scholarly journals Cryptocurrency Safe Haven Property against Indonesian Stock Market During COVID-19

2021 ◽  
Vol 24 (1) ◽  
pp. 121
Author(s):  
Mudita Gunawan ◽  
Achmad Herlanto Anggono
Keyword(s):  
Stock Market ◽  
Composite Index ◽  
Garch Model ◽  
Price Volatility ◽  
High Price ◽  
Safe Haven ◽  
Risk And Return ◽  
Volume Increase ◽  
Dummy Variable ◽  
The Garch Model

Safe-haven assets conserve their value or grow against another asset or portfolioduring market turmoil. Indonesian stock market, represented by the Jakarta composite index (JKSE), plunged in price because of COVID-19, pushing investors to look for safe-havens. The cryptocurrency began to be perceived as a store of value as indicated by the transaction volume increase; hence it was expected to be a safe haven asset. However, cryptocurrency’s high price volatility cast doubts on its store of value effectiveness, prompting inspection for its safe haven property as well. This research aimed to predict the assets' risk and return plus investigate whether cryptocurrency is safe haven assets against the Indonesian stock market during COVID- 19. Daily closing prices of JKSE, Bitcoin, Ethereum, Litecoin, and Ripple were used, then the GARCH model was implemented in the forecasting. DCC-GARCH model, followed by dummy variable regression, will be applied to the return data to evaluate the safe haven property. The prediction projected Bitcoin as the most profitable asset andRipple as the riskiest. The analysis and robustness test suggested that none of these cryptocurrencies were safe haven assets during the whole observation. This indicates that investors who intend to seek safe haven investments were advised against investing in these cryptocurrencies.

The dependence structure in volatility between Shanghai and Shenzhen stock market in China

2016 ◽  
Vol 6 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Mingyuan Guo ◽  
Xu Wang
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Estimation Method ◽  
Garch Model ◽  
Price Volatility ◽  
Market Volatility ◽  
High Frequency Data ◽  
Dependence Structure ◽  
Frequency Data ◽  
Content Type

Purpose – The purpose of this paper is to analyse the dependence structure in volatility between Shanghai and Shenzhen stock market in China based on high-frequency data. Design/methodology/approach – Using a multiplicative error model (hereinafter MEM) to describe the margins in volatility of China’s Shanghai and Shenzhen stock market, this study adopts static and time-varying copulas, respectively, estimated by maximum likelihood estimation method to describe the dependence structure in volatility between Shanghai and Shenzhen stock market in China. Findings – This paper has identified the asymmetrical dependence structure in financial market volatility more precisely. Gumbel copula could best fit the empirical distribution as it can capture the relatively high dependence degree in the upper tail part corresponding to the period of volatile price fluctuation in both static and dynamic view. Originality/value – Previous scholars mostly use GARCH model to describe the margins for price volatility. As MEM can efficiently characterize the volatility estimators, this paper uses MEM to model the margins for the market volatility directly based on high-frequency data, and proposes a proper distribution for the innovation in the marginal models. Then we could use copula-MEM other than copula-GARCH model to study on the dependence structure in volatility between Shanghai and Shenzhen stock market in China from a microstructural perspective.

scholarly journals Ramadan Effect and Volatility Risk by Garch Model: Evidence in Indonesia Stock Market

2021 ◽  
pp. 73-82
Author(s):  
Dery Westryananda Putra ◽  
Sri Hasnawati ◽  
Muslimin Muslimin
Keyword(s):  
Stock Market ◽  
Sampling Method ◽  
Stock Exchange ◽  
Garch Model ◽  
Analytical Tool ◽  
Volatility Risk ◽  
Purposive Sampling ◽  
Model Evidence ◽  
The Garch Model

This study aims to analyze the effect of the Ramadan effect and volatility risk on the Indonesian stock market using the GARCH model. The population in this study are companies listed on the LQ45 index on the Indonesia Stock Exchange during 2019. There are 42 companies used as samples in this study. The research sample was taken using purposive sampling method. This study uses the GARCH model as an analytical tool. The results of this study indicate that there is no Ramadan effect on the LQ45 index, but the volatility in the month of Ramadan affects the volatility in the LQ45 index. Keywords: Ramadan Effect, Volatility Risk, GARCH Model Abstrak Penelitian ini bertujuan untuk menganalisis pengaruh Ramadhan effect dan risiko volatilitas terhadap pasar saham Indonesia dengan menggunakan model GARCH. Populasi dalam penelitian ini adalah perusahaan yang terdaftar pada indeks LQ45 di Bursa Efek Indonesia selama tahun 2019. Terdapat 42 perusahaan yang dijadikan sampel dalam penelitian ini. Sampel penelitian diambil dengan menggunakan metode purposive sampling. Penelitian ini menggunakan model GARCH sebagai alat analisis. Hasil penelitian ini menunjukkan bahwa tidak ada pengaruh Ramadhan terhadap indeks LQ45, namun volatilitas pada bulan Ramadhan berpengaruh terhadap volatilitas pada indeks LQ45. Kata Kunci: Ramadhan Effect, Risiko Volatilitas, Model GARCH

scholarly journals The Dynamic Relationship between Crude Oil Prices and Stock Market Price Volatility in Nigeria: A Cointegrated VAR-GARCH Model

2019 ◽  
pp. 1-12
Author(s):  
David Adugh Kuhe
Keyword(s):  
Stock Market ◽  
Crude Oil ◽  
Market Price ◽  
Garch Model ◽  
Price Volatility ◽  
Oil Prices ◽  
Market Prices ◽  
Dynamic Relationship ◽  
Crude Oil Prices ◽  
Long Run

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.

scholarly journals Accessing the Equity Return Volatility Effect of East and South Asian Nations: The Econometrics Modelling Method

2019 ◽  
Vol 8 (3S) ◽  
pp. 594-603
Keyword(s):  
Stock Market ◽  
South Asian ◽  
Market News ◽  
Institutional Investors ◽  
Garch Model ◽  
Equity Market ◽  
Stock Index ◽  
Critical Element ◽  
Market Returns ◽  
The Garch Model

In financial management, the equity market performance is the critical element of equity market returns volatility wherever the shareholder’s resilience around the instability subsists. The data is collected from the authenticated secondary sources for the analysis. This paper shows that the 2008economicpredicament, as well as the effect above proceeding developing financial prudence of the globe, is found in the equity return instability connation of developing financial prudence (2004-2015). By the GARCH model, it can be examined that as the information from the U.S.A. stock market news has an essential consequences on the earnings of the S&P 500 stock market index, the indices of the east, as well as south Asian nations, has also influenced by the news of U.S.A. The GARCH model is estimated for the U.S.A. stock market news has a substantial effect or not on East and South Asian nation's daily share market returns. The outcomes show that market earnings in the equity market in east and south Asian nations are incredibly reliant on their historical earnings. It is found that Tokyo Topic (4.8929) is a highly volatile stock index among the East and South Asian stock returns, and the low volatile stock index is DSEX (0.0068). The news of the U.S.A. stock market has affected the equity market of India, Japan, China, and Korea, which are included in the East and South Asian stock market. In all the country’s share markets, found most significant variance in the equity income instability. This study is essential for the shareholders looking for the diversification in the portfolio, domestic institutional investors and foreign institutional investors

scholarly journals Gold and Government Bonds as Safe-Haven Assets Against Stock Market Turbulence in China

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402199065
Author(s):  
Wei Huang ◽  
Meng-Shiuh Chang
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Composite Index ◽  
Investment Strategy ◽  
Chinese Stock Market ◽  
Safe Haven ◽  
Copula Models ◽  
Government Bonds ◽  
Market Turbulence ◽  
Vine Copula

We examine whether gold and China’s government bonds are safe-haven assets against the turbulence of the Shanghai Stock Exchange Composite Index by employing vine copula models during the 2003 to 2015 period. We find that either bonds or gold can be a weak safe haven but only gold can be a strong safe haven. Our simultaneous analysis advises against a joint safe-haven strategy of gold and bonds, given the high- to low-tail correlation. This result highlights an investment strategy of using a single safe-haven asset against the Chinese stock market turbulences.

scholarly journals INDEX FUTURES INTRODUCTION AND STOCK MARKET VOLATILITY: EMPIRICAL STUDY IN VIETNAM

2021 ◽  
Author(s):  
鬼谷 子
Keyword(s):  
Stock Market ◽  
Empirical Study ◽  
Policy Maker ◽  
Garch Model ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Index Futures ◽  
Dummy Variable ◽  
Futures Contract

This paper aims at answering the question whether the VN30 index futures introduction has an impact on stock market volatility in Vietnam. Apply GARCH model of volatility with additive dummy variable from 28/7/2000 to 10/9/2020, the result shows that when the first listed index futures contract appears, it makes the volatility of VNIndex increases. The result is still robust after excluding the turmoil period of Vietnam stock market. This paper implies that policy maker should be more careful in promoting derivatives market in Vietnam.

scholarly journals Stock price volatility during the COVID-19 pandemic: The GARCH model

2021 ◽  
Vol 18 (4) ◽  
pp. 12-20
Author(s):  
Endri Endri ◽  
Widya Aipama ◽  
A. Razak ◽  
Laynita Sari ◽  
Renil Septiano
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Practical Implication ◽  
Abnormal Returns ◽  
Garch Model ◽  
Price Volatility ◽  
Efficient Market Hypothesis ◽  
Empirical Literature ◽  
Stock Price Volatility ◽  
The Garch Model

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

Russian Stock Index volatility: Oil and sanctions

2020 ◽  
pp. 86-100
Author(s):  
Artem D. Aganin
Keyword(s):  
Stock Market ◽  
Price Volatility ◽  
Oil Prices ◽  
Stock Index ◽  
Sharp Drop ◽  
Oil Price Volatility ◽  
Russian Stock Market ◽  
The Garch Model ◽  
The Impact ◽  
Parametric Estimate

Since 2014, the Russian stock market has been under pressure due to both sanctions and a sharp drop in oil prices, which led to its increased volatility. This paper analyzes the impact of the price volatility of Brent oil and sanctions on the volatility of the Russian stock index RTS. Under volatility the paper understands both its parametric estimate obtained from the GARCH model estimation as well as non-parametric estimate — realized volatility. To estimate the effect of oil price volatility and sanctions, several cointegrated regressions were analyzed. The robustness of the results in relation to the choice of volatility assessment is demonstrated. The results show that RTS index volatility still depends on oil prices volatility in 2007—2018. This dependence is most pronounced in the periods of crisis. The paper also demonstrates the adjustment of the Russian stock market to the previous sanctions, which calls into question their long-term efficiency.

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