Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

scholarly journals Volatility Similarity and Spillover Effects in G20 Stock Market Comovements: An ICA-Based ARMA-APARCH-M Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shanglei Chai ◽  
Zhen Zhang ◽  
Mo Du ◽  
Lei Jiang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Spillover Effects ◽  
Series Data ◽  
Dominant Source ◽  
Financial Internationalization

Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.

scholarly journals INTERRELATIONSHIPS AMONG INTERNATIONAL STOCK MARKET INDICES: EUROPE, ASIA AND THE AMERICAS

2005 ◽  
Vol 08 (05) ◽  
pp. 603-622 ◽  
Author(s):  
ADEL SHARKASI ◽  
HEATHER J. RUSKIN ◽  
MARTIN CRANE
Keyword(s):  
Hong Kong ◽  
Wavelet Transform ◽  
Stock Market ◽  
Stock Markets ◽  
Spillover Effects ◽  
Data Series ◽  
Testing Method ◽  
International Stock Markets ◽  
The Us ◽  
European Markets

In this paper, we investigate the price interdependence between seven international stock markets, namely Irish, UK, Portuguese, US, Brazilian, Japanese and Hong Kong, using a new testing method, based on the wavelet transform to reconstruct the data series, as suggested by Lee [11]. We find evidence of intra-European (Irish, UK and Portuguese) market co-movements with the US market also weakly influencing the Irish market. We also find co-movement between the US and Brazilian markets and similar intra-Asian co-movements (Japanese and Hong Kong). Finally, we conclude that the circle of impact is that of the European markets (Irish, UK and Portuguese) on both American markets (US and Brazilian), with these in turn impacting on the Asian markets (Japanese and Hong Kong) which in turn influence the European markets. In summary, we find evidence for intra-continental relationships and an increase in importance of international spillover effects since the mid 1990s, while the importance of historical transmissions has decreased since the beginning of this century.

Are there Monthly and Turn-of-the-Month Effects in Indian Stock Market?: Evidence and Implications

Paradigm ◽  
2007 ◽  
Vol 11 (2) ◽  
pp. 16-22 ◽  
Author(s):  
Deepa Mangala ◽  
S.K. Sharma
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Stock Markets ◽  
Major Part ◽  
Market Returns ◽  
Stock Market Returns ◽  
International Stock Markets ◽  
Indian Stock Market ◽  
Daily Returns

The seasonal components of stock market returns have been extensively documented, yet the major part remains unexplained. The monthly effect has been reported in several international stock markets. The objective of this paper is to examine the existence of monthly effect and turn-of-the-month effect in Indian stock market by using S&P CNX Nifty index over the period from January 1994 to April 2005. The results reveal significantly high mean daily returns for days immediately before and during the first half of the month, especially, during the first few trading days of the month and indistinguishable from zero or even negative mean returns for the second half and the rest of the month. Turn-of-the-month is marked by abnormally high returns. This gives a strong evidence of existence of monthly effect and turn-of-the-month effect in Indian stock market.

Are there Monthly and Turn-of-the-Month Effects in Indian Stock Market?: Evidence and Implications

Paradigm ◽  
2007 ◽  
Vol 11 (2) ◽  
pp. 9-15
Author(s):  
Deepa Mangala ◽  
S.K. Sharma
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Stock Markets ◽  
Major Part ◽  
Market Returns ◽  
Stock Market Returns ◽  
International Stock Markets ◽  
Indian Stock Market ◽  
Daily Returns

The seasonal components of stock market returns have been extensively documented, yet the major part remains unexplained. The monthly effect has been reported in several international stock markets. The objective of this paper is to examine the existence of monthly effect and turn-of-the-month effect in Indian stock market by using S&P CNX Nifty index over the period from January 1994 to April 2005. The results reveal significantly high mean daily returns for days immediately before and during the first half of the month, especially, during the first few trading days of the month and indistinguishable from zero or even negative mean returns for the second half and the rest of the month. Turn-of-the-month is marked by abnormally high returns. This gives a strong evidence of existence of monthly effect and turn-of-the-month effect in Indian stock market.

International stock market liquidity: a review

2016 ◽  
Vol 42 (2) ◽  
pp. 118-135 ◽  
Author(s):  
Rui Ma ◽  
Hamish D. Anderson ◽  
Ben R. Marshall
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Liquidity ◽  
Short Sales ◽  
Liquidity Premium ◽  
Content Type ◽  
International Stock Markets ◽  
Stock Market Liquidity ◽  
Dark Pools ◽  
The Impact

Purpose – The purpose of this paper is to review the literature on liquidity in international stock markets, highlights differences and similarities in empirical results across existing studies, and identifies areas requiring further research. Design/methodology/approach – International cross-country studies on stock market liquidity are categorized and reviewed. Important relevant single-country studies are also discussed. Findings – Market liquidity is influenced by exchange characteristics (e.g. the presence of market makers) and regulations (e.g. short-sales constraints). The literature has identified the most appropriate liquidity measures for global research, and for emerging and frontier markets, respectively. Major empirical facts are as follows. Liquidity co-varies within and across countries. Both the liquidity level and liquidity uncertainty are priced internationally. Liquidity is positively associated with firm transparency and share issuance, and negatively related to dividends paid out. The impact of internationalization on liquidity is not universal across firms and countries. Some suggested areas for future studies include: dark pools, high-frequency trading, commonality in liquidity premium, funding liquidity, liquidity and capital structure, and liquidity and transparency. Research limitations/implications – The paper focusses on international stock markets and does not consider liquidity in international bond or foreign exchange markets. Originality/value – This paper provides a comprehensive survey of empirical studies on liquidity in international developed and emerging stock markets.

scholarly journals INVESTIGATION OF FORECASTED RISK INTERRELATIONSHIP: BASE ON GARCH MODEL, CAUSALITY IN CHINA MARKETS

2014 ◽  
Vol 15 (5) ◽  
pp. 853-861
Author(s):  
Shu-Shian Lin
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Unit Root ◽  
Stock Markets ◽  
Null Hypothesis ◽  
Unit Root Test ◽  
Stock Exchanges ◽  
Equilibrium Relationship ◽  
Research Period ◽  
Integration Analysis

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.

An Effective Stock Classification Method Via MDS Based on Modified Mutual Information Distance

2019 ◽  
Vol 19 (02) ◽  
pp. 2050018 ◽  
Author(s):  
Jun Jiang ◽  
Pengjian Shang ◽  
Xuemei Li
Keyword(s):  
Time Series ◽  
Mutual Information ◽  
Stock Market ◽  
Financial Data ◽  
Simulation Experiments ◽  
Market Data ◽  
Information Distance ◽  
Proximity Matrix ◽  
The Stability ◽  
Stability And Accuracy

This paper proposes a multidimensional scaling (MDS) method based on modified mutual information distance (M-MDS) to analyze stock market data. To better describe the relativity of financial data, it is worthwhile to point out that the commonly used proximity matrix in MDS is replaced with modified mutual information distance (M-MI-D) matrix. Refer to M-MI-D, a higher dissimilarity leads to a larger distance. In order to demonstrate the stability and accuracy of M-MDS, logistic time series are used in simulation experiments. In addition, a comparison of this new M-MDS method with classical MDS is given using the stock market data. It is noted that the new M-MDS method shows better stability than that of classical MDS method. Moreover, not only the stocks in the same US stock block, but also the stocks in different blocks have been discussed to illustrate the efficiency of M-MDS method.

Stock Market Integration and Trade: A Study on India and its Major Trading Partners

2021 ◽  
pp. 097226292110344
Author(s):  
Samiran Jana
Keyword(s):  
United States ◽  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
The United States ◽  
Asia Pacific ◽  
Panel Regression ◽  
Conditional Correlation ◽  
Trading Partners ◽  
Indian Stock Market

Indian stock market is increasingly integrated with other markets of the world after economic liberalization. This linkage of Indian stock market reduces the scope of risk minimization of portfolio by diversifying between stock markets of India and its integrated partners. Researchers indicate that economic variables influence the integration of stock markets. Trade is one of the major parameters. In this study effort has been made to find how Indian stock market integration varies with amount of trade with its trading partners. Hence 21 years’ weekly data from 1 January 1999 to 31 December 2019 have been collected for 15 countries, which belong to the list of top 25 trading partners of India since 1999. Total sample of 15 countries have been divided into two groups—Asia Pacific and European group along with United States, to check whether integration increase with trade. Johansen Cointegration test has been used between stock markets of India and two groups of countries. To confirm the result of Johansen cointegration test, the same test was ran on joint index for each group and Sensex of India. It also helped to check the effect of geographical proximity on this integration. Conditional correlation was found using asymmetric generalized dynamic conditional correlation (AGDCC) GARCH model, between Sensex and each of the 15 countries, to observe the time varying nature of correlation. Twenty one year’s data has helped to find the impact of global financial crisis (GFC) of 2008 on these interlinkages. Lending rate differential and inflation rate differential can cover many economic parameters, hence used as control variable. Time series regression has been used to find the impact of trade, interest rate and inflation differential on correlation between Indian stock market index and index of any other countries. Pooled panel regression has been used to check the same relationship on all countries in every group. Nine Asian countries together contribute higher amount of trade with India since 2004 than jointly five European countries and the United States. Trade difference is very low, hence this study analysed both the groups. Asia Pacific group of countries is more integrated with India than European group and the United States. None of the joint index is integrated with Indian stock market index. Conditional correlation between Indian index and each of the country has changed over time. Time series regression implies that except very few cases, trade and other economic factors cannot influence the integration. As expected, the interest rate differential and inflation differential have negative and positive impacts on the correlation respectively but these impacts are not significant in many cases. Pooled panel regression shows that trade and GFC have positive and significant impact on correlation between India and Asia Pacific countries but not with the same between India and European countries and United States. International investors will not be able to reduce their portfolio risk by diversifying between India and any other of the 15 countries in the sample because all of them are integrated with Indian stock market. Trade of India with Asian countries has increased in recent years and integration has also increased. Although time series and pooled panel regression do not prove it’s significant impact on conditional correlation between India and the sampled countries. But trade between two countries definitely bear a role in integration.

scholarly journals Econophysical bourse volatility – Global Evidence

2020 ◽  
Vol 9 (2) ◽  
pp. 87-107
Author(s):  
Bikramaditya Ghosh ◽  
Krishna MC
Keyword(s):  
Time Series ◽  
Reynolds Number ◽  
Stock Market ◽  
Information Asymmetry ◽  
Financial Literacy ◽  
Stock Markets ◽  
Herd Behaviour ◽  
Stochastic Time Series ◽  
Stochastic Time ◽  
Global Evidence

AbstractFinancial Reynolds number (Re) has been proven to have the capacity to predict volatility, herd behaviour and nascent bubble in any stock market (bourse) across the geographical boundaries. This study examines forty two bourses (representing same number of countries) for the evidence of the same. This study finds specific clusters of stock markets based on embedded volatility, herd behaviour and nascent bubble. Overall the volatility distribution has been found to be Gaussian in nature. Information asymmetry hinted towards a well-discussed parameter of ‘financial literacy’ as well. More than eighty percent of indices under consideration showed traces of mild herd as well as bubble. The same indices were all found to be predictable, despite being stochastic time series. In the end, financial Reynolds number (Re) has been proved to be universal in nature, as far as volatility, herd behaviour and nascent bubble are concerned.

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