pre, during, post-crisis and the most recent periods. The interconnectedness of the stock markets during the COVID-19 times has been compared with similar periods in different years immediately preceding and succeeding the COVID-19 times which has not been done in any of the existing study.

The Impact of Ukrainian Crisis on the Connectedness of Stock Index in Asian Economies

The main aim of this study is to measure the dynamic connectedness and spillover effects among emerging stock markets in Asia and the developed stock markets of the US and Europe in the ongoing Ukrainian crisis. The paper also aims to provide a comparative analysis of return and volatility spillovers during the global financial crisis in 2008, the COVID-19 pandemic, and the Ukrainian crisis. This paper utilizes the multiple structural beak test of Bai & Perron (2003) and also depicts the risk and return transmissions among these markets using the Diebold & Yilmaz (2012) method. The main outcomes of this study indicate that the stock markets in Asia are less affected by the political crisis in Ukraine as compared to the previous effects during the GFC and COVID-19 periods. The results also show that sensitivity of Asian financial markets to global shocks has been weakened in the wake of the Ukrainian crisis in favour of increased resilience of Asian stock indices to external shocks. These results carry an important implication for international and local investors as well as for policy makers in Asia, where investors have greater potentials for portfolio diversify and risk reduction across Asian markets. Doi: 10.28991/ESJ-2023-07-02-04 Full Text: PDF

Pengaruh Indikator Makroekonomi, Harga Minyak Dunia Dan Indeks Bursa Asia Terhadap IHSG Periode 2015-2019

This research aims to examine the effect of macroeconomic indicators (BI Rate, Inflation, and Gross Domestic Product), Crude Oil Prices, and Asian Stock Indexes (Nikkei 225 Index and Straits Times Index) on the Composite Stock Price Index (CSPI). This study uses monthly data with a total of 44 samples during The Fed Fund Rate's hike period from December 2015 to July 2019. Multiple linear regression is used for data analysis with IBM SPSS 23 software. The partial test result shows that the exchange rate has a negative influence on CSPI while BI Rate, Inflation, GDP, Crude Oil Prices, Nikkei 225 Index, and Straits Times Index do not influence CSPI movement. BI Rate doesn't affect because investors prefer stock returns over bank interest. Inflation doesn't affect because the increase in inflation is relatively low. GDP doesn't influence because the unequal income level makes people not interested in investing. The crude Oil price has no effect because the shares of oil and gas mining sub-sector companies are not attractive to investors. Nikkei 225 index doesn't influence because the economic situation between Japan and Indonesia is not related. The Straits Times Index doesn't influence because the number of domestic investors is more than foreign investors.

Do stock market fluctuations lead to currency deflation in the South Asian region? Evidence beyond symmetry

AbstractThis study explores the role of the asymmetrical influence of stock market reaction to the fluctuations in US dollars against the domestic currencies of the South Asian region. The symmetrical Panel based autoregressive distributed lag model (PARDL) model and panel‐based non‐linear autoregressive distributed lag model (NARDL) model with the Pooled Mean Group Approach (PMG) approach were employed to estimate the impact of stock market bullish and bearish behaviour on local currency variabilities of the South Asian region. The results show that during the pre‐economic recession regime, negative fluctuations in stock indexes had a direct association with exchange rate fluctuations for a longer period. However, after the crisis, both negative and positive stock index shocks have formulated an indirect or inverse association with exchange rate fluctuations. These findings pose important practical implications for the short‐term and long‐term shareholders and financial analysts. Hence, it is recommended that shareholders and financial analysts should base their investment in South Asian stock indices on negative and positive shocks since the non‐linear connectivity between both variables also exhibits regime dependence characteristics.

A lesson from COVID-19 pandemic: Developing a survival investment strategy to deal with crisis conditions

This study introduces the novel concept of a stock investment strategy tailored to crises. It adopts a quantitative approach that tests various factors influencing stock price dynamics. In the first stage of the study, a multiple regression analysis is conducted, encompassing macroeconomic indicators such as Southeast Asian, Chinese, and American stock indexes, gold prices, and the impact of COVID-19 on stock prices. Subsequently, an additional examination assesses the influence of LQ-45 company performance on stock price formulation. Finally, a qualitative analysis is employed to scrutinize government policies implemented during the pandemic and their impact on Jakarta Composite Index (JCI) movements. As a result, it is found that all macroeconomic factors exert a significant negative influence on stock prices. Additionally, as COVID-19 cases surge, stock indexes in Southeast Asia, China, and America exhibit a substantial positive effect on stock prices. In contrast, firm performance and gold prices do not significantly affect stock price movements. Moreover, the market also reveals a more favorable response to government initiatives aimed at relaxing activity restrictions and positive developments in vaccination efforts. Lastly, based on the extensive analyses conducted in this study, we have identified four distinct investment strategies tailored explicitly for effectively navigating crises. These strategies encompass diversification, a prudent wait-and-see approach, the Richman party, and the Guerrilla strategy.

Stock market reaction to the COVID-19 pandemic: an event study

The COVID-19 pandemic created unprecedented challenges for communities and economies around the world. Based on 13 leading global stock indices, the event study method is adopted in this research to explore the impact of the COVID-19 pandemic on the performance of the stock market indices in the short term. Regression results show that the global stock markets performed poorly in response to the COVID-19 pandemic. The findings of the event study imply that the stock markets reacted rapidly and negatively to the COVID-19 pandemic when lockdown restrictions were announced to contain the spread of the novel coronavirus. The Asian stock indices experienced more negative abnormal earnings than the stock indices of the countries outside Asia. Moreover, investor sentiments act as a wedge between financial investment decisions, returns, and fear of uncertainty caused by the pandemic. Furthermore, the panic experienced by investors may be an effective transmission channel through which the COVID-19 outbreak affects the returns on the stock market indices.

Normality Tests and its Power against Alternative Distributions: An Empirical Analysis on Emerging Asian Stock Index Returns.

In this paper, we investigate the power of various normality tests against alternative distributions using Monte Carlo simulation experiments. We use seven different normality tests classified as moments tests, correlation and regression tests, and empirical distribution functional tests against six symmetric and four asymmetric alternative distributions. We also perform the rank analysis for the power of the normality tests. Furthermore, we conduct an empirical analysis of five emerging Asian stock indices (India, Indonesia, Malaysia, Singapore, and Taiwan) to understand whether the returns follow a normal distribution or not during the period from January 2000 to January 2020. We find that emerging Asian stock index returns do not follow normal distribution irrespective of the different frequencies of the data.

Return and Volatility Spillover Effects between Rupee–Dollar Exchange Rate and Asian Stock Indices

India is a major Asian economy that has attracted global investment due to its economic stability and relatively open financial markets. We investigate the return and volatility spillover effects between the Indian rupee against US dollar and stock index volatility in neighboring (Asian) nations. Consistent with the uncovered equity parity, we find that when the rupee depreciates against US dollar, the foreign institutional investors pull back their portfolio investments from other Asian markets and invest in Indian stock market, similarly if rupee appreciates they may divest their investments to gain profits. The unidirectional causality from the exchange rate to all stock indices is indicated by the causality in the mean approach. Our study shows a strong linkage between the currency value of a stable economy and the economies with which it has strong trade ties. Further, the results also show that investors adjust their position in the related markets during periods of currency appreciation and depreciation in an economy with significant growth prospects. JEL Codes: F31, D53, B23

Study of Asian indexes by a newly derived dynamic model.

We take the stock prices as a dynamic system and characterize its movements by a newly derived dynamic model, called the new Price Reversion Model (nPRM), for which the solution is derived and carefully analyzed under different circumstances. We also develop a procedure of applying the nPRM to real daily closing prices of a stock index. This proposed procedure brings a different perspective to the study of stock prices based on thermodynamics, and the time varying coefficients in the nPRM offer economic meanings of the stock movements. More specifically, the average of smoothed historical data A in the nPRM, analogous to the environment temperature in the Newton's law of cooling, represent an implied equilibrium price. The heat transfer coefficient κ is adapted to be either negative or positive, which illustrates the speed of convergence or divergence of stock prices, respectively. The empirical study of ten Asian stock indexes shows that the nPRM accurately characterizes and forecasts the market values.

Forecasting volatility in Asian financial markets: evidence from recursive and rolling window methods

The present paper examines the relative out-of-sample predictive ability of GARCH, GARCH-M, EGARCH, TGARCH and PGARCH models for ten Asian markets by using three different time frames and two different methods, considering the features of volatility clustering, leverage effect and volatility persistence phenomena, for which the evidence of existence is found in the data. Five measures of comparison are employed in this research, and a further dimension is investigated based on the classification of the selected models, in order to identify the existence or lack of any differences between the recursive and rolling window methods. The empirical results reveal that asymmetric models, led by the EGARCH model, provide better forecasts compared to symmetric models in higher time frames. However, when it comes to lower time frames, symmetric GARCH models tend to outperform their asymmetric counterparts. Furthermore, linear GARCH models are penalized more by the rolling window method, while recursive method places them amongst the best performers, highlighting the importance of choosing a proper approach. In addition, this study reveals an important controversy: that one error statistic may suggest a particular model is the best, while another suggests the same model to be the worst, indicating that the performance of the model heavily depends on which loss function is used. Finally, it is proved that GARCH-type models can appropriately adapt to the volatility of Asian stock indices and provide a satisfactory degree of forecast accuracy in all selected time frames. These results are also supported by the Diebold-Mariano (DM) pairwise comparison test.

CURRENT STOCK MARKET INDICATOR TRENDS AGAINST THE BACKGROUND OF COVID-19 AND THE RUSSIAN-UKRAINIAN WAR

The scientific work analyzes stock indices and how they react to external changes. Characteristic features of the main world stock indices are highlighted and described, and possible prospects for their development are revealed. The article reveals the meaning of such concepts as stock indices, considers their essence, key indicators, as well as the impact of global problems on market fluctuations. The work is devoted to the study of some of the main problems of humanity during the last decade. The authors consider how Covid- 19 and the Russian-Ukrainian war affected the stock markets. The work provides an analysis of the features and development trends of the main stock indices against the background of global events. The impact, economic and political consequences, possible threats caused by the pandemic and war, as well as prospects for the further development of the world economy are revealed. The study identifies the main problems and trends in the development of stock markets at the current stage. Covid-19 has shown how sensitive markets can be to global societal issues, including how it can significantly affect the global economy and stock indices. Also, an equally important problem considered in the article is the impact of the Russian-Ukrainian war on the economy and its further consequences are predicted. In general, the beginning of the full-scale armed aggression of the Russian Federation on the territory of Ukraine became a significant shock and challenge after the almost complete overcoming of the global Covid-19 pandemic. This discovery study examines the impact of the Covid-19 pandemic and war on USA, European, and East Asian stock indices. Our results show that changes in the background of Covid-19 and war had a significant impact on the Dow Jones, Nikkei 225, FTSE 100 and NASDAQ 100.

Effect of COVID-19 Pandemic on Global Stock Markets

This paper examined the effect of COVID-19 pandemic on global stock markets. Daily closing prices were obtained from Wall Street data base from 1st January to 31st March 2020. For the analysis 80 trading days were used from 20th January, 2020, when the news of COVID-19 pandemic broke out to 11th March, 2020 when WHO declared it a global pandemic. Eighteen stock indices from the most affected countries were selected for the study based on the reports of Worldometers and event study was used for the analysis. The CARs and CAARs for each stock index were computed based on regional classification using four windows of analysis. The regional classifications comprised Europe, America (North and South) and Asian stock indices. A two-way fixed effect model was estimated to test for the effect of regional volatility. The study discovered that the worst hit stock index with the lowest AR a day to the event was Spain’s IBEX 35 Index while on the event day the lowest was MSCI Poland Index. And the worst hit on the day after the event was S&P 500 Composite Index of the US. It further found that the COVID-19 pandemic affected each region in different ways. The North and South American stocks suffered the highest volatility, whereas, the highest losses were observed in European stocks. The study recommends that governments should be very mindful of the policy responses and measures they adopt to address the COVID-19 pandemic. Internal policies should be designed in such a way to accommodate for external shocks.

Empirical Evidence of Dynamic Conditional Correlation Between Asian Stock Markets and US Stock Indexes During COVID-19 Pandemic

This study aims to explore the dynamic conditional correlation (DCC) between ten Asian stock indexes, the US stock index, and Bitcoin by using the dynamic conditional correlation model. The time span of the daily data is between January 2015 to May 2021, the total observation is 1,116. DCC(1,1)-EGARCH(1,1) with multivariate t and normal distributions for the DCC and EGARCH models, respectively, outperforms other models by the goodness of fit values. Except for Bitcoin, we discovered that the majority of the securities' volatilities have a very high volatility persistence. Furthermore, the negative shocks/news have more impact on the volatilities than positive shocks/news in most of the cases, except the stock index of China and Bitcoin. Most of the correlation pairs exhibit higher correlation during the COVID-19 pandemic compared to the pre-COVID-19, except Hong Kong-The US and Malaysia-Indonesia. Moreover, the correlation between Asian stock indexes during the COVID-19 pandemic is statistically higher than the pre-COVID-19 pandemic. However, there are a few instances where the Hong Kong stock index and a few countries are identical. The result of correlation size shows the connectedness between Asian stock markets, which are well-connected within the region, especially with South Korea, Singapore, and Hong Kong.