ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

2020 ◽  
pp. 1-16
Author(s):  
MUHAMMAD UMAR ◽  
NGO THAI HUNG ◽  
SHIHUA CHEN ◽  
AMJAD IQBAL ◽  
KHALIL JEBRAN
Keyword(s):  
Operating System ◽  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Wavelet Coherence ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Nasdaq Composite Index

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

scholarly journals Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402110057
Author(s):  
Fahim Afzal ◽  
Pan Haiying ◽  
Farman Afzal ◽  
Asif Mahmood ◽  
Amir Ikram
Keyword(s):  
At Risk ◽  
Stock Returns ◽  
Stock Markets ◽  
Value At Risk ◽  
Stock Exchange ◽  
Integrated Approach ◽  
Actual Behavior ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

TIME-VARYING LONG MEMORIES OF THE CHINESE CURRENCY AND STOCK MARKETS BASED ON THE HURST EXPONENT

2014 ◽  
Vol 13 (01) ◽  
pp. 1450007 ◽  
Author(s):  
CAO GUANGXI ◽  
HAN YAN ◽  
CUI WEIJUN
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Markets ◽  
Hurst Exponent ◽  
Composite Index ◽  
Fluctuation Analysis ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Currency Market ◽  
Cointegration Tests

Based on the daily return and volatility series of the Chinese yuan (RMB)/US dollar (USD) exchange rate and the Shanghai Stock Composite Index, the time-varying long memories of the Chinese currency and stock markets are investigated by comprehensively using the rescaled range (R/S), the modified R/S, and the detrended fluctuation analysis methods. According to the results drawn: (1) the efficiency of the Chinese currency market has not improved significantly, whereas the efficiency of the Chinese stock market has improved steadily, (2) volatility series presents longer memory than return series either in the Chinese currency or stock market and (3) the time-varying Hurst exponent of the Chinese currency market is sensitive to the reform that enhances the flexibility of the RMB exchange rate. Moreover, we find that short-term bidirectional Granger causal relationship exists, but no long-run equilibrium relationship between the time-varying Hurst exponents of the Chinese currency and stock markets was found based on the Granger causality and cointegration tests, respectively.

Inter-relationship between performance of Bursa Malaysia and foreign stock markets

2006 ◽  
Vol 3 (1) ◽  
pp. 113
Author(s):  
T. Chantrathevi P. Thuraisingam ◽  
You Hoo Tew ◽  
Dalila Daud
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Asian Financial Crisis ◽  
Moderate Correlation ◽  
Crisis Period ◽  
Dow Jones Industrial Average ◽  
Daily Returns ◽  
Over Time

This paper explores the general perception that the Malaysian stock market is influenced by leading overseas stock markets. Employing correlation analysis comparison was made between the performance ofBiirsa Malaysia's Composite Index and six stock market indices namely Straits Times Index, Hang Seng Index, Nikkei 225 Stock Average, Australia All Ordinaries Index, Dow Jones Industrial Average Index and Financial Times 100 Index. This study also seeks to determine ifthere is any significant stability ofcorrelations over time. These indices were studied over a period offifteen years from I January 1990 to 31 December 2004, beginning with the cessation oftrading ofMalaysian shares on the Singapore stock exchange, which is synonymous with the pre-Asian financial crisis period, the crisis period and a post crisis period of almost five years. The study found that the, daily returns of the Composite Index over the period is positively co-related with the foreign indices indicating that the markets were moving in the same direction, in other words there is interdependency between the stock markets. However, the low to moderate correlation refutes the belief that the Malaysian stock market is influenced by the performance ofthe major stock markets. The study also found that generally the correlations are unstable over lime.    

scholarly journals Commodity Price Correlation And Time Varying Hedge Ratios

2014 ◽  
Vol 30 (4) ◽  
pp. 1053
Author(s):  
Amine Lahiani ◽  
Khaled Guesmi
Keyword(s):  
Stock Market ◽  
Value At Risk ◽  
Price Volatility ◽  
Commodity Price ◽  
Conditional Value At Risk ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Hedging Strategies ◽  
Hedge Ratios

<p>This paper examines the price volatility and hedging behavior of commodity futures indices and stock market indices. We investigate the weekly hedging strategies generated by return-based and range-based asymmetric dynamic conditional correlation (DCC) processes. The hedging performances of short and long hedgers are estimated with a semi-variance, low partial moment and conditional value-at-risk. The empirical results show that range-based DCC model outperforms return-based DCC model for most cases.</p>

Time-varying Correlation Between Indian Equity Market and Selected Asian and US Stock Markets

2019 ◽  
Vol 21 (6) ◽  
pp. 1354-1375 ◽  
Author(s):  
Neha Seth ◽  
Laxmidhar Panda
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Stock Markets ◽  
Equity Market ◽  
Leverage Effect ◽  
Time Varying ◽  
Asymmetric Effect ◽  
Conditional Volatility ◽  
Conditional Correlation ◽  
Volatility Persistence

The purpose of this article is to examine the dynamic relationship between the Indian stock market and the selected Asian and US stock markets during the post-crisis period. This article uses univariate GARCH (Generalized Autoregressive Conditional Heteroskedasticity) family models on daily observations from March 2009 to December 2015 to evaluate the volatility persistence and leverage effect on Asian developed (Japan, Singapore and Hong Kong) and emerging markets (India, China, Indonesia, Korea, Malaysia and Taiwan) along with the US stock market. AR (Autoregressive) ( 1 )-GARCH (1, 1)-ADCC (Asymmetric DCC) model is employed to find out the dynamic correlation between the Indian equity market and other selected stock markets. The results of the present study give evidence of the leverage effect in conditional volatility but not in conditional correlation, which implies that the rise in conditional volatility is more due to negative shocks than positive ones. On the other hand, dynamic conditional correlation (DCC) does not support any asymmetric effect for the time-varying correlation. The result of average conditional correlation shows the existence of higher diversification opportunities for Indian investors in Malaysian, Chinese and Japanese stock markets (having lower conditional correlation) than in Hong Kong, Indonesian and South Korean markets. The DCC fluctuates more in the cases of India with Singapore, Hong Kong and Indonesia over the sample period. It indicates that the stability of DCC is less reliable and the coefficient of correlation may not be used as a guide for portfolio decisions. But the cases of India with the USA, Japan and China show more stable conditional correlation coefficients. This article investigates the volatility persistence and time-varying relationship among Asian stock markets during the recent period, 2009–2015. The results of this article may be helpful in international portfolio planning and will contribute towards the literature on asymmetric time-varying relationships among Asian markets.

scholarly journals MODELLING THE CONDITIONAL CO-MOVEMENTS OF PAKISTAN AND INTERNATIONAL STOCK MARKETS

2021 ◽  
Vol 9 (3) ◽  
pp. 295-307
Author(s):  
Ali Akbar Pirzado ◽  
Naeem Ahmed Qureshi ◽  
Imran Khan Jaoti ◽  
Komal Arain ◽  
Riaz Ali Buriro
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Information Criterion ◽  
Forecast Evaluation ◽  
Conditional Correlation ◽  
International Stock Markets ◽  
Out Of Sample ◽  
The Government ◽  
New Policies

Purpose of the study: This study assesses and evaluates the conditional co-movements and dynamic conditional correlation of the Pakistan Stock Exchange (PSX) with other Stock Market. Methodology: DCC-GARCH model has been applied due to its feasibility to model the covariance as a function of correlation and variance together. Main findings: The findings of the research suggest that the Pakistani Stock Exchange (PSX) is highly volatile compared to two other selected stock markets. In-sample fitting, the study has selected the DCC-GARCH (1, 1) model based on information criterion, conversely, the criterion used for out-of-sample forecast evaluation such as MSFE, RMSFE, MAPE, selected the DCC (2,1)-GARCH (1,1). Application of the study: This study is very useful for the Pakistan stock market and other international selected stocks markets until and unless the government of Pakistan and other governments will devise new policies which may open new opportunities to investors. Novelty/ Originality of the study: This study has a great potential in the Pakistani stock market to offer investors to several foreign and domestic investors, allowing them to hold Pakistan as well as foreign and local stocks all major benefits.

Time-varying synchronization and dynamic conditional correlation among the stock market returns of leading South American economies

2018 ◽  
Vol 14 (2) ◽  
pp. 245-262 ◽  
Author(s):  
Ajaya Kumar Panda ◽  
Swagatika Nanda
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
South American ◽  
Conditional Correlation ◽  
Market Returns ◽  
Stock Market Returns ◽  
Content Type ◽  
Dynamic Conditional Correlation ◽  
Auto Regressive

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.

META-ANALYSIS ON AMERICAN PRESIDENTIAL ELECTION - 2016 AND AMERICAN STOCK MARKET

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Farhan Ahmed ◽  
Salman Bahoo ◽  
Sohail Aslam ◽  
Muhammad Asif Qureshi
Keyword(s):  
Content Analysis ◽  
Stock Market ◽  
Event Study ◽  
Presidential Election ◽  
Republican Party ◽  
Stock Exchange ◽  
Meta Analysis ◽  
Composite Index ◽  
Stock Index ◽  
Political Event

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

Momentum Effect in Stock Market: Empirical Evidence from Pakistan Stock Exchange

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Shahid Rasheed ◽  
Umar Saood ◽  
Waqar Alam
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Stock Markets ◽  
Stock Exchange ◽  
Momentum Effect ◽  
Using Data

This study aims to examine the momentum effect presence in selected stocks of Pakistan stock market using data from Jan 2007 to Dec 2016. This study constructed the strategies includes docile, equal weighted and full rebalancing techniques. Data was extracted from the PSX – 100 index ranging from 2007 to 2016. STATA coding ASM software was used for calculating momentum portfolios, finally top 25 stocks were considered as a winner stocks and bottom 25 stocks were taken as a loser stocks. In conclusion, the results of the study found a strong momentum effect in Pakistan stock exchange PSX 100- index. As by results it has been observed that a substantial profit can earn by the investors or brokers in constructing a portfolio with a short formation period of three months and hold for 3, 6 and 12 months. There is hardly a study is present on the same topic on Pakistan Stock Exchange as preceding studies were only conducted on individual stock markets before merger of stock markets in Pakistan while this study leads the explanation of momentum phenomenon in new dimension i.e. Pakistan Stock Exchange. Keywords: Momentum, Portfolio, Winner Stocks, Loser Stocks

scholarly journals The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Halit Cinarka ◽  
Mehmet Atilla Uysal ◽  
Atilla Cifter ◽  
Elif Yelda Niksarlioglu ◽  
Aslı Çarkoğlu
Keyword(s):  
Case Reports ◽  
Time Varying ◽  
Conditional Correlation ◽  
Web Based ◽  
Specific Symptom ◽  
Sliding Windows ◽  
Dynamic Conditional Correlation ◽  
The United Kingdom ◽  
Correlation Models ◽  
Google Search

AbstractThis study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

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