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Effect of Gold Prices

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“Affect of Gold Prices on the Stock Market of Pakistan"

Abstract The research is on the impact of gold prices on the stock market of Pakistan. With an increase in gold prices investors find it more profitable to invest their money into gold as the value of gold per gram is high due to its increased demand. When investors invest into gold naturally they don’t have money to invest in other places like stocks thus stock market is affected by gold prices. The attractive gold price takes the attention of the investor and as a result less investment in stock is made and stock market suffers.
This paper aims to find this relationship between stock market and gold prices. In this research Karachi stock exchange has been considered as the stock market to see the relationship with gold prices. ADF unit root test and granger causality test have been used in this research.
The results showed that data series for both gold and KSE 100 index has integration at first order. The results also showed that there is a relationship between stock market and gold prices and the relationship is bidirectional. Gold prices affect the Karachi stock exchange and Karachi stock exchange also affects gold prices.
Keywords: Gold prices, stock market, gold price and stock market, gold and stock market relationship.

Table of Contents Sr.# | DESCRIPTION | PAGE NO. | 1 | AcknowledgEment……………………………………... | I | 2 | ABSTRACT………………………………………………….. | II | 3 | CHAPTER 1. INTRODUCTION…………………………. 1.1. Overview………………………………………………. 1.2. Research Objective………………………………. 1.3. Problem Statement……………………………… 1.4. Hypothesis ...………………………………….. | 44555 | 4 | CHAPTER 2. LITERATURE REVIEW | 5 | 5 | CHAPTER 3. RESEARCH METHODOLOGY………………………..3.1. Data………………………………...3.2. Sample…………………………………………3.3. Variable……………………………………………….3.4. Statistical model…………………………….. | 7888 | 6 | CHAPTER 4. RESULTS and DISCUSSION……..………... | 8 | 7 | CHAPTER 5 CONCLUSION | 18 | 8 | Bibliography……………………………………... | 19 |

1.Introduction
1.1 Overview Gold is the best option for people in times of national crisis, war, negative real interests and invasions and people like to invest in this solid asset. According to Opdyke (2010) in the times of recession the international investors found gold as a haven to secure their investments. USA, India, and China are the top countries consuming the major portion of the gold produced globally. In Pakistan due to tight economic situation, economic instability, lowering down of economic indicators, and low return in stock market, the Pakistani investors are investing in gold bullion rather than the stock market because of which the demand for gold increased. In 2002 KSE (Karachi stock exchange) was declared the best stock market of the world and by Bloomberg Business Week 2002. In 2007 it was termed the most emerging stock market. But in 2010 especially, the interest of investors diverted from the stock market to gold as the stock market didn’t promise any proper returns and gold seemed to be a more profitable and secure investment.
The paper examines the impact of gold prices on Karachi Stock Exchange (as it is the biggest stock market of Pakistan). As more investors are buying gold it is becoming a trend and attracting more and more investors. In 2010 the gold price exceeded the forty thousand mark on per Tola as of which more investors got interested in gold as it seemed a more attractive investment and shifted from stock market to gold. Thus it is important to see the impact of gold price on the stock market for which Unit Root Test of Augmented Dickey Fuller, Phillip Perron, Johansen’s Correlation Integration test and Granger Causality test have been used in this paper.
1.2 Research Objectives
This research is aimed to determine whether the prices of Gold have any impact on the stock market or not.
1.3 Research Question
What is the impact of change in gold prices on the Karachi Stock Market in Pakistan?
1.4 Hypothesis
H1: Gold prices have an impact on the stock market of Pakistan.
H0: Gold prices have no impact on the Pakistan stock market.

2. Literature Review Levin and Wright (2006) stated that any stock which is kept with its owner for a certain amount of time, gives them on return on its price plus the dividend, as for the gold it is just a hoarding of the value. This concept became a popular believe in the 19th century when there was little commotion in the economy of US and there was political stability. However, in light of recent global recession Pritchard (2010) said that investors have forgone these believe and are more interested in investing into gold. Investments in stock markets are declining every day and as a result stock markets are beginning to crash. Sudden rise in demand of gold has greatly increased the price of gold. Before the global recession, the price of gold in 2005 was US$ 415 per ounce. After the global the recession struck the US economy, the price of gold hit $1000 in 2008. Currently, the price of gold reached to $1421 in November of 2010.
Levin and wright (2006) created a model which showed the causes of price of based on simple economics in the short as well as long run. Based on this model it can be said that supply of gold determines its price. Political instability, financial turmoil and changes in exchange rates and real interest rates are the short-term causes which bring about the variations in the price of gold. This study showed that there is substantially significant relationship between the price of gold and the price level of the US economy. This study also showed that inflation in US economy and price of gold have a positive relationship but a negative relationship between the price of gold and exchange rate of US dollars.
It can be said that stock market is affected by change in the price of gold and many other economic variables. Ratanapakorn and Sharma (2007) conducted a research using the data of the first quarter 9f 1975 till the last quarter of 1999 to study the long as well as short term relationship between the US stock price index. Finding showed a negative correlation between the interest rate and stock price index of the economy. On the other hand, Kolluri (1981) study showed that a relationship between gold price and inflation rate does exist; this can easily be used in favor when doing hedging. But Mahdavi and Zhou (1997), Blose and Shieh (1995), Chan (1998) conclusion were stating the different story altogether. Their findings stated that gold can no longer be termed as inflation defensive asset.
Graham (2001) conducted a research to find out the relation between the price of gold and the stock prices of the economy in long and short run. His study concluded that in the long run there is no relationship between the two variables but in the short stock prices are slightly affected by the price of the gold.
Wang and Haung (2010) conducted a research, using the data from countries like Japan, China, Taiwan, Germany and America. Variables used for the study included oil prices, exchange rates against dollar, gold prices and stock price index of each country. Their findings showed that co-integration exist between all the variables in every country expect for the America. Finding showed that there is no co-integration or relationship between the variable in America in short run as well as long run.
Moore (1990) conducted a research using the data from 1970 to 1988. His findings suggested a negative correlation between the price of gold and stock market which means when the value of gold rises, there is a fall in the stock market. These findings were supported by Büyüksalvarcı (2010). He conducted a research on Turkish stock exchange. He found out that, for Turkish investors, gold is an alternative route. Investors would invest in gold when its prices rise, in turn there would be less investment into stock market. Thus, initiating the stock market prices to drop. Hence, a negative correlation between two variables.
Sharma & Mahendru (2010) piloted a research to find out the impact of economic variables on the prices of gold in India. They used data of one year starting from January 2008 to January 2009. They found out that there is a positive correlation between the gold price and the stock market and gold price does have an impact on the stock market of the economy.

3. Methodology
3.1 Data
Secondary data has been used for the research. Time series data for gold prices and KSE-100 index has been used. The closing prices of KSE-100 index for every month have been used along with the average per month gold price in grams ( price in Pakistan). The data has been used from the year February 2005- 2012. The closing value of KSE 100 index has been collected from Yahoo Finance and gold prices in grams have been collected from forex (online website).
3.2 Variables
Independent Dependent
Gold price (in grams) KSE 100-index (stock market)
3.3 Statistical Model
The statistical models that will be used in this research to test whether gold prices effect the stock market will be: Unit root test of Augmented Dickey Fuller and Granger causality test. All the tests will be carried out on Eviews. Both the time series data for KSE 100-index and gold prices will be tested on Augmented Dickey Fuller model will be used to check that data is stationary. If data of both variables is integrated on the same order then the data will be tested for integration with Johansen co-integration test. Finally to see the relationship between both variables Granger causality test is applied.

4. Results and Discussion
UNIT ROOT TEST ( AUGMENTED DICKEY FULLER)

LEVEL FIRST DIFFERENCE With Trend Without trend With Trend Without Trend Gold price | 0.041405 | 0.011006 | 0.241856*** | 0.223657*** | KSE 100-INDEX | 0.042044 | 0.041183 | 0.189718*** | 0.188192*** | *** Denotes 99% significance level

At level and without trend (gold) ADF Test Statistic | 1.860525 | 1% Critical Value* | -3.5111 | | | 5% Critical Value | -2.8967 | | | 10% Critical Value | -2.5853 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(GOLD) | Method: Least Squares | Date: 11/12/12 Time: 13:34 | Sample(adjusted): 2005:05 2012:02 | Included observations: 82 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | GOLD(-1) | 0.020478 | 0.011006 | 1.860525 | 0.0666 | D(GOLD(-1)) | -0.351912 | 0.117103 | -3.005144 | 0.0036 | D(GOLD(-2)) | 0.051516 | 0.119699 | 0.430382 | 0.6681 | C | 16.29695 | 25.45132 | 0.640319 | 0.5238 | R-squared | 0.153542 | Mean dependent var | 48.31707 | Adjusted R-squared | 0.120986 | S.D. dependent var | 113.9154 | S.E. of regression | 106.8022 | Akaike info criterion | 12.22739 | Sum squared resid | 889724.0 | Schwarz criterion | 12.34479 | Log likelihood | -497.3228 | F-statistic | 4.716220 | Durbin-Watson stat | 1.966592 | Prob(F-statistic) | 0.004459 |

Data is stationary at lag 2 as probability is above 5% indicating that the data cannot be used for forecasting. R-square value is significant since the value is near 15.3%. Durbin Watson is the closest to 2 around 1.96 which is showing negative autocorrelation. Difference of R –square and adjusted R-square is less than 5% showing no Sample error. T-statistic is greater than 2 indicating significance.
Augmented Dickey Fuller Statistic is 1.85 showing fail to reject the hypothesis of presence of unit root ie, Gold is stationary. In statistics and econometrics, an Augmented Dickey–Fuller test (ADF) is a test for a unit root in a time series sample. It is an augmented version of the Dickey–Fuller test for a larger and more complicated set of time series models. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence. To further conduct the test we will have to incorporate the trend in the test since ADF is higher than critical value
At level and with trend (gold) ADF Test Statistic | -1.372596 | 1% Critical Value* | -4.0727 | | | 5% Critical Value | -3.4645 | | | 10% Critical Value | -3.1585 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(GOLD) | Method: Least Squares | Date: 11/12/12 Time: 13:35 | Sample(adjusted): 2005:05 2012:02 | Included observations: 82 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | GOLD(-1) | -0.056832 | 0.041405 | -1.372596 | 0.1739 | D(GOLD(-1)) | -0.316248 | 0.116565 | -2.713062 | 0.0082 | D(GOLD(-2)) | 0.071778 | 0.118115 | 0.607702 | 0.5452 | C | 17.88804 | 25.02898 | 0.714693 | 0.4770 | @TREND(2005:02) | 3.917429 | 2.025177 | 1.934363 | 0.0567 | R-squared | 0.192769 | Mean dependent var | 48.31707 | Adjusted R-squared | 0.150834 | S.D. dependent var | 113.9154 | S.E. of regression | 104.9732 | Akaike info criterion | 12.20433 | Sum squared resid | 848492.1 | Schwarz criterion | 12.35108 | Log likelihood | -495.3773 | F-statistic | 4.596940 | Durbin-Watson stat | 1.968029 | Prob(F-statistic) | 0.002212 | | | | |

Data is still stationary at second lag as probability is above 5% (exactly 54.5%) indicating that the data cannot be used for forecasting. R-square is 19.2% shows variance in the data and is significant as F-statistics is greater than 4. Durbin Watson is 1.968 showing the level and trend and intercept to be valid yet autocorrelation is negative. Difference of R –square and adjusted r-square is less than 5 % showing no Sample error.
Augmented Dickey Fuller Statistic is -1.37which is higher than the T-statistic at 10% level (-3.1) showing fail to reject of hypothesis After this analysis we need to further conduct Unit root test on the gold, while taking first difference and intercept in consideration till the data has attained non-stationary status.

At first difference with trend (gold) ADF Test Statistic | -6.701310 | 1% Critical Value* | -4.0742 | | | 5% Critical Value | -3.4652 | | | 10% Critical Value | -3.1589 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(GOLD,2) | Method: Least Squares | Date: 11/12/12 Time: 13:37 | Sample(adjusted): 2005:06 2012:02 | Included observations: 81 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | D(GOLD(-1)) | -1.620751 | 0.241856 | -6.701310 | 0.0000 | D(GOLD(-1),2) | 0.276500 | 0.193742 | 1.427153 | 0.1576 | D(GOLD(-2),2) | 0.236429 | 0.115361 | 2.049468 | 0.0439 | C | 10.80271 | 24.65268 | 0.438196 | 0.6625 | @TREND(2005:02) | 1.547660 | 0.557803 | 2.774564 | 0.0070 | R-squared | 0.702408 | Mean dependent var | 1.185185 | Adjusted R-squared | 0.686745 | S.D. dependent var | 185.9581 | S.E. of regression | 104.0792 | Akaike info criterion | 12.18792 | Sum squared resid | 823268.2 | Schwarz criterion | 12.33573 | Log likelihood | -488.6108 | F-statistic | 44.84584 | Durbin-Watson stat | 2.047723 | Prob(F-statistic) | 0.000000 |

Taking Gold figures in consideration the data was re analyzed on E-Views implementing the unit root test. While taking difference and incorporating intercept both the following was derived:
Data is now Non-stationary as probability is less than 5 %( exactly 0.1%) indicating that the data will or can be used for forecasting. R-square value is highly significant since the value is near 70.2%.
Durbin Watson is the closest to 2 around 2.04 showing the 1st difference & trend to be valid hence Difference of R –square and adjusted r-square is less than 5 % showing no Sample error. T-Statistic is Greater than 2 Indicating significance. Augmented Dickey Fuller Statistic is -6.7 which is lower than the T-statistic at 10% level (-3.1) showing rejection of hypothesis.
At first difference without trend (gold) ADF Test Statistic | -5.861007 | 1% Critical Value* | -3.5121 | | | 5% Critical Value | -2.8972 | | | 10% Critical Value | -2.5855 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(GOLD,2) | Method: Least Squares | Date: 11/12/12 Time: 13:39 | Sample(adjusted): 2005:06 2012:02 | Included observations: 81 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | D(GOLD(-1)) | -1.310858 | 0.223657 | -5.861007 | 0.0000 | D(GOLD(-1),2) | 0.050845 | 0.183334 | 0.277334 | 0.7823 | D(GOLD(-2),2) | 0.142941 | 0.115029 | 1.242645 | 0.2178 | C | 63.80213 | 16.24808 | 3.926750 | 0.0002 | R-squared | 0.672264 | Mean dependent var | 1.185185 | Adjusted R-squared | 0.659496 | S.D. dependent var | 185.9581 | S.E. of regression | 108.5117 | Akaike info criterion | 12.25971 | Sum squared resid | 906658.8 | Schwarz criterion | 12.37796 | Log likelihood | -492.5184 | F-statistic | 52.64851 | Durbin-Watson stat | 2.007662 | Prob(F-statistic) | 0.000000 |

Taking Gold figures in consideration the data was re analyzed on E-Views implementing the unit root test. While taking difference and incorporating intercept both the following was derived:
Data is now Non-stationary as probability is less than 5 %( exactly 0.1%) indicating that the data will or can be used for forecasting. R-square value is highly significant since the value is near 70.2%.
Durbin Watson is the closest to 2 around 2.04 showing the 1st difference & trend to be valid hence Difference of R –square and adjusted r-square is less than 5 % showing no Sample error. T-Statistic is Greater than 2 Indicating significance. Augmented Dickey Fuller Statistic is -6.7 which is lower than the T-statistic at 10% level (-3.1) showing rejection of hypothesis.

At level without trend (kse) ADF Test Statistic | -1.890217 | 1% Critical Value* | -3.5111 | | | 5% Critical Value | -2.8967 | | | 10% Critical Value | -2.5853 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(KSE) | Method: Least Squares | Date: 11/12/12 Time: 13:40 | Sample(adjusted): 2005:05 2012:02 | Included observations: 82 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | KSE(-1) | -0.077844 | 0.041183 | -1.890217 | 0.0624 | D(KSE(-1)) | 0.135518 | 0.111500 | 1.215406 | 0.2279 | D(KSE(-2)) | -0.052580 | 0.109568 | -0.479884 | 0.6327 | C | 861.0886 | 437.7552 | 1.967055 | 0.0527 | R-squared | 0.059894 | Mean dependent var | 50.05329 | Adjusted R-squared | 0.023736 | S.D. dependent var | 821.3831 | S.E. of regression | 811.5762 | Akaike info criterion | 16.28338 | Sum squared resid | 51375166 | Schwarz criterion | 16.40079 | Log likelihood | -663.6188 | F-statistic | 1.656464 | Durbin-Watson stat | 1.998861 | Prob(F-statistic) | 0.183267 |

Data is still stationary at second lag as probability is above 5% (exactly 63.2%) indicating that the data cannot be used for forecasting. R-square is 5.9% shows variance in the data and model is insignificant as F-statistics is less than 4. Durbin Watson is 1.99 showing the level and trend and intercept to be valid yet autocorrelation is negative. Difference of R –square and adjusted r-square is less than 5 % showing no Sample error.
Augmented Dickey Fuller Statistic is -1.89 which is higher than the T-statistic at 10% level (-2.5) showing fail to reject of hypothesis After this analysis we need to further conduct Unit root test on the KSE, while taking first difference and intercept in consideration till the data has attained non-stationary status.
At level with trend (kse) ADF Test Statistic | -1.849845 | 1% Critical Value* | -4.0727 | | | 5% Critical Value | -3.4645 | | | 10% Critical Value | -3.1585 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(KSE) | Method: Least Squares | Date: 11/12/12 Time: 13:41 | Sample(adjusted): 2005:05 2012:02 | Included observations: 82 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | KSE(-1) | -0.077775 | 0.042044 | -1.849845 | 0.0682 | D(KSE(-1)) | 0.135459 | 0.112386 | 1.205292 | 0.2318 | D(KSE(-2)) | -0.052676 | 0.110710 | -0.475801 | 0.6356 | C | 862.0303 | 450.8714 | 1.911921 | 0.0596 | @TREND(2005:02) | -0.038108 | 3.874718 | -0.009835 | 0.9922 | R-squared | 0.059895 | Mean dependent var | 50.05329 | Adjusted R-squared | 0.011059 | S.D. dependent var | 821.3831 | S.E. of regression | 816.8287 | Akaike info criterion | 16.30777 | Sum squared resid | 51375101 | Schwarz criterion | 16.45452 | Log likelihood | -663.6187 | F-statistic | 1.226446 | Durbin-Watson stat | 1.998867 | Prob(F-statistic) | 0.306607 |

Data is still stationary at second lag as probability is above 5% (exactly 63.5%) indicating that the data cannot be used for forecasting. R-square is 5.9% shows variance in the data and model is insignificant as F-statistics is less than 4. Durbin Watson is 1.998 showing the level and trend and intercept to be valid yet autocorrelation is almost null but bordering on negative. Difference of R –square and adjusted r-square is less than 5 % showing no Sample error.
Augmented Dickey Fuller Statistic is -1.84 which is higher than the T-statistic at 10% level (-3.1) showing fail to reject of hypothesis After this analysis we need to further conduct Unit root test on the D(KSE), while taking first difference and intercept in consideration till the data has attained non-stationary status.

At first difference without trend ADF Test Statistic | -4.862423 | 1% Critical Value* | -3.5121 | | | 5% Critical Value | -2.8972 | | | 10% Critical Value | -2.5855 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(KSE,2) | Method: Least Squares | Date: 11/12/12 Time: 13:42 | Sample(adjusted): 2005:06 2012:02 | Included observations: 81 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | D(KSE(-1)) | -0.915067 | 0.188192 | -4.862423 | 0.0000 | D(KSE(-1),2) | 0.015879 | 0.151719 | 0.104663 | 0.9169 | D(KSE(-2),2) | -0.061487 | 0.111226 | -0.552808 | 0.5820 | C | 53.67777 | 92.86043 | 0.578048 | 0.5649 | R-squared | 0.462073 | Mean dependent var | 14.72963 | Adjusted R-squared | 0.441115 | S.D. dependent var | 1111.995 | S.E. of regression | 831.3123 | Akaike info criterion | 16.33201 | Sum squared resid | 53213171 | Schwarz criterion | 16.45025 | Log likelihood | -657.4464 | F-statistic | 22.04734 | Durbin-Watson stat | 2.009670 | Prob(F-statistic) | 0.000000 |

Taking KSE figures in consideration the data was re analyzed on E-Views implementing the unit root test. While taking difference and incorporating intercept both the following was derived:
Data is now Non-stationary as probability is less than 5 %( exactly 0.1%) indicating that the data will or can be used for forecasting. R-square value is highly significant since the value is near 46.2%. The data also shows that impact is seen on the first lag only.
Durbin Watson is the closest to 2 around 2.00 showing the 1st difference & trend to be valid hence Difference of R –square and adjusted r-square is less than 5 % showing no Sample error. T-Statistic is Greater than 2 Indicating significance. Augmented Dickey Fuller Statistic is -4.86 which is lower than the T-statistic at 10% level (-2.56) showing rejection of hypothesis.

At first difference with trend ADF Test Statistic | -4.853793 | 1% Critical Value* | -4.0742 | | | 5% Critical Value | -3.4652 | | | 10% Critical Value | -3.1589 | *MacKinnon critical values for rejection of hypothesis of a unit root. | | | | | | | | | | | Augmented Dickey-Fuller Test Equation | Dependent Variable: D(KSE,2) | Method: Least Squares | Date: 11/12/12 Time: 13:44 | Sample(adjusted): 2005:06 2012:02 | Included observations: 81 after adjusting endpoints | Variable | Coefficient | Std. Error | t-Statistic | Prob. | D(KSE(-1)) | -0.920851 | 0.189718 | -4.853793 | 0.0000 | D(KSE(-1),2) | 0.019323 | 0.152763 | 0.126490 | 0.8997 | D(KSE(-2),2) | -0.058875 | 0.112004 | -0.525651 | 0.6007 | C | 127.0294 | 199.2307 | 0.637599 | 0.5257 | @TREND(2005:02) | -1.660216 | 3.983545 | -0.416769 | 0.6780 | R-squared | 0.463299 | Mean dependent var | 14.72963 | Adjusted R-squared | 0.435052 | S.D. dependent var | 1111.995 | S.E. of regression | 835.8090 | Akaike info criterion | 16.35442 | Sum squared resid | 53091831 | Schwarz criterion | 16.50222 | Log likelihood | -657.3539 | F-statistic | 16.40149 | Durbin-Watson stat | 2.009313 | Prob(F-statistic) | 0.000000 |

Data is still non-stationary at first lag as probability is above 5% (exactly 0.00%) indicating that the data can be used for forecasting. R-square is 46% shows variance in the data and model is significant as F-statistics is more than 4 indicating that model is fit for use. Durbin Watson is 2.00 showing the level and trend and intercept to be valid yet there is no autocorrelation. Difference of R –square and adjusted r-square is less than 5 % showing no Sample error.
Augmented Dickey Fuller Statistic is -4.85 which is lower than the T-statistic at 10% level (-3.1) showing rejection of null hypothesis After this analysis we need don’t need to conduct to further conduct Unit root test on the KSE, as data has attained non-stationary status.

GRANGER CAUSALITY TEST Pairwise Granger Causality Tests | | Sample: 2005:02 2012:02 | Lags: 2 | Null Hypothesis: | Obs | F-Statistic | Probability | D(GOLD) does not Granger Cause D(KSE) | 82 | 1.09568 | 0.33947 | D(KSE) does not Granger Cause D(GOLD) | 1.22312 | 0.29996 |

The granger causality test is used for explorative purposes by economists and political scientists working with time series, In both cases of either gold affecting KSE or KSE affecting gold it is seen that we cannot reject the null hypothesis as the values of probability are coming to be insignificant. We fail to reject the null hypothesis whenever the p-value is greater than the 0.05

5.0 Conclusion After applying the Unit root test and granger causality test on the KSE and gold figures the test indicates that when applied at level testing for both trend and intercept the data.
Further carrying out the test while taking the first difference and intercept it is found that the data has become non-stationary indicating that the data now can be used for forecasting.
Furthermore the augmented dickey fuller value and the t-statistic value when compared conclude that the hypothesis “Gold prices have no impact on the Pakistan stock market” is rejected as it is seen that after every one year at level both figures for KSE AND GOLD were seen to reject the null hypothesis due to their augmented dickey-fuller statistics going below the 10% critical value for t-statistic.
Meanwhile the granger causality test also rejects the null hypothesis that gold prices have no impact on Pakistan stock market as the statistics for this hypothesis ruled to be insignificant on both model fitness and model significance hence proving that when H0 is rejected then resulting is that H1 is accepted.
The recent gold prices have seen a paradigm shift in stock prices as if the gold prices increase then the value for international currency also increases which have an effect on price of oil import, export and so on hence an overall general increase or decrease in the stock prices depending on the performance of those companies in comparison to rising gold prices and related currency prices or setbacks.
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