An investigation of stock market volatility: evidence ...

Journal of Economic and Administrative Sciences An investigation of stock market volatility: evidence from Dubai financial market Hussein Mohammad Salameh, Bashar Alzubi,

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An investigation of stock market volatility: evidence from Dubai financial market

Investigation of stock market volatility

Hussein Mohammad Salameh College of Administrative and Financial Sciences, King Khalid University, Abha, Saudi Arabia, and

Bashar Alzubi

Received 11 April 2017 Revised 31 July 2017 Accepted 16 September 2017

Arab Open University ( Jordan Branch), Amman, Jordan


Abstract Purpose – The purpose of this paper is to assess the sources of Dubai Financial Market Index volatility shocks if they are from its own or previous shocks on the one hand, or if they are out board shocks (FSTE and S&P500) on the other. Design/methodology/approach – A daily time series data were collected over the period 1st January 2014-31st December 2015 and the generalized autoregressive conditional heteroskedasticity (GARCH) methodology was implemented. Findings – Empirically, the authors find that the current volatility of Dubai Financial Market Index is largely dependent on its own shocks and part of the external shock; in particular, S&P500. However, other external volatility (FSTE) cannot contribute to this volatility. Furthermore, our findings indicate that Abu Dhabi stock Exchange (APX) affects Dubai Financial Market Index. Practical implications – These results conclude that Securities Regulation Department in the federal state of United Arab Emirates had captured the effect of outside shocks from the UK only, but not from USA; this is basically due to the strong ties between the two countries. Accordingly, UAE investors seek capital outside their home country within a climate of increasing overseas’ investment options in the UK. More transparency of transactions via information technology will increase the efficiency of Dubai Financial Market. Originality/value – To the best of the knowledge, this is the first work that shows the external and internal sources of volatility shocks at once; previous studies have focused almost exclusively on one type of shocks. To investigate DFM volatility shocks, the authors employed GARCH methodology; this method is an advanced econometric method and is often a preferred method to depict actual effects because it provides a more real-world context than other forms when trying to predict volatility shocks of financial instruments. Keywords GARCH, Abu Dhabi stock index (APX), Dubai Financial Market Index ( DFM ), FSTE, Index volatility shocks, S&P500 Paper type Research paper

1. Introduction Several literatures investigated the volatility between stock exchanges which employ generalized autoregressive conditional heteroskedasticity (GARCH) models (Al Rjoub, 2009), (Budd, 2014). Several literatures provide evidence on linkages and contagion effects among different countries and their respective financial markets as well as monetary policy (see for example Hussain, 2010). Further literature reports an increasing amount of evidence on the predictability in volatility across various financial assets and markets. In a world of market uncertainties, the study of cluster volatility is of crucial prominence to the research topic of global risk. (Budd, 2014). Empirical observations of linked patterns of stock price activity between national and international stock exchanges are becoming more apparent as markets are moving closer together. Manifestly this observation was particularly witnessed during the recent Global Financial Crisis in 2008. When the USA faced the crisis, contagion captures other countries as reported by Al-Zu’bi et al. (2016). These waves of global asset price movements have been further enthused by the improved cooperation of heightened cross-border capital flows, transparency of transactions via advance technology, the ease of capital mobility regulations, as

Journal of Economic and Administrative Sciences © Emerald Publishing Limited 1026-4116 DOI 10.1108/JEAS-04-2017-0020


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well as added competitive market pressure on corporations to seek capital in the oversee within a climate of increasing overseas’ investment options (Budd, 2014). In consequence, this study raises several questions; does the volatility of one market leads the volatility of other markets in the United Arab Emirates? Does the previous volatility of financial market index leads to the current volatility? And does the previous day’s Dubai Financial Market Index information about volatility influences today’s volatility of Dubai Financial Market Index? One would expect that the answers to these questions will provide a greater insight and assistance into future global portfolio risk management, policymakers, and financial analysts alike in the United Arab Emiratis Stock Exchanges. Our paper makes several major contributions to the literature. First, this is, to our knowledge, the first study that assesses the sources of Dubai Financial Market Index volatility shocks if they are from its own or previous shocks on the one hand, or if they are out board shocks (FSTE and S&P500) on the other. Previously, there has been a wide range of empirical studies which investigate the volatility spillover effects on Dubai Financial Market. However, these studies have focused almost exclusively on the effect of individual markets (see, e.g., Maghyereh and Awartani, 2012). This procedure highlights the distinctive features of the volatility spillover effects on the stock market but it makes it difficult to determine at once whether these effects are own shocks, or outside shocks on the stock market. Our second contribution is that we apply the new advanced GARCH methodology to investigate own volatility and outside volatility. The econometric method has received little attention in the financial markets literature. The GARCH process is often a preferred method to depict actual effects because it provides a more real-world context than other forms when trying to predict volatility shocks of financial instruments. So this research creates a strong linkage with the industry. The rest of the paper is organized in the following manner. Section 2 summarizes the main previous studies which investigates the stock market index volatility shocks. Section 3 focuses on the methodology and the data used. The result and analysis follow in Section 4. Finally, conclusion is presented in Section 5. 2. Literature review Although the assumption in the financial press indicated that the stock market index volatility shocks are influenced by their own factors or by the volatility of macroeconomic factors (outside shocks), empirical evidence regarding the impact of main factors, own shocks, or outside shocks on volatility of stock returns have been mixed. Investigating the impact of monetary policy in stock returns in Europe and USA, Hussain (2010) showed that monetary policy decisions generally exert an immediate and significant influence on stock index returns and volatilities in both European and US markets. The findings also indicate that European Central Bank’s press conferences following monetary policy decisions on the same day have define impacts on European index return volatilities, implying that they convey important information to market participants. Overall, the analysis suggests that the use of high frequency data is critical for separating the effects of monetary policy actions from those of macroeconomic news announcements on stock index returns and volatilities. From their side, Liljeblom and Stenius (1997) suggested that stock market volatility is a predictor for macroeconomic volatility, as well as the converse. Tests of the explanatory power of the macroeconomic volatilities indicate that big part of the changes in aggregate stock volatility might be related to macroeconomic volatility. Some evidence of a negative relationship between stock market volatility and trading volume growth was also detected. Highlighting the USA and European markets further, Corradi et al. (2010) illustrated that the level of stock market volatility cannot be merely explained by business cycle factors.


Rather, it relates to the presence of some unobserved factor. At the same time, their model predicts that such an unobservable factor cannot explain the ups and downs stock volatility experiences over time of volatility. Instead, the volatility of stocks relates to the business cycle. Furthermore, Rigobon and Sack (2002) showed that the response of asset prices to changes in monetary policy can be identified based on the increase in the variance of policy shocks that occurs on days of FOMC meetings and of the Chairman’s semi-annual monetary policy testimony to Congress. Moreover, Chatziantoniou et al. (2013) results show that both fiscal and monetary policies influence the stock market, via either direct or indirect channels. More importantly, the results indicated that the interaction between the two policies is very important in explaining stock market developments. Thus, investors and analysts in their effort to understand the relationship between macroeconomic policies and stock market performance should consider fiscal and monetary in cycle rather than in isolation. From their side, Bjørnland and Leitemo (2005) found great interdependence between interest rate setting and stock prices; a major part of the surge in stock prices at the end of the 1990s is attributed to these non-fundamental shocks. To discuss further, D’Amico and Farka (2003) illustrated that monetary policy responds in a positive fashion to contemporaneous changes in the stock market, but this relationship is not significant. In addition, the results show that stock returns respond negatively to a positive monetary policy shock and that this response is significant at 1 percent level. Gali and Gambetti (2015) pointed to protracted episodes in which, after a short-run decline, stock prices increase persistently in response to an exogenous tightening of monetary policy. Those responses are clearly odd with the “conventional” view on the effects of monetary policy on bubbles, as well as with the predictions of bubble less models. On the other hand, Ozdagli and Yu (2012) do not support the hypothesis that stock prices of financially constrained firms are more responsive to monetary policy shocks, which seems to contradict the financial accelerator theory presented in Bernanke et al. (1999) but is consistent with Lamont et al. (2001) who find that the relative stock market performance of constrained firms does not reflect monetary policy or credit conditions. Furthermore, Bredin et al. (2007) have conducted a study on the UK market; the variance decomposition results indicate that the monetary policy shock leads to a persistent negative response in terms of future excess returns for a number of sectors. From their side, Ahmed et al. (2006) showed that the estimated coefficients of money supply and money demand equations from the structural VAR model are theoretically consistent, suggesting that the short-run identification restrictions are valid. Finally, Caldara et al. (2016) indicated that financial shocks have a significant adverse effect on economic outcomes and those shocks were an especially important source of cyclical fluctuations since the mid-1980. Moving to discuss it in the Middle East and Asia, Albaity (2011) found that in the variance univariate models of the conventional indices, the M1, M3, inflation rate, and real growth in GDP are significant in influencing Islamic stock market index in Malaysia volatility, while M2, M3, inflation rate and interest rate affected Islamic Stock Market Index in the US volatility. Al Rjoub (2009) studied the market in Jordan and measured the impact of the financial crisis on stock market returns and volatility; the results illustrated that volatility behavior during crises behaves in different manners in the Amman Stock Exchange. However, imported crises caused volatility to decrease or increase based on the general public expectations; if expectations are pessimistic, the effect will be resembled by dampen demand for investment causing volatility to decrease and the size of trading to decrease. On the other hand, if expectations are optimistic volatility will increase derived by the increased size of investment. The results indicated that local stock market crash during 2005 and the global financial crises of 2008 showed no impact on volatility with insignificant coefficients. Staying in the Middle East, Maghyereh and Awartani (2012) investigated return and volatility spillover effects between the Dubai Financial Market and the Abu Dhabi



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Stock Exchange (APX) using two models: the VAR-BEKK model and the AG-DCC model. They reached to a conclusion that the returns and volatilities of the Dubai are important in forecasting the future dynamics of the Abu Dhabi, however, the returns and volatilities in Abu Dhabi have less impact on the future dynamics of Dubai. This means that the information is incorporated first into stocks’ prices in Dubai before being held into the stocks’ prices in Abu Dhabi. They also found that Abu Dhabi responds with a delay to market information. Moving back to Asia, Chen (2014) showed that the price fluctuation of each market has an influence on other markets, although the price behavior is significantly independent. From his side, Purnomo and Rider (2013) investigated the market of Indonesia and found evidence that the Jakarta Composite Stock Market Index is cointegrated with several domestic macroeconomic variables. In addition, he found that the Indonesian-dollar exchange rate has bidirectional influences on the Jakarta Composite Index. In addition to domestic macroeconomic variables, he reported evidence that the Composite Index is cointegrated with the stock market indexes of several Southeast Asian stock markets. Turning to Africa, Oluseyi (2015) studied the market of Nigeria and used monthly data for a period of January 1990-December 2014. The results showed that the volatility in GDP, inflation and money supply were not found to Granger-cause and not significantly related to stock market prices volatility. However, only volatility in interest rate and exchange rate does Granger-cause stock market prices volatility; while from the regression analysis side, only interest rate volatility and exchange rate are significantly linked with stock market prices volatility. This finding is permissible in the case of developing countries with the rule of non-institutional investors and the existence of information asymmetry problem among investors which could account for the weak relationship between stock market prices volatility and macroeconomic variables’ volatility. From his side, Yonis (2011) compared between USA and South African stock markets; he found evidence of return spillover from NYSE to JSE by analyzing VAR based on two lags. While analyzing the MA-GARCH model, empirical results exhibit that volatility spillover between US and SA is perseverance. 3. Methodology 3.1 Data selection and data collection In our data base, our aim was to construct data for volatility of Dubai Financial Market Index return. In addition we obtained the FSTE Index, APX Index and S&P500 data. A daily time series data were collected from January 1, 2014 to December 31, 2015. After assessing the availability and quality of data and most important periods for which the data were available, we arrived to 502 observations for each of the variables of the model. 3.2 Daily return The daily return is the function of the price of index in the current day and the price of index in the previous day. This can be represented in the following equation: Rti ¼ ðP ti –P ti1 Þ=P ti1 where Rti is the return of the index for the current day; Pti the price of the index for the current day; and Pti−1 the price of the index for the previous day. 3.3 Unit root test Before conducting estimation and in order to avoid possible spurious regression, it is necessary to distinguish stationary from non-stationary variables. The first step undertaken would be to establish the order of integration of variables used in the model. This is accomplished by applying first the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP)

tests on each of the series in the estimated equations, standard unit root tests. The well-known ADF test for a unit root in yt, omitting a linear deterministic trend is: X diDyt1 þet Dyt ¼ aþbyt1 þ


where Δ is the difference operator, εt is a white noise disturbance term with variance σ2, and t ¼ 1 …, T indexes time. The Δyt−i terms on the right-hand side of equation allow for serial correlation and are designed to ensure that εt is white noise. The empirical evidence suggests that there is no time trend in the data. The ADF (parametric test) test and PP (non parametric test) has a null hypothesis of non-stationarity (random series) against an alternative of stationarity (non random series). 3.4 ARCH and GARCH Time series data usually exhibit three main characteristics. First, they exhibit volatility clustering or volatility pooling. In other words, periods of high volatility will be followed by periods of high volatility and the same applies for periods of low volatility. Second, their distribution is leptokurtosis, which mean that the distribution fat tailed. Third characteristic is the leverage effect. The leverage effect is the fact that bad news affects returns more than good news. In other words, changes in the prices tend to be negatively correlated with changes in volatility. Therefore modeling such series needs to be extended using other models. The first two characteristics have been successfully modeled using ARCH (Autoregressive Conditional Heteroskedasticity) by Engle (1982) and GARCH) developed by Bollerslev (1986). The idea of ARCH and GARCH is to model the variance of the error term from the mean equation on the previous squared error terms. If the mean equation is as follow: Y t ¼ ai þyi Y t1 þbi X t1 þet where Yt is the dependent variable, Xt is the independent variable, and εt is the error term and αI and βI are the coefficients. The error term εt ∼ N (0, δ2) is assumed to have zero mean and a constant variance or homoscedasticity. However, it is unlikely in the financial time series that the variance of the error term be Homoskedastic. Ignoring the fact that the variance of the error term is Heteroskedastic will result in either over/under estimation of the standard error and therefore bias inferences. To overcome this problem ARCH model is used. The arch model is as follow: s2t ¼ oþ

p X

@i e2t1

i¼1

where s2t is the conditional variance, e2t1 the lagged term of the squared error term from the mean equation, and ω and αi the coefficients. This model indicates that the variance of the error term is dependent on the lagged squared error term. Such model is referred to as ARCH (q) where (q) indicates the lag order of the squared error term in the variance equation. Although ARCH model is capable of eliminating the heteroskedasticity in the mean equation, it still has some drawbacks that led to the development of GARCH model. GARCH model was developed by Bollerslev (1986) who indicated that a GARCH model with smaller number of terms can perform as well as or even better than ARCH model with many lags. The idea of the GARCH model is simply to include the lagged value of the variance in the variance equation. The GARCH model is as follows: s2t ¼ o þ

q X i¼1

@i e2ti þ

p X j¼1

gj s2tj þ

l X v¼1

|v X tv



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The first term in the right-hand side is the ARCH term explained earlier, while the second term is the lagged variance that is GARCH. This model is referred to as GARCH ( p,q) where (q) is the lagged ARCH term and ( p) is the GARCH lagged term. The above model indicate that ω is the long-term average variance, αi is the information about the volatility in the previous period, and the beta is the coefficient of the lagged conditional variance. Although GARCH model is better than ARCH specification since it is more parsimonious and less likely to breach the non-negative constraint it is still does not account for the leverage effect in the apparent in financial time series and does not allow for any direct feedback between the conditional variance and the conditional mean. 3.5 Development of ARCH and GARCH model The following steps illustrate the way of analyzing the results and which can apply to our GARCH (1, 1) Model. In this paper, we have chosen daily data for estimating GARCH (1, 1) Model. We conducted the following steps. Step 1: check the stationary of all the variables. Step 2: the mean equation of our GARCH (1,1) Model is as follows: DFM ¼ C1þ C 2 APX þe where DFM, Dubai Financial Market Index; C, constant; APX, Abu Dhabi Stock Index; e, residuals. (We have taken daily data of 502 days). Step 3: we plot the residual of Dubai Financial Market (DFM)derived from mean equation of 502 days, it is worth mentioning that there is a prolonged period of low volatility from day 1 to other day and also there is a prolonged period of high volatility from one day to another. In other words, periods of high volatility are followed by high volatility and periods of low volatility are followed by low volatility. This suggests that residuals or error term is conditionally heteroscedastic and it can be represented by ARCH and GARCH model. Step 4: variance equation: residuals derived from mean equation is used in making variance equation as below: H t ¼ C 3 þC 4 H t1 þC 5 e2 t1 þC 6 Lag1FTSEþC 7 Lag1S&P500 where Ht is the variance of the residual (error term) derived from mean equation, it is also can be known as current day’s variance or volatility of DFM, C3 the constant, Ht−1, the previous day’s residual variance or volatility of DFM, it is known as GARCH term, e2t1 the previous day’s squared residual derived from mean equation, it is also known as previous day ‘s DFM information of volatility. It is ARCH term. Lag1FSTE the Lag1 of London Stock Exchange Index. It is an exogenous or predetermined variable Lag1S&P500:Lag1 of USA Stock Exchange Index. It is an exogenous or predetermined variable. Step 5: the three types of distribution that will be used to test the variance equation: (1) Normal Gaussian distribution. (2) Student’s t with fixed df. (3) Generalized Error Distribution GED with fixed parameters. Under the three distributions we will test if ARCH is significantly affecting Ht; i.e. previous day’s DFM information on volatility can influence today’s volatility of DFM. In addition, under the three distributions we will test if GARCH is significantly affecting Ht; i.e. previous day’s residual volatility of DFM (Ht−1) can influence today’s volatility of DFM (Ht). This means that DFM volatility is influenced by its own ARCH and GARCH factors or own shocks.

The following is to test if FSTE London Stock Exchange Index is significantly affecting Ht; i.e. lag1 of London Stock Exchange Index Return volatility or outside shocks can influence the volatility of DFM. Finally, we will test if S&P500USA Stock Exchange Index is significantly affecting Ht; i.e. Lag1 of USA Stock Exchange Index Return volatility or outside shocks can influence the volatility of DFM. Step 6: model selection: we need to select the most appropriate model to be implemented in this study. Doing so will suggest proper hypotheses to fulfill. First hypothesis:


H10. There is no serial correlation in the residual or error term. H1A. There is a serial correlation in the residual or error term. Second hypothesis: Downloaded by 79.173.255.95 At 13:20 09 April 2018 (PT)

H20. There is no ARCH effect. H2A. There is ARCH effect. Third hypothesis: H30. Residuals are normally distributed. H3A. Residuals are not normally distributed. Correlogram square residual (Q test) will be performed in H10-H1A and Jarque Beru Statistics will be applied in H30-H3A. 4. Results This section provides the detailed results of this study; the first part shows the unit root test results, while the second part provides the discussion of GARCH (1.1) mean and variance equations. In part three, we discuss the decision of the model selection. Finally, in the fourth part we evaluate the models under three distributions. 4.1 Unit root results This study uses the ADF statistics (parametric test). The null hypothesis is that there is a unit root in the index return. If the null hypothesis is rejected, it means that the time series is stationary (not random). The results in Table I show that the null hypothesis of the unit root has been rejected under the ADF test at 1, 5 and 10 percent significance level with intercept for the DFM, APX, FSTE and S&P500. This indicates that the series are stationary (not random) at level I(0) at 1 percent significance level.

With intercept Variables

t-statistics

Dubai Financial Market Index DFM −20.08192*** Abu Dhabi Stock Exchange Index APX −21.69273*** London Stock Exchange Index FSTE −21.97867*** S&P00 −21.60808*** Note: *,**,***Significant at 10, 5, and 1 percent levels, respectively

p-value 0.0000 0.0000 0.0000 0.0000

Table I. Unit root tests (Augmented Dickey-Fuller)

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4.2 Discussion of GARCH(1,1) model, mean equation and variance equation 4.2.1 Mean equation


DFM ¼ C1 þC 2 APXþe where DFM, Dubai Financial Market Index; C1, constant; APX, Abu Dhabi Stock Market Index; e, residuals. Daily data of 502 days have been applied. The results in Table II indicated that the adjusted R2 is 58.65 percent which means that the independent variables explain 58.65 percent of the variations in the dependent variable but not all of it. This means that there are other variables which explain the dependent variable. Furthermore, we can reject the main null hypothesis; there is no significant effect of all the variables on the DFM and accept the alternative hypothesis which indicate that there is significant effect of all the variables on the DFM. We based our rejection on the fact that p-value and F-statistics are less than 1 percent (1-confidence level (99 percent)), so it falls within the rejection area. Moreover, we can reject the null hypothesis which indicates that there is no significant effect of the APX on the DFM, because the p-value is less than 1 percent (1-confidence level (99 percent)). This means that APX significantly affects the DFM. The residual derived from mean equation can be plotted as given in Figure 1.

Dependent variable: DFM β

Coefficients

Table II. OLS regression

t-value

p-value

(Constant) −3.41E-05 −0.058916 APX 1.289598*** 26.67855 0.587372 R2 0.586547 Adjusted R2 Durbin-Watson 2.158711 F 711.7453 p-value 0.000000 Note: *,**,***Significant at 10, 5, and 1 percent levels, respectively

0.9530 0.0000

0.15 0.10 0.05 0.00 0.08 –0.05 0.04 –0.10 0.00 –0.04 –0.08

Figure 1. Residuals of DFM for 502 days

–0.12 I

II

III

IV

I

II

2014

III 2015

Residual

Actual

Fitted

IV

As shown in Figure 1, there is a prolonged period of low volatility from day 1 to day 502, but the prolonged period of high volatility (there is some high volatility for small periods of time) does not exist. In other words, periods of low volatility are followed by low volatility. This suggests that residuals or error term is conditionally heteroscedastic and can be represented by both ARCH and GARCH model. 4.2.2 Variance equation. Normal Gaussian distribution:



H t ¼ C 3 þC 4 H t1 þC 5 e2t1 þC 6 Lag1FSTE þC 7 Lag1S&P500 Residuals derived from mean equation are used in making variance equation. Table III shows that under this distribution GARCH (Ht−1) or C4 is significant at 1 percent as the p-value is 0.0000. This means that previous day’s residual volatility of DFM (Ht−1) can influence today’s volatility of DFM(Ht). Moreover, Table III shows that ARCH(e2t1 ) or C5 is significant at 1 percent as the p-value is 0.37 percent. This means that previous day’s DFM information on volatility can influence today’s volatility of DFM (Ht). These results indicate clearly that Dubai Financial Market Index (DFM) volatility is influenced by its own ARCH and GARCH factors or own shocks. Furthermore, Lag1FSTE London Stock Exchange Index or C6 is not significant meaning that FSTE cannot convey effect to DFM. Finally, Lag1S&P500 USA Stock Exchange Index is significant or C7 is significant at 5 percent as the p-value is 1.92 percent, meaning that S&P500 return volatility or outside shocks can influence the volatility of DFM. Student’s t with fixed df: H t ¼ C 3 þC 4 H t1 þC 5 e2t1 þC 6 Lag1FSTE þC 7 Lag1S&P500 Residuals derived from mean equation are used in making variance equation. The results in Table IV indicate that GARCH (Ht−1) or C4 is significant at 1 percent level as the p-value is 0.0000, it means that previous day’s residual volatility of DFM (Ht−1) can influence today’s volatility of DFM (Ht). In addition, the results show that ARCH (e2t1 ) or C5 is significant at 5 percent as the p-value is 1.44 percent indicating that previous day’s DFM information on volatility can influence today’s volatility of DFM (Ht). These results

Coefficients (Constant) APX

β

0.000142 1.160181 Variance equation model Dependent variable: Ht C 1.52E-05 RESID(−1)^2 0.103455*** GARCH(−1) 0.804987*** Lag1FSTE −0.000711 Lag1S&P500 −0.002264** 0.581731 R2 0.580893 Adjusted R2 Durbin-Watson 2.093381 Note: *,**,***Significant at 10, 5, and 1 percent levels, respectively

z-value

p-value

0.285150 28.44707

0.7755 0.0000

2.926347 2.899030 15.54346 −0.933183 −2.342404

0.0034 0.0037 0.0000 0.3507 0.0192 Table III. Normal Gaussian distribution

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Coefficients

β

(Constant) APX


Table IV. Student’s t with fixed df

9.80E-05 1.176005 Variance equation model Dependent variable: Ht C 1.54E-05 RESID(−1)^2 0.108997** GARCH(−1) 0.803050*** Lag1FSTE −0.000552 Lag1S&P500 −0.002217* 0.583126 R2 2 0.582291 Adjusted R Durbin-Watson 2.101303 Note: *,**,***Significant at 10, 5, and 1 percent level, respectively

z-value

p-value

0.200721 27.09599

0.8409 0.0000

2.371531 2.446898 12.80282 −0.554148 −1.875282

0.0177 0.0144 0.0000 0.5795 0.0608

demonstrate that DFM volatility is influenced by its own ARCH and GARCH factors or own shocks. Furthermore, Lag1FSTE London Stock Exchange Index or C6 is not significant meaning that FSTE cannot convey effect to DFM. Finally, Lag1S&P500 USA Stock Exchange Index is significant or C7 is significant at 10 percent as the p-value is 6.08 percent, meaning that S&P500 Return volatility or outside shocks can influence the volatility of DFM. It means that we have received the same result when the distribution was normal Gaussian. Generalized error distribution GED with fixed parameters: H t ¼ C 3 þC 4 H t1 þC 5 e2t1 þC 6 Lag1FSTEþC 7 Lag1S&P500 Residuals derived from mean equation are used in making variance equation. Table V shows that GARCH (Ht−1) or C4 is significant at 1 percent as the p-value is 0.0000 indicating that previous day’s residual volatility of DFM (Ht−1) can influence today’s

Coefficients (Constant) APX

Table V. GED with fixed parameters

β

0.000128 1.159705 Variance equation model Dependent variable: Ht C 1.54E-05 RESID(−1)^2 0.107824** GARCH(−1) 0.802983*** Lag1FSTE −0.000597 Lag1S&P500 −0.002289* 0.581697 R2 2 0.580859 Adjusted R Durbin-Watson 2.093196 Note: *,**,***Significant at 10, 5, and 1 percent levels, respectively

z-value

p-value

0.266924 27.03888

0.7895 0.0000

2.264661 2.290973 12.03416 −0.582183 −1.829593

0.0235 0.0220 0.0000 0.5604 0.0673


volatility of DFM(Ht). It is also shown that ARCH (e2t1 ) or C5 is significant at 5 percent as the p-value is 2.20 percent meaning that previous day’s DFM information on volatility can influence today’s volatility of DFM. These results illustrate that DFM volatility is influenced by its own ARCH and GARCH factors or own shocks. Furthermore, Lag1FSTE London Stock Exchange Index or C6 is not significant meaning that FSTE cannot convey effect to DFM. Finally, Lag1S&P500 USA Stock Exchange Index is significant or C7 is significant at 10 percent as the p-value is 6.73 percent, meaning that S&P500 Return volatility or outside shocks can influence the volatility of DFM. Here we have received the same result when the distribution was Normal Gaussian and student’s t with fixed df. 4.2.3 Decision of the model. So, we can conclude that the volatility of Dubai Financial Market Index (DFM) is largely dependent on its own shocks such as ARCH and GARCH. But volatility of FSTE cannot contribute in the volatility of Dubai Financial Market Index (DFM).While S&P500 contribute in the volatility of Dubai Financial Market Index (DFM). 4.2.4 Evaluation of models under three distributions. We need to select the most appropriate model to be implemented in this study. Doing so will suggest suitable hypotheses to be fulfilled. Testing H1. Table VI shows that we cannot reject the null hypothesis which indicates that there is no serial correlation in the residual or error term as the probability is more than 10 percent (1-confidence level (90 percent)). This means that the first hypothesis has been fulfilled for the three distributions. Testing H2. Table VII below shows that we cannot reject the null hypothesis which indicates that there is no ARCH effect as the probability χ2(1) is more than 10 percent (1-confidence level (90 percent)). Accordingly, the second hypothesis has been fulfilled for the three distributions. Testing H3. Table VIII shows that we cannot reject the null hypothesis which indicates that residuals are normally distributed as the p-value is more than 10 percent (1-confidence level (90 percent)) for normal Gaussian and GED with fixed parameters, 5 percent (1-confidence level (95 percent))for Student’s t. This means that the third hypothesis has been fulfilled for the three distributions. The three models have normality of residuals and no serial correlation of the residuals as well as no ARCH effect which indicate that three of them can be accepted. 5. Conclusion Dubai Stock Exchange is one of the biggest exchanges in Gulf and in the Middle East region. Empirical studies have evidence that its stock prices and index affect all the exchanges in the surrounding countries. In addition, it is the fastest exchange that contagion to the USA and UK exchanges. Furthermore, investors in the United Arab Emiratis invest usually in Dubai Financial market or APX. Therefore, this paper has offered important contributions to the existing literature about the sources of Dubai Financial Market Index (DFM) volatility shocks if they are from its own or previous shocks on the one hand, or if they are out board shocks (FSTE and S&P500) on the other. To the best of our knowledge, this is the first work that shows the external and internal sources of volatility shocks at once; previous studies have focused almost exclusively on one type of shocks. To investigate DFM volatility shocks, we employed GARCH methodology; this method is an advanced econometric method and is often a preferred method to depict actual effects because it provides a more real-world context than other forms when trying to predict volatility shocks of financial instruments.



JEAS

Table VI. Serial correlation in the residual

Normal Gaussian Q-Stat Prob*

Student’s t with fixed df Q-stat Prob*

GED with fixed parameters Q-stat Prob*

0.0746 0.0851 3.5143 4.1860 4.3624 5.2171 5.2568 5.2941 9.7904 9.8855 10.292 10.395 10.411 10.866 11.807 12.818 12.915 13.244 13.641 13.658 13.663 13.676 13.797 13.983 17.317 18.796 20.229 20.554 20.993 22.169 22.585 22.789 22.822 25.979 28.933 29.903 29.907 29.943 30.196 30.649 31.559 34.689 35.060 35.366 35.378 35.606 35.770 35.970 36.260 36.289

0.1199 0.1350 3.5085 4.1581 4.3589 5.2142 5.2638 5.2764 9.5445 9.6006 9.9340 10.023 10.061 10.423 11.514 12.560 12.673 13.055 13.431 13.432 13.434 13.481 13.560 13.760 16.738 18.165 19.518 19.867 20.370 21.300 21.747 21.953 22.009 25.303 28.394 29.272 29.293 29.301 29.630 30.150 31.101 34.352 34.620 34.900 34.937 35.164 35.306 35.485 35.763 35.831

0.0874 0.1015 3.5125 4.1392 4.3282 5.1541 5.1938 5.2238 9.6041 9.6885 10.083 10.181 10.205 10.599 11.582 12.551 12.657 13.024 13.424 13.434 13.441 13.464 13.567 13.776 16.965 18.396 19.771 20.104 20.570 21.635 22.082 22.281 22.320 25.396 28.382 29.366 29.377 29.398 29.675 30.148 31.050 34.337 34.665 34.956 34.969 35.250 35.411 35.601 35.879 35.916

0.785 0.958 0.319 0.381 0.498 0.516 0.629 0.726 0.368 0.451 0.504 0.581 0.660 0.697 0.694 0.686 0.742 0.777 0.804 0.847 0.884 0.913 0.933 0.947 0.870 0.845 0.821 0.843 0.859 0.848 0.864 0.885 0.908 0.836 0.755 0.753 0.790 0.821 0.843 0.856 0.855 0.781 0.800 0.820 0.847 0.866 0.884 0.900 0.912 0.927

0.729 0.935 0.320 0.385 0.499 0.517 0.628 0.728 0.389 0.476 0.536 0.614 0.689 0.731 0.715 0.705 0.758 0.788 0.816 0.858 0.893 0.919 0.939 0.952 0.891 0.870 0.850 0.869 0.881 0.878 0.891 0.909 0.927 0.860 0.778 0.779 0.813 0.843 0.861 0.871 0.869 0.793 0.815 0.835 0.860 0.877 0.895 0.910 0.921 0.934

0.768 0.951 0.319 0.388 0.503 0.524 0.636 0.733 0.383 0.468 0.523 0.600 0.677 0.717 0.710 0.705 0.759 0.790 0.816 0.858 0.892 0.919 0.939 0.952 0.883 0.861 0.840 0.861 0.874 0.867 0.880 0.900 0.920 0.856 0.778 0.775 0.810 0.840 0.859 0.871 0.870 0.794 0.814 0.833 0.859 0.875 0.893 0.907 0.919 0.933


The results showed that the current volatility of DFM is largely dependent on its own shocks such as previous day’s DFM information about volatility (ARCH) and previous day’s residual volatility of DFM (GARCH). But volatility of FSTE do not contribute in the volatility of DFM, while S&P500 contribute in the volatility of DFM which means that outside shocks can influence the volatility of DFM. In addition, APX affects Dubai Financial Market Index (DFM). This result is very realistic especially after Dubai financial crisis in 2009; Abu Dhabi rescued Dubai with billions of dollars to allow the later to repay its debt. Since then, Abu Dhabi is certainly on the rise. Abu Dhabi is the capital city of the UAE and it is the major oil exporter in the whole Arab Gulf region. Abu Dhabi depends heavily on oil exports, thus, the main feature of the Emirates stock markets is that they are very sensitive to changes in oil prices. Furthermore, testing the model under the three assumptions (normal Gaussian assumption, Student’s t with fixed df assumption, GED with fixed assumption) have shown the normality of residuals, no serial correlation of the residuals and no ARCH effect. Therefore, the assumptions can be accepted as parameters. Based on these results, several points can be highlighted; first, United Arab Emirates Securities Regulation Department had captured the effect of outside shocks from UK stock exchange, but not from the US stock Exchange; this means that UAE is an open market for waves of global asset price movements from the USA and UAE investors seek capital outside their home country within a climate of increasing overseas’ investment options in the UK. Nevertheless, these outside shocks from the US do extend entirely. Second, Dubai Financial Market Index and APX are the main national stock exchanges in UAE. However, investing in one of them will certainly affect the other because of common distribution between them. Imposing more inside regulations will reduce the side effect between them. Finally, we conclude that more transparency of transactions via information technology will increase the efficiency of Dubai Financial Market. This will lead for the previous volatility and previous information to embed in current volatility directly and immediately. Accordingly, we recommend that further research which investigates cross-volatilities between Dubai Financial Market Index and economic events is necessary.

Normal Gaussian Distribution F-statistic Obs × R2

0.073777 0.074062

Prob. F(1, 498) Prob. χ2(1)

0.7860 0.7855

Student’s t with fixed df Distribution F-statistic 0.118492 0.118939 Obs × R2

Prob. F(1, 498) Prob. χ2(1)

0.7308 0.7302

GED with fixed parameters F-statistic Obs × R2

Prob. F(1, 498) Prob. χ2(1)

0.7689 0.7684

Normal Gaussian Jarque-Beru p-value

0.086383 0.086715

Student’s t with fixed df 3.953046 0.138550

Jarque-Beru p-value

5.338899 0.069290


Table VII. Heteroskedasticity test: ARCH

GED with fixed parameters Jarque-Beru p-value

4.353914 0.113386

Table VIII. Normally distribution for residuals


JEAS

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Oluseyi, A. (2015), “An empirical investigation of the relationship between stock market prices volatility and macroeconomic variables’ Volatility in Nigeria”, European Journal of Academic Essays, Vol. 2 No. 11, pp. 1-12. Ozdagli, A. and Yu, Y. (2012), “Monetary policy shocks and stock returns: identification through impossible trinity”, Working Paper No. 12-18, Federal Reserve Bank of Boston, available at: www.bostonfed.org/economic/wp/wp2012/wp1218.htm Purnomo, B. and Rider, M. (2013), “Domestic and foreign shocks and the indonesian stock market: time series evidence”, working paper. Rigobon, R. and Sack, B. (2002), “The impact of monetary policy on asset prices”, Working Paper No. 8794, National Bureau of Economic Research. Yonis, M. (2011), “Stock market co-movement and volatility spillover between USA and South Africa”, working paper, UMEA UNIVERSITET. Further reading Kearney, C. and Daly, K. (1998), “The causes of stock market volatility in Australia”, Applied Financial Economics, Vol. 8 No. issue 6, pp. 597-605. About the authors Hussein Mohammad Salameh is an Associate Professor in Finance, College of Administrative and Financial Sciences, King Khalid University, Saudi Arabia since October 2016. Also, he is the Head of Development and Quality Assurance Department Unit in College of Administration and Financial Sciences, King Khalid University, Saudi Arabia since December 2016. He was an Associate Professor in Finance/Amman Arab University (1 year), and was the Director of Accreditation and Quality Assurance Department (1.5 year), Acting Manger of Admission and Registration Department (half year) and an Associate Professor (since 2011) in Finance/Arab Academy for Banking and Financial Sciences enrolled at the university since 2006, and teaches as a Visiting Lecturer for three semesters in MBA program (part time)/Arab Academy, Damascus branch, Syria. His researches have been accepted in several leading academic journal (covering several subjects, i.e. corporate finance, investment, portfolio management, financial markets). Hussein Mohammad Salameh is the corresponding author and can be contacted at: [email protected] Bashar Alzubi recently is an Assistant Professor, Arab Open University ( Jordan Branch). He is a Visiting Professor in several local and regional universities. Prior to his current position, he worked as a Senior Advisor to the CEO of the Jordan Investment Board ( JIB), and as a Research Assistant on a Corporate Governance project in AIM, London. He has completed his PhD in Financial Economics from the University of Birmingham, having previously finished his MSc in Finance from Queen’s University of Belfast. His research has been accepted in several leading academic journal and many international conferences including the EFMA conference (Europe’s best in finance).

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