THE ASYMMETRIC VOLATILITY OF THE ISLAMIC CAPITAL MARKET DURING THE COVID-19 PANDEMIC

This study attempts to identify the existence of asymmetric volatility in the Islamic capital market in Indonesia during the Covid-19 pandemic. The paper employs the symmetric analysis of the GARCH (1,1) model and the asymmetric analysis of the TGARCH (1,1) model in order to identify Islamic capital market behaviour duringthe first 200 days after the first Covid-19 cases were confirmed. We used the daily closing prices of the Indonesia Sharia Stock Index (ISSI). The symmetric analysis of the GARCH (1,1) model revealed that the current value of return on the ISSI does not have a significant impact on its future value. On the other hand, the TGARCH (1,1) model showed that the asymmetric parameter coefficient was positive and statistically significant. Good news and bad news does not have the same level of impact on the volatility of returns on the ISSI. Furthermore, coefficients αi and γi in the variance equation indicate that good news has a higher volatility impact than bad news. The results indicate that investors should not to worry about the bad news effect of theCovid-19 pandemic, while the government should continue the mitigation of the spread of the coronavirus along with its economic recovery policy.

Do US News and Volatility in Exchange Rate Exposure Matter (Empirical Evidence from Emerging Economies)

This study is aimed to examine the impact of US News and exchange rate exposure on emerging economies of Pakistan, China, Turkey and Iran. Daily exchange rates have been used for the period Jan 1, 2003 to Dec 31, 2018 to identify the volatility in exchange rate exposure due to news effect. US News is modeled with variance equation respectively for each country exchange rate. GARCH (1,1) by Bollerslev (1986), and EGARCH (1,1) by Nelson (1991) models have been used to estimate the volatility of exchange rate dynamics. Results indicate that impact of US News is significantly positive on the exchange rate of Pakistan and China and the results of US news impact on Turkey and Iran are insignificant. Present study is helpful for investors, financial analysts and economic decision makers for understanding the changing dynamics of exchange rate volatility.

Possible Impact of Facebook’s Libra on Volatility of Bitcoin: Evidence from Initial Coin Offer Funding Data

This paper examines the impact of Libra on volatility of Bitcoin using the classical framework of C. G. Lamoureux and W. D. Lastrapes (1990). ARCH and GARCH effects disappear when lagged ICO funding size is included in the variance equation. A negative association between volatility and funding size and the disappearance of volatility persistence (long-term volatility effect) suggest that Libra, as a dominant new currency, is likely to stabilize the cryptocurrency market and enhance potential for currency diversification. Furthermore, it is revealed that the stability cannot be ensured merely by backing decentralized blockchain instruments, such as Bitcoin, with bank deposits, government securities or exchange rate.

Do seasonality, break and spillover effects explain commodity price volatility

Purpose The purpose of this paper is to understand the volatility in commodity futures and spot markets. The study starts with a few questions: first, the effect of seasonality on the volatility is studied. Thereafter, the presence of structural breaks in the variance is identified. At last the seasonality, structural shifts and spillover effects are examined together to find out their effects on volatility. Design/methodology/approach The methodology heavily employs econometric tools and techniques. The monthly seasonal dummies are incorporated to identify the effects of seasonality on volatility. Then, the presence of break in volatility is tested by cumulative sum of squares (CUSUM test), followed by generalized autoregressive conditional heteroscedastictity and EGARCH models are measured by including seasonal dummies, break dummies and the residuals of other market in the variance equation to determine spillover effects. Findings It is found that the effects of seasonality on volatility cannot be ignored as the effects are significant. The presence of asymmetry is detected in all the commodities. The presence of seasonality and structural breaks in the variance equation are statistically able to reduce the volatility but the magnitude is very negligible with an exception in cumin futures markets. Bi-directional volatility spillover between futures and spot markets is observed in all the commodities and the effect of spillover is more from spot markets to the futures markets. Research limitations/implications This study is limited to a few agro commodities which are well traded. This study could have been extended to the other thinly traded commodities. This study has also taken only near month futures contracts as it contains more information but the same could have been studied by taking far month contracts also. Originality/value The present study attempted to understand the conjugated effects of seasonality, structural breaks and spillover on volatility of commodity markets which is not apparent in the previous studies. This study has also employed methodological rigor to identify the breaks in the variance equation. In addition to this it has also investigated whether Indian commodity futures markets are informationally more efficient than the spot markets.

Price Volatility Analysis in Indonesian Beef Market

The Indonesian beef price movement increasing erratically and tends to be volatile in recent years. Based on the price monitoring in several production centers, there are beef price fluctuations in the consumer level across time and between provinces. This study tries to present the relationship between the beef price volatility and Indonesia’s efforts to ensure food security through self-sufficiency in beef. We consider a series of consumer daily beef price from January 2006 to December 2013, with total T=2086 observations to understand beef price volatility in Indonesia, and to analyze the impact of beef self-sufficiency program to the beef price volatility in Indonesia. Data was obtained from Ministry of Trade, Government of Indonesia and it was collected through market survey from three different markets in 33 capital provinces in Indonesia. The methodology follows GARCH model to measure the beef price volatility. The GARCH (1.1) model gives information that beef price movements are influenced by the volatility from the previous period and yesterday’s variance. The volatility of beef price was driven more by its own variance rather than external shocks. GARCH (1.1) model shows that the beef price volatility will tend to be smaller and persistence in the future. Parameter of the third dummy variable in the variance equation to capture the change policy is statistically significant. It indicates that the beef self-sufficiency program may lower the beef price volatility. Keywords: beef price, garch model, price volatility, self sufficiency

Information arrival and volatility: Evidence from the Saudi Stock Exchange (Tadawul)

This paper investigates the validation of the Mixture of Distributions Hypothesis (MDH) using trading volume and number of trades as contemporaneous proxies for information arrival in 15 sector indices of the Saudi Stock Exchange (Tadawul) using the TGARCH model. Findings provide strong evidence for the validity of the MDH for the Saudi market. Volatility persistence decreases when the trading volume and the number of trades are included in the conditional variance equation. The most striking finding is that contemporaneous number of trades is a better proxy for information arrival than trading volume, interacting with volatility in a manner anticipated under the MDH. This can be attributed to the unique characteristic of the Saudi equity market where only domestic investors are allowed to execute trade transactions. Further, the results reveal that the leverage effect was amplified, indicating a more pronounced asymmetric effect of bad news on volatility.

Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

This paper aims to propose the augmented GJR-GARCH (GJR-GARCH_M) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR’s variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH_M model outperforms the GJR-GARCH model for most cases, suggesting that the GJR-GARCH-based VaR forecasts can be moderately improved with the additional information embodied in the ONV, PK and VIX volatility estimators. In addition, daily high-low prices range and VIX are far more informative than the overnight volatility estimator for improving the GJR-GARCH-based VaR forecasts. Risk managers can employ the proposed models for estimating and controling the potential loss of ETFs in the face of financial catastrophes.

Budget Of The Scalar Variance Equation In A Turbulent Patch Arising From Lee Wave Breaking

Based on averaged data of the direct numerical simulations, statistical moments are obtained in a turbulent patch arising after lee wave overturning in a flow with stable stratification and obstacle. Temporal evolution and spatial behavior of the scalar-variance transport equation budget have been studied. A priori estimations of algebraic approximations for scalar dissipation, scalar variance and turbulent-diffusion processes in the scalar-variance equation have been carried out. Such an analysis is helpful to explore the turbulent patch in terms of statistical moments, and to verify closure hypotheses in turbulence models. In the global balance of the scalar-variance equation, the compensation of production by dissipation and advection is shown, as for the turbulent kinetic energy equation. The ratio of turbulent time scales of the scalar and velocity fields varies from 0.2 to 2.2 within the wave breaking region, and the global value of this parameter is close to unity during the quasisteady period. The algebraic expression derived from the assumption of production and dissipation balance is incorrect leading to unphysical negative values, therefore the use of the full scalarvariance equation in the turbulent transport model is justified.

VIX and the variance of Dow Jones industrial average stocks

Purpose – The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach – GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings – Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications – In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles. Originality/value – Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.