The rising oil prices

Oil prices have an effect into the real economy, by increasing cost to firms and by reducing the amount of disposal income that consumers have to spend. As a consequence, it can be expected that rising oil prices have a negative effect into the level activity of an economy and into its stock markets as well. Previous authors like Sadorsky (1999), Mohan Nandha and Hammoudeh (2007), Park and Ratti (2008) established that rising oil prices tend to decrease the stock returns. The present study tries to determine the relationship between oil prices and stock returns using a more recent sample data. The selected stock market is Karachi Stock Exchange of Pakistan. Pakistan's economy deserves a special attention in this document. First, its stock market affects the other stock markets in Asia. Second, Pakistan is one of the leading oil exporting country, Mustafa and Nishat (2003). As a result any relationship between oil prices and the Karachi stock market is reflected into other economies. Oil shocks affect the stock markets as well as the real economy itself; VAR approach will be the main tool in the present research, since it allows examining the dynamic interaction between economic variables.

Sadorsky (1999) Use a VAR model to investigate the relationship between oil prices, interest rate, industrial production, consumer price index and stock markets in the U.S. The sample data starts in January 1950 and ends in April 1996. The Stock market returns Sadorsky used the S&P500 index. Sadorsky established the interest rate, oil prices, industrial production and real stock return ordering. Sadorsky found that stock markets explain most its own variance changes during the entire sample period; Sadorsky also found that interest rate shocks have a greater impact on real stock and industrial production than oil prices. However when he divides the sample period in two sub periods, 1950:01 to 1985:12 and 1986:01 to 1996:04, Sadorsky discovered that in the second sub period oil prices shocks have played a greater role explaining industrial production and real stock returns variance. Sadorsky also conducted test for symmetry between positive and negative oil shocks into the economy, Sadorsky concluded that negative oil shocks have a greater impact then positive oil shocks into stock markets and the level of economic activity. Sadorsky also discovered that increasing volatility of oil prices have a negative effect into stock markets. Jones and Kaul (1996) studied the stock markets of the United States, Japan, Canada and the United Kingdom and their reaction to oil price shocks; the hypothesis is that oil shocks are absorbed by present and future changes in actual cash flow and in expected returns. Then, stock returns should vary across time due to changes in current and expected returns. The evidence supports that the stock markets of Canada and the United States capture the impact of oil shocks into their cash flows, because oil prices don't have an effect on real stock returns. In case of the United Kingdom and Japan the evidence shows that their stock markets tend to over react to oil price changes.

Basher and Sadorsky (2004) used an international multifactor model to investigate the relationship between oil prices and emerging stock markets returns. Data cover the period from 1992:12 to 2005:10, and include 21 emerging stock markets; Argentina, Brazil, Chile, Colombia, India, Indonesia, Israel, Jordan, Korea, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, South Africa, Sri Lanka, Taiwan, Thailand, Turkey, and Venezuela. An unconditional version of the model is estimated among world stock returns, country market return, oil price and exchange rate. And then, a conditional version of the model is estimated, which includes a dummy variable to differentiate when the market is up and down. The estimation procedure for both versions includes two steps; First country stock beta, oil price beta, and exchange rate beta are estimated using pooled OLS. Second, a cross sectional regression is estimated for a pooled data set of realized returns and risk parameters. They found that oil prices do affect stock returns with a coefficient significant at 5% level in most cases. In the conditional model a test of symmetry is conducted, to examine if emerging markets react in the same way to market betas (sources of risk) when the market is up and down. They found that there is a significant asymmetrical relationship between market betas and returns in ups and downs.

Nandha and Hammoudeh (2007) studied the effect of oil price changes and exchange rate into stock markets returns in 15 countries in the Asia-Pacific region before and after the Asia financial crises of 1997. Nandha and Hammoudeh use data from 1994:05 2004:06 Total return indices are used for stock market indices. Nandha and Hammoudeh estimated conditional and unconditional systematic risk betas within the International APT framework for each market. According to this paper the domestic market risk toward world stock market changes can be affected by oil prices and exchange rates. High beta countries face bigger wins (losses) than low beta countries when the world stock market is up (down). High beta countries included; Hong Kong, Malaysia, Singapore and South Korea. Low beta countries include; New Zealand, Pakistan, Philippines and Sri Lanka. Thirteen of the fifteen countries show a significant sensitivity to changes in oil prices in terms of local prices, regardless the world stock market is up or down. But China and Thailand only show oil price sensitivity only if the world stock market is down. Indonesia and Malaysia, net export countries, show significant negative sensitivity when the oil price (in local prices) is down.

Park and Ratti (2008) estimated the effect of oil price shocks and oil price volatility in the U.S, and 13 European countries over 1986:1 - 2005:12. A multivariate VAR analysis is used capture the dynamic relation between the following variables; Industrial production, stock returns and interest rate are from the OECD in case of the European countries, and for the U.S. the S&P 500 from the COMPUSTAT, the interest rate from FRED, and industrial production from the OECD. Oil prices are U.K. Brent in dollars per barrel from the IMF. The 13 European countries are Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, and the U.K. Oil price shocks had a statistical significant impact on real stock returns in the same month or within one month. Despite other countries, Norway, a net exporter of oil, shows a positive response to oil price increase in their stock returns. The median result from variance decomposition analysis is that oil price shocks account for a statistically significance of 6% of the volatility in real stock returns. For many European countries, increased price volatility depresses stock returns, but does not for the U.S. A one standard deviation increase in the world oil price significantly raises the short term interest rate in the U.S. and eight European countries with a lag of one or months. The null hypothesis of symmetric effects on real stock returns of positive and negative oil price shocks cannot be rejected for the oil importing European countries but is rejected for Norway and the U.S.

Nandha and Faff (2008) examined how oil prices changes affect the equity price and then, they explore if there is any asymmetric impact of oil price on equity returns. They use monthly data from 35 industrial sectors, from the globally diversified industry portfolios (as presented in DataStream global industry indices). All data is measured in dollars and the oil price is the West Texas Intermediate Cushing expressed in dollars per barrel. They consider that portfolios are free from country specific factors and are more suitable for measuring the impact of oil prices into equity prices. They use the standard market model augmented by the oil price factor. They find that in 33 industry sectors oil prices have a significant and negative impact, these industrial sectors include industries such as; Aerospace, Auto and parts, Banks, Beverages, Chemical, Construction, Food and drugs retailers, Forestry, Insurance companies, Hotels and Leisure, Telecommunications and Transport. Oil and mining are the two remaining industries in which oil prices have a positive impact. Their finding suggest that oil prices have a negative impact on real output and hence an adverse effect on corporate profits where oil is used as an input. When price effect asymmetry is tested they find that oil price change effect on equity price is symmetric, not asymmetric as expected.

Cong, Wei, Jiao, Fan (2008) Studied the effect of oil price shocks on the real stock return of China. They use data from the Shanghai and Shenzhen stock markets; from them they use two composite indices, 10 classification indices, and four company stock prices to examine the Chinese market. Brent oil price data from the EIA is used as the oil price variable. Exchange rate and interest data are form the Bank of China. And Industrial production data is from the National Bureau of Statistics of China. Their results reveal that oil price shocks do not show a significant impact on stock returns in China. But stock returns in manufacturing index and some oil price index are increased due to some oil shocks. Asymmetric effect of oil price changes on oil companies is not supported by statistical evidences. Increase in price oil volatility may not affect most stock returns, but may increase speculation in mining index and petrochemicals index which raise stock returns. Both oil price shocks and China oil price shocks can explain much more than interest rates for manufacturing index. This means that oil price changes are a source of monthly volatility in its stock returns. The relative importance of interest rates and oil price varies across different indices and oil company stock returns in the stock market.

Oil is the lifeblood of modern economies. As countries urbanize and modernize their demand for oil increases significantly. Future oil demand is difficult to predict but is generally highly correlated with the growth in industrial production. Consequently, countries experiencing rapid economic growth are the ones most likely to dramatically increase their demand for oil. In particular, countries like China and India are experiencing rapid growth in Gross Domestic Product (GDP). Between 1991 and 2001 China's average annual growth rate in real GDP was 9.8% while India's average annual growth rate in real GDP was 5.4% (The Economist, 2004). In the future, emerging economies in general, and China and India in particular, are expected to consume an increasing share of the world's oil. Energy, financial markets and the economy are all explicitly linked together on a country's path of economic growth. Higher oil prices act like an inflation tax on consumers and producers by 1) reducing the amount of disposable income consumers have left to spend on other goods and services and 2) raising the costs of non-oil producing companies and, in the absence of fully passing these costs on to consumers, reducing profits and dividends which are key drivers of stock returns. In addition to global demand and supply conditions, oil prices also respond to geopolitics, institutional arrangements (OPEC), and the dynamics of the futures market (Sadorsky, 2004). Unanticipated changes in any of these four factors can create volatility, and hence risk, in oil futures prices. Oil price volatility increases risk and uncertainty which negatively impacts stock returns and reduces wealth and investment.

The relationship between oil price changes and stock returns can be explained using an equity pricing model. In an equity pricing model, the price of equity at any point in time is equal to the expected present value of discounted future cash flows (Huang, Masulis, & Stoll, 1996). Oil, along with capital, labour and materials represent important components into the production of most goods and services and changes in the prices of these inputs affects returns. Rising oil prices, which, in the absence of complete substitution effects the factors of production and increases production costs. Higher production costs dampen cash flows and reduce stock returns. Rising oil prices also impact the discount rate used in the equity pricing formula. Rising oil prices are often indicative of inflationary pressures which central banks can control by raising interest rates. Higher interest rates make bonds look more attractive than stocks leading to a fall in stock returns. The overall impact of rising oil prices on stock returns depends of course on whether a company is a consumer or producer of oil and oil related products. Since there are more companies in the world that consume oil than produce oil, the overall impact of rising oil prices on stock markets is expected to be negative.

Developed economies are more energy efficient today than they were 20 years ago with oil consumption per dollar of GDP less than half of what it was in the 1970s. This increase in energy efficiency has occurred because of reduced energy intensity through technological innovation and more reliance on a diversified range of energy sources (like a greater mix between non-renewable and renewable energy sources). Emerging economies tend, however, to be more energy intensive than more advanced economies and are therefore more exposed to higher oil prices. Consequently, oil price changes are likely to have a greater impact on profits and stock returns in emerging economies.

Globalization, broadly defined as the increased flow of goods, services and financial capital between national borders, has increased interdependencies between all economies in the world. Consequently, the growth in world trade is more sensitive to rises in oil prices than in the past due to the growing importance of emerging economies like Brazil, China and India. The increased flow of portfolio money (in the form of stocks, bonds and mutual funds) means that oil price impacts on emerging stock markets affect both domestic and international investors alike.

Moreover, past experience has shown that oil price shocks have a much larger impact on the poorer countries in the world. The OPEC oil embargo of 1973, which increased the price of oil from $3 per barrel to $13 barrel in just over a few short months, created real economic and social hardship for developing countries by raising their costs of imported oil. International lending organizations like International Monetary fund (IMF) had to provide loans to developing countries so that they could continue with their economic development projects (Rifkin, 2002, chapter 9). Between 1973 and 1980 commercial bank loans to developing countries increased by 550%. The second oil price shock in 1979 led to global recession and imposed even more hardship on the prosperity of developing countries as the price for their oil imports rose and the price for their other export products fell. By 1985 Third World Debt exceeded $1 trillion dollars. The problem for most developing countries was that any new borrowed money was mostly being used to buy imported oil and pay interest payments on existing debt. Very little money was left over for new economic development projects. This relationship between high oil prices, high debt and low economic development is very much a concern today. In 2000, Kofi A. Annan, the Secretary General of the United Nations, wrote in the International Herald Tribune, that "liability -servicing expenses are likely to raise if higher oil prices guide to higher worldwide interest rates" in the coming years (Annan, 2000).

There is now a growing body of published research on the relationship between energy prices and stock returns. Most of the research has focused on the developed countries. The paper by Chen, Roll, and Ross (1986) is one of the first papers to systematically investigate the impact of macroeconomic innovations on stock returns. They found that interest rates, inflation rates, bond yield spreads, and industrial production have risk that is priced in the stock market. They did not, however, find any evidence that oil price risk is rewarded by the stock market. Hamao (1989) applied the approach of Chen et al. (1986) to a sample of Japanese equity data and also found no evidence for the pricing of an oil price factor. Kaneko and Lee (1995), using a more recent sample of Japanese equity data did find some evidence in favor of an oil price factor impacting stock returns. Ferson and Harvey (1995) find evidence that an oil price risk factor does have a statistically significant but different impact on the 18 equity markets that they study.

Using the Producer Price Index for Fuels as a measure of oil prices, they do find a relationship between oil prices and stock market returns. After including future industrial production into the analysis.

Huang et al. (1996) main focus on the daily oil future returns and the daily stock returns of U.S. Using a vector auto regression (VAR) approach, From this research they found that oil future returns do not have much force on market indices in broad base like the S&P 500, but oil future lead oil company stock return. They also find that oil futures volatility leads the petroleum stock index volatility.

Sadorsky (1999) estimates a vector auto regression model with monthly data to study the relationship between oil prices changes and real stock returns in the United States. In his analysis, he finds that oil price alteration and oil price instability both play vital roles in upsetting real stock returns.. After 1986, oil price movements explain a larger fraction of the forecast error variance in real stock returns than do interest rates. There is also confirmation that oil price instability shocks have a symmetric effect on the market. Particularly, positive oil price shocks have a greater impact on stock returns and economic activity than do negative oil price shocks. Faff and Brailsford (1999) investigate the sensitivity of Australian industry equity returns to an oil price factor. Their analysis is carried out using monthly data over the period 1983 to 1996. They find a positive and significant impact of oil prices on the Oil and Gas and Diversified Resources industries and a negative and significant impact of oil prices on the Paper and Packaging, and Transportation industries.

Sadorsky (2003) uses monthly data from July 1986 to April 1999 to investigate the macroeconomic determinants of U.S. stock price conditional volatility. Industrial production and the consumer price index each have the largest direct impact. In contrast to the work done on developed markets, relatively little research has focused on the relationship between energy prices and emerging stock markets. Recent work in this area includes Papapetrou (2001) and Hammoudeh and Eleisa (2004).

Papapetrou (2001) uses a multivariate vector auto regression model to study the dynamic interaction between real stock prices, real economic actions, oil prices and interest rates, in Greece. His empirical results show that changes in oil prices influence real activity and employment.

Hammoudeh and Eleisa (2004) study the relationship between oil prices and stock returns for five members (Bahrain, Kuwait, Oman, Saudi Arabia, and the U.A.E) of the GCC. Using daily data they find that only the Saudi Arabia stock market has a bi-directional relationship between oil prices and stock returns.

In recent years many countries in the Asia-Pacific region have significantly increased their consumption and imports of crude oil, and they now constitute the fastest growing region of oil demand. The International Energy Agency forecasted Asia's total oil demand to grow by 3.2 percent, after growing by 5.3 percent in 2004. This growth is significantly higher than the world growth of oil demand which is usually less than 2 percent and was about 2.4 percent in 2004. Some of these countries were also the best stock market performers in the 1990s. However, during this period they experienced grave financial crises when some of their currencies plummeted by more than 50 percent.

The Asian-Pacific countries covered in this study have various distinctive characteristics. There are those who are developed such as Australia, New Zealand and Japan, developing such as India, Indonesia, Pakistan, and Sri Lanka, and in between such as Korea, Singapore and Taiwan. Some of these countries are net oil importers, while others such as Malaysia is net oil exporter, and Indonesia is an oil producer and is self sufficient. Last, but not least, some of these countries' financial markets was hurt more than others during the 1997 Asian crisis. Therefore, it will be interesting to study the relationship between Asian-Pacific countries' stock market performance and changes in their own domestic risk and in global factors such as the oil price and foreign exchange rates.

Several studies examined the effects of global, country and industry factors on the movements and volatilities of stock returns but not the domestic and global sensitivities under consideration in this paper. Beckers et al. (1995) find that global factors and national factors are of roughly equal importance in explaining the comovements of stock returns, while national factors are dominant in explaining the stock return volatility. Moreover, Grinold et al. (1989), Drummen and Zimmermann (1992), and Heston and Rouqwenhorst (1994) all find that national factors dominate stock return volatilities although industry factors play a significant role. Jones and Kaul (1996) study the impact of global oil shocks on the equity prices in Canada, Japan, UK and the US. completely account for this reaction. Huang et al. (1996) examine the relationship between daily returns of oil futures and US stock returns, using an unrestricted VAR model. In an industry-specific study, Faff and Brailsford (1999) repot signifincat positive oil price sensitivity of Australian oil and gas, and diversified resources industries. On the other hand, other industries such as paper and packaging, banks and transport seems to exhibit negative sensitivity to oil price hikes. Faf and Brailsford (2000) test the role of an oil price factor in explaining the systematic impact on prices in equity markets. Sadorsky (1999) examines the links between the fuel oil prices and stock prices based on US monthly data from January 1947 to April 1996. Using an unrestricted VAR model, that also includes short-term interest rate and industrial production, Sadorsky highlights the importance of oil price in explaining the movements of the other variables. Ciner (2001) finds a nonlinear linkage between energy shocks and financial markets. More recently, Aminduh and Wohl (2004) examine the relationship between stock prices and political news related to Saddam Hussein's oil contracts. In other geographical areas, Hammoudeh and Choi (2006) examined the long-run relationship among the Gulf's Arab (GCC) stock markets in the presence of the US oil market, the S&P 500 index and the US Treasury bill rate. They found that the T bill rate has direct impact on these markets, while oil and S&P 500 have indirect effects. This finding suggests that local and regional factors are dominant in these markets. Choi and Hammoudeh (2007) compared the conditional volatility between the Gulf Arab (GCC) stock markets and volatility of Mexico and the oil market within a Markov-switching framework. They found that the crisis-ridden Mexico has the highest volatility for the two regimes, followed by the oil market. GCC markets which are isolated by too many government regulations. In this paper we examine the relationship between stock market performances, on oil price changes.

Pakistan is an emerging stock market, which in spite of its smaller size has potential importance for global investors. However, Pakistan heavy depends on oil imports for running its economic machinery as a result oil price shocks may have destabilizing effects on domestic financial markets. It implies that changes in oil prices will have an impact on the volatility of stock prices. The stock market in Pakistan initially reacted positively to the changes in oil prices in

1991-1993, but political instability and an uncertain investment environment have hindered Pakistan's attempts to develop its stock market (Arif and Khalid 2002). Regulatory policies may be needed to reduce the potentially negative effects on the domestic economy (Gelos and Sahay, 2001). Pakistan also introduced the regulatory polices in 2003 to control the negative impact on Pakistan economy. Pakistan's government changes oil prices for every fifteen days depending upon the changes of prices at world level. Oil prices have increased continuously since 2003 hitting a peak of $s137/bbl in July 2008, but after that a declining style has set in. Since 1970, it was the fifth major negative oil shock. The first one occurred in 1973-74 as a result of the OPEC oil embargo; and second one in 1978-79 when the OPEC put limit on its production. This growing flow in oil prices continued until mid 1980s, whereas Iran Iraq war in early 1980s had its contribution in swelling it further. However in 1986, when Saudi Arabia increased its crude oil production, oil price decreased. In 1990, Iraqi incursion of Kuwait lead to a further price shock but it stabilized in a year, as a result of Asian financial crisis. In 1999-2000 the OPEC again restricted its production leading to an added price shock. Final oil price shock took off in 2003 which sustained till July 2008. In other words, oil prices have consistently remained quite volatile. All these shocks have raised serious concerns surrounding the policy makers around the globe. The unpleasant economic impact of higher oil prices on oil-importing developing countries is commonly considered more stern compared to the developed countries as they are more reliant on imported oil and are more energy-intensive (incompetent in use of energy)1 (IAE 2004). In particular, the recent gush in the oil prices (2000s) has troubled economists concerning its potential undesirable impacts; as this rising trend in the price of oil has damaged Pakistan's economy as well as the different economics pf different countries, in provisions of creating inflationary pressure in the economy, mounting budget deficit and balance of payment problems (Malik 2007). Pakistan with a population exceeding 150 million has been on the lane of rising GDP growth in the last few years, but since the last fiscal year the circumstances are not very sound. The unremitting rise in oil prices in the preceding few years is regarded as one of the causative factor. Energy sector has a straight link with the economic growth of a country. In line with the intensifying growth rate of GDP, requirement for energy has also grown swiftly. The extent by which economies are spoiled as a result of price shock depends on the distribution of cost of oil in national income, the level of dependence on imported oil and the capability of end-users to diminish their use and switch away from oil. In the energy mix for the year 2005-06, oil records for 32 percent of the total energy consumed in Pakistan. Although the focus with which oil is worn in total energy consumption has decreased in the last couple of years but still is the second largest supply of energy used after natural gas, which holds for 39 percent. The energy force is concerned it has remained almost constant since 1990-91 (i.e., 1 %). Shrink in energy intensity is known as the most promising means for reducing susceptibility to oil shocks (Bacon 2005). Oil being the second major source of energy used along with almost an unvarying rate of its production Pakistan is profoundly dependent on oil imports from Middle East exporters (Saudi Arab playing the lead role). Almost 82% of the demand for petroleum products in the country is met through imports. Pakistan spent about 44 percent of export income on oil imports in 2006-07. This percentage was only 27 percent in 2004-05. As a result, the international oil price fluctuations contain a direct manner on the macro economy of the country, particularly on the oil price GDP correlation. The share of net oil imports in GDP is an index of the relation significance of the oil price increase to the economy in terms of the potential adjustments needed to offset it. For Pakistan over the last few years, this ratio has increased from -3.13 in 1990-91 to -5.24 in 2005-06 (Malik 2007). With such an elevated ratio, unless country is consecutively in surplus, or has tremendously large foreign exchange reserves, high oil price is dealt by severe macro economic adjustments.

The impact of oil price on dividends in U.S., Canada, Japan and U.K. stock markets was studied by Jones and Kaul (1996). They used a cash-flow valuation model which reflected that all stock markets were inversely proportional to oil shocks. Later, Sadorsky (1999) also studied the impact of oil price shocks on stock returns. They used vector auto regressions in U.S. industrial production and short interest rates and reported the evidence of oil shock on cumulative stock returns. However, Huang, Masulis and Stoll (1996) provide the evidence opposite to the results of Sadorsky (1999) for the same economy. Maghyereh & Aktham (2004) examined the dynamic linkages between oil price shocks and the stock market returns in twenty two rising economies, including Pakistan. They used VAR model on daily data for 1998 to 2004 and found weak evidence about a relationship between oil price shocks and the stock market returns in these emerging economies. Likewise, results from impulse analysis revealed the innovations in the oil market, which are slowly transmitted in the emerging stock markets. They recommended that the inefficient transmission of new information of oil market do not rationally signal changes in oil prices. Basher and Sadorsky (2004), used a multi-factor arbitrage pricing model and found proof that oil price risk has many impacts returns of emerging stock markets. Driesprong, Jacobsen and Maat (2004) reported the evidence that investors in stock markets under react. Agren (2006) studied the volatility spillover from oil prices to stock markets. He applied asymmetric BEKK model using weekly on the aggregate stock markets of Japan, Norway, Sweden, the UK and the US. He found the strong evidence of volatility spillover for all stock market except Sweden, which is found week evidence.

The future prospects are also not very encouraging. All trading is being made on +90 $. OPEC promised to raise it's out put but with out any significant effect. For the time being the prices were dipped but were raised again on much higher values.

Rising tension between US and Iran is one reason. Some sources are predicting the attack on Iran is imminent. Emerging economies and developing will suffer most. Their economy is dependent on energy resources. How can they survive and how can they meet their production commitments.

In recent months oil surged from 70$ to 92$. It will lead to rise in inflation, shutting down of inefficient industries and rising unemployment in third world countries.

When it comes to individuals, the poor will suffer compared to the high income group which will survive. But the strains coming on poor in third world countries will transform to social unrest and hence will cause instability in the region.

Already many textile mills close due to high manufacturing costs. Increase in oil prices will definitely bring more strain on existing working units. In election environments it will be definitely a difficult decision for Prime Minister Shaukat Aziz to authorize the fuel prices in Pakistan.

But he has no other option. How far the Government continues to absorb the fuel bills eventually it has to increase the prices. And again who will suffer simply the poor.

Today only there is news that Pakistan is going ahead 2000 MW power plant based on furnace oil. Now we have to look into our future of sustainable economic activity.

Another area which is to be emphasized is the conservation of energy. By persuading all concerned and by taking all possible measures to save energy, it is the possible to have a developing the future.

According to Malik (2008), a country's vulnerability to oil shocks can be seen through a number of indicators. Firstly, the oil self sufficiency index, which is calculated as the difference between oil production and oil consumption divided by oil consumption. This ratio is negative for oil importers (with -1 being the extreme value). Pakistan had a value of -0.79 in 2005-2006, indicating its high susceptibility to oil shocks. Secondly, Vulnerability to rising oil prices also depends on the intensity with which oil is used. The intensity of oil use in energy consumption index measures the share of oil in an economy's primary energy consumption. Pakistan had a value of0.32 in 2005-2006, showing slight decrease from the past due to shift towards alternatives. Thirdly, Energy Intensity measures the energy intensity for an entire economy (measured as percentage change in energy consumption divided by percentage change in GDP). A decrease in energy intensity is considered as the most promising route for reducing vulnerability to oil shocks (Bacon and Kojima 2006). For Pakistan, this has remained more or less constant at about 0.9 in 2005-2006, showing that there has not been much improvement in this area. Finally, the net oil imports in GDP represent the magnitude of the direct effect of a price increase. Pakistan had a value of -5.24 in 2005-2006. Hamilton (2005) argues that a potential macroeconomic effect of oil price is on the inflation rate as long run inflation rate is governed by monetary policy, and so ultimately it depends on how the central bank responds to oil prices.

A large body of the empirical research has confirmed that oil prices have negative and strong effect consequences on the world economy (see seminal work of Hamilton 1983 and for more recent review see Hamilton in 2003). A long line of empirical work finds that oil price increases negatively impact measures of macroeconomic activity. It has been estimated that a $5US price increase a barrel reduces global economic growth by 0.3% in the following year5. In sharp contrast to the volume of studies investigating the link between oil price shocks and macroeconomic variables, there have been relatively few analyses on the correlation between financial markets (Stock market) and oil price shocks. In this context, Huang et al., (1996) opine that if oil plays an important role in an economy, one would expect changes in oil price to be correlated with changes in stock returns. Driesprong et al. (2007) study whether changes in oil prices predict stock returns. They used stock market data from forty-eight countries, a world market index and price series of several types of oil6. They found that oil price changes sensitivity is expected to vary across countries. Jones and Kaul (1996) argue that the impact of oil price changes to a country's economy of which reflected on stock returns are likely to vary across countries depending on their oil production and consumption level. Theoretically, in oil exporting countries7, stock market prices are expected to be affected positively to oil price changes through positive income and wealth effects. Bjrnland (2008) argued that higher oil prices represent an immediate transfer of wealth from oil importers to oil exporters. She stated that the medium to long term effect depend on what the government in the oil producers do with the additional income. If this income is used to purchase goods and services in their country, higher oil prices will generate a higher level of activity and though improve stock returns. In oil importing countries, oil prices are expected to have significant negative effect on the stock market. One of the key researches is done by Sadorsky (1999). He investigates the dynamic interaction between oil price and other economic variables including stock returns using US data. He finds that oil price instability and oil price changes have a significant negative impact on real stock returns. He also finds that industrial production and interest rates responded positively to real stock return shocks. Jones and Kaul (1996) examine whether stock prices reveal the impact of news on existing and prospect actual cash flows. They find that oil price increases in the post war period have a significant detrimental effect for the US, Canadian, Japanese and UK stock market. Park and Ratti (2008) examine the relationship between oil price shocks and stock markets in the US and 13 European countries using monthly data during the period 1986-2005. This study finds that oil prices play a crucial role in the stock market of oil importing countries. Additionally, Park brings evidence that stock markets in oil exporting countries are less affected by oil prices relative to oil importing countries. Recent papers of the oil prices effect on stock markets distinguished between developed and emerging market response to the changes in oil prices. Sadorsky (2006) argue that developed economies are more energy efficient with oil consumption as a result of their ability to reduce the energy intensity through technological innovation and these countries do rely more on a diversified range of energy sources. In this context, Henriques and Sadorsky (2006) measure how responsive the financial act of unusual energy companies are to alteration in oil prices. Two variables VAR model has been build up and expected in order to examine the relationship between substitutes Cement stock prices and oil prices. It shows that oil prices individually hinder the stock returns of Cement companies. Basher and Sadorsky (2006) stated that emerging economies are less able to reduce oil consumption and thus these countries are more energy intense and more exposed to oil prices than more developed economies. Therefore, oil price changes are likely to have a greater impact on profits and stock returns in emerging economies. Yet, the effect of oil prices shocks on stock returns in developed and emerging countries is mixed. In this context, Maghyereh (2004) examines the correlation between oil price and stock market returns for 22 emerging economies for the period from 1998 to 2004. He shows very weak evidence that oil price shocks affect stock market returns in emerging economies. He concludes that the higher the country energy intensity consumption, the higher the response to oil prices. He explains these results based on the efficient market hypothesis. Stock markets in the emerging economies are inefficient in the transmission of new information of the oil market, and stock market returns in those countries do not rationally signal changes in crude oil price. Nandha and Hammoudeh (2007) observe the correlation between realized stock index return and beta risks in the occurrence of oil and exchange price sensitivities for fifteen countries in the Asia Pacific district using the international factor model and weekly data during the period 1994-2004. In spite of the attention paid to examine the oil price effect on oil exporting countries, no such attention were paid to test the influence of oil prices increase on oil importing emerging countries. In our view, this issue is even more important to examine. Increasing the prices over a short period may created a serious hardship for many non oil exporting countries by raising their costs of imported oil. Therefore, in this study we contribute to the previous literature by focusing on oil importing emerging country examining the relationship between oil prices and stock market returns.

There is a lot of work has been done so far in this regard. Now the paper has overview some of economist works in this section of the paper as review literature. The hypothesis the change in macro economics variable have got strong impact on assets prices as subject extensive research

Robert D. gay (2008) used MA method with OLS to find relationship between stock prices and macro economics variables effects on four emerging economies India, Russia, Brazil and China. He used oil price, exchange rate, and moving average lags values as explanatory variables but result are insignificants which shows inefficiency in market final conclusion is that these economies are emerging so domestics factors more influence outside factors oil price and exchange rate Dr. Aftab(2000). He try to link between monetary and fiscal policy of Pakistan equities market and the result of his analysis is significant. He found that fiscal and monetary policy change market capitalization through equity (changes floated shares) and liquidity which can significantly causal relation shows with market capitalization/ stock price. In case of Pakistan data set is used 1993 to 1998 Liaquat Ali and Nadeem Ahmed(2008) they used data 1971 to 2006 and try to make relation ship of economic growth with stock market prices they found the dynamics relation ship between stock prices and economic growth. They employed DF-GLS test first time in case of Pakistan. M.Shahbaz (2006) he tries make relationship between stock prices and rate of inflation he used ARDL model which used dynamics analysis. His findings are stock hedges against inflation in long run but not in short run and discuss black economy which effect long run and short run prices of the stock he used variables CPI, as proxy of inflation and share of black economy the sample size he took 1971 to 2006. Safail Sharma (2007) he used rate of interest, exchange rate, industrial production index, money supply and inflation as explanatory variables he used AR and MA as also explanatory variable to remove effects of non stationary in the data. His finding are lags values are highly correlated with current prices suggest speculation in market. Exchange rate, industrial production index and money supply are significantly related he took data set 1986 to 2004.

Song-zan-chiou-wei(2000) he used money supply oil price and exchange rate as explanatorily variables for Asian stock market he used VAR model applied to observed the differences of the structure of fluctuation after 1997 financial crises. His finding oil prices and inflation are highly effect the stock market of Asian economy.

The stock market plays an important role in the economy in the sense that it mobilizes domestic resources and channels them to productive investments. However, to perform this role it must have significant relationship with the economy. In this context, the causal analysis between the stock market variables, e.g., stock prices, market capitalization, etc., and the variables, for instance, representing the real sector of the economy like real gross domestic product, real consumption expenditures, and real investment spending, would provide useful insights regarding the role of stock market in an economy. In other words, we can examine whether changes in stock market variables cause fluctuations in the real sector implying that stock market leads economic activity or are caused by the real variables indicating that it lags economic activity.

The issue whether stock market leads or lags economic activity is now becoming very crucial in Pakistan as the stock market has gained much attraction in the last few years.

The market has been, in general, among the best performing markets. The indicators like market capitalization, trading volume, the market index has shown phenomenal growth.

These developments are often claimed by the authorities to be an indication of economic progress of the country. It would be useful to examine whether these developments has influenced the economy, particularly the real sector. Moreover, the relationship between stock prices and the real sector variables is also important in view of the various economic reforms started in early 1990s. The measures taken for economic liberalization, privatization, relaxation of foreign exchange controls, and in particular the opening of the stock markets to international investors are supposed to have great impacts on the economy including the real sector.

The theoretical basis to examine the link between stock returns and the real variables are well established in economic literature, e.g., in Baumol (1965), Bosworth (1975). The relationship between stock returns and real consumption expenditures, for instance, is based on the life cycle theory, developed by Ando and Modigliani (1963), which states that individuals base their consumption decision on their expected life time wealth. Part of their wealth may be held in the form of stocks linking stock price changes to changes in consumption expenditure. Similarly, the relationship between stock returns and investment spending is based on the q theory of James Tobin (1969), where q is the ratio of total market value of firms to the replacement cost of their existing capital stock at current prices. Finally, the relationship between stock returns and GDP, a measure of economic activity, indicates whether the stock market leads or lags economic activity.

The empirical evidence, particularly in the South Asian region, regarding the direction of causality between stock returns and the real variables is not conclusive. For example, a unidirectional causality from stock prices to consumption expenditures is observed by

Nishat and Saghir (1991) in Pakistan and Ahmed (1999) in Bangladesh whereas Mookerjee

(1988) observes the opposite case in India. Similarly, Mookerjee (1988) and Ahmed (1999) report a unidirectional causality from stock returns to investment spending for India and Bangladesh respectively whereas the opposite case is reported by Nishat and Saghir (1991) for Pakistan. Regarding causal relation between stock returns and economic activity Mookerjee (1988) finds evidence that GDP leads stock prices in India whereas Nishat and Saghir (1991) find the opposite evidence in Pakistan. On the other hand, Ahmed (1999) finds the evidence that Index of Industrial Production (IIP) leads stock returns in Bangladesh.

In another study for Pakistan, Husain and Mahmood (2001), covering the data from 1959/60 to 1998/99 report a uni-directional causality from the macro economic variables, GDP, consumption, investments, to stock prices implying that the stock market lags economic activity and thus cannot be characterized as the leading indicator of the economy in Pakistan.

The objective of this paper is to extend the analysis by Husain and Mahmood (2001) by including the recent data as well as by taking care of the expected shift in the data due to the start of the economic liberalization program in the early 1990s. The program resulted in significant improvements in the size and depth of the Karachi stock market. The remaining part of the paper is organized as follows. Section II discusses the data and explains the methodology for testing the stationary, the existence of co integration, and the direction of causality. Section III reports the results regarding the causal relationship between oil prices and stock returns.

The search for the routes by which oil price shocks work their way through the economy has had some important additions in recent years. Two of these are primarily theoretical analyses, connected with data by simulations relying on aggregate models of the economy. The other two are empirical and highly disaggregated. One study from each category appeared shortly after the 1996 DOE Conference on Oil Security, and the other two have appeared in the past year but have been available in preliminary versions for some five years or longer.

The two empirical studies shed light on the sectoral shocks transmission mechanism, formulated theoretically by Lilien (1982) and Hamilton (1988) and explored empirically by Loungani (1986). The Davis and Haltiwanger (2001; D&H) study is a revision of the study they prepared for the 1996 DOE Conference on Oil Security. Its empirical base is quarterly, plant-level Census data from 1972:2 to 1988:4 on employment, capital per employee, energy use, age and size of plant, and product durability, at the four-digit SIC level. They used vector auto regressions (VARs) to examine the response of job creation and destruction to separately defined, positive and negative oil price shocks. D&H's examination of job creation and destruction separately lets them distinguish between aggregate and allocative transmission mechanisms. The aggregate channels are potential output, income transfer, and sticky wage effects emphasized by traditional macroeconomic analyses. For example, an oil price increase shrinks potential output since the price increase is equivalent to a reduction in resources available. Income transfers operate through relative price changes, and sticky wages refer to the effects of labor contracts on the ability of the labor market to adjust employment and earnings to demand or price changes. Allocative channels involve the effect that oil price changes have on the closeness of match between firms' desired and actual levels of labor and capital. For example, an oil price change, in either direction, can alter the mix of labor skills that a firm possesses, given its capital stock. The aggregate effects of an oil price shock reduce job creation and increase job destruction, while the allocative aspects increase both creation and destruction. Their test for distinguishing the operation of these channels empirically relies on the patterns of response of job creation and destruction to oil price changes. The aggregate channels would increase job destruction and reduce creation in response to an oil price increase, while the allocative channels would increase both creation and destruction. Furthermore, the aggregate channels should operate symmetrically; while the allocative channels operate asymmetrically because both oil price increases and decreases would alter firms' desired employment structures. Thus if oil price shocks operate predominantly through aggregate channels, employment would respond roughly symmetrically to positive and negative oil price shocks.

This study empirically determines the relationship between stock returns and oil prices to take the volatility clustering into account. This paper uses the Regression model which is developed by Bollerslev (1986) with some variations. A Regression model is a specification that works well for stock returns in Pakistan with capturing the Inverse effect. This ensures that the inferences based on statistical tests are valid throughout, the modeling process. In methodology of Hendry (1985), we have amended in the model to ensure the suitability of the model according to the nature of Pakistani stock market. Stock returns is the dependent variable, oil prices are used as independent variable which is the important variable in this study. One important addition in

Hendry (1995) model is KSE turnover or daily trading volume. It is used as proxy variable for GDP, because the daily data on GDP is not available. This variable shows the economic conditions of the country as well as local influences of the market. We incorporate day of the week effect in the model. This is because the Karachi stock market starts on Monday with a positive sentiment where as the sentiment is mostly negative on Friday due short span of time due to Friday Prayers. Moreover, Pakistan stock returns have seasonality's (Hussain: 1999 and Mustafa and Nishat: 2003),during some months there are high returns and during others, not so significant. We have also considered the basis for efficient market hypothesis in which any new information relevant to the market is spontaneously reflected in the stock returns.

The data on stock price and trading volume are collected from daily "Business Recorder".

The return is calculated from difference between logs of two successive KSE-100 indexes.

The data on information is collected on daily basis from the headlines of front-page news of daily "Business Recorder". The Business Recorder is business and economic oriented newspaper. The data on oil prices are taken form various issues of economic survey. The length of data period is July 01, 2003 to June.30, 2009. This sample period is interesting in that diverse kinds of information were generated during this period. Three major events that took place during this period had implications for the stock market. First, the financial crisis that hit in 2008. It created deep effect on the financial sector. Second, the controversy between IPP's (Independent Power Producers) and Government of Pakistan regarding the HUBCO project peaked during this time. The contribution of HUBCO in the total trading volume of KSE is large so is its importance in KSE-100 index. Therefore any factor that affects HUBCO can significantly affect the aggregate activity in stock market.

Consequently, any news regarding HUBCO affects the activity of stock exchange. Third,

Two political governments have been dissolved which creates uncertainty in the country.

Fourth, Military regime came into Power. This resulted in uncertainty in domestic business environment accompanied by further economic sanctions by foreign governments. Furthermore, efforts to increase the tax base of the country by the government but which were opposed by the business also affected the stock market. Fifth, 9/11 events which creates uncertainty in the country.

D&H find that both oil-price and monetary shocks cause larger responses in job destruction than job creation in nearly every industrial sector. The magnitude of effect of oil price shocks is about twice that of monetary shocks, and the response of employment to oil price shocks is sharply asymmetric, the response to positive shocks being ten times larger than that to negative shocks. The allocated consequences of oil price shocks is substantial: the 1973:3-1973:4 episode caused job reallocation equal to 11 percent of total manufacturing employment over the following 15 quarters. However, the sectoral-shifts hypothesis that positive and negative oil price shocks would cause the same extent of allocated response is not borne out in the results. Keane and Prasad (1996; K&P) used the individual data of the National Longitudinal Survey of Young Men, a nationally representative sample of 5,225 young males between 14 and 24 years in 1966, interviewed in 12 of the 16 years from 1966 to 1981. They screened the sample to include only those who, as of interview date, were at least 21 years old, had completed schooling and military service, and had available data for all variables used in the analysis. Their final sample size was 4,439 males and a total of 23,927 person-year observations. Their oil price variable is the real price of refined petroleum products, calculated as the producer price index for refined products divided by the overall PPI, averaged over the 12 months prior to the interview date. K&P found that oil price increases depress real wages for all workers but raise the relative wage of skilled workers. Over this period, real wages fall between 3% and 4% in the long run following an increase in the real price of refined petroleum products of 1-standard deviation around trend (c. 19%). The short-run effect of an oil price increase on aggregate employment is negative, but the long-run effect is positive, possibly because of complementarities and substitutability's among major categories of factors. Oil price increases also induce changes in employment shares and relative wages across 3-digit industries. K&P find that oil price changes do not appear to cause labor to flow consistently into sectors with relative wage increases; while K&P find this counterintuitive, reverse causation could be operating: large flows of labor going to particular sectors could depress their wages. Why would not wages equalize across sectors? Skill differentials, for one thing. Oil price changes could destroy part of people's less tangible skills, leaving them to find employment in industries requiring minimal skills, such as retail trade and services. There also is evidence that oil price changes affect people with different experience levels and "tenure" lengthsnumber of years in the current jobdifferently. Employment probabilities for skilled workers rise following oil price increases, suggesting that skilled labor may be a good substitute for energy in the production functions of most industries. Workers with longer experience in the labor force tend to experience greater reductions in real wages following oil price increases; this may be an age effect rather than a human-capital effect. They suggest that the rising wage premium for skills in the U.S. economy during the 1970s may in part be related to the sustained increase in the real price of oil over that period. There is much in the details of K&P's findings that is consistent with the sectoral shifts view of Lilien (1982) and Hamilton (1988): considerable reallocation of labor across industries, with differential consequences for skill levels and experience. Turning to the theoretical studies, Rotemberg and Woodford (1996; R&W) and Finn (2000) are simulation models of an aggregate economy intended to find mechanisms that will allow oil price shocks to have the magnitude of effect on output that is found empirically. R&W note that, empirically, a 10% innovation in the price of oil reduces output by 2.5% 5 or 6 quarters later; but that their 1-sector model, with perfect competition, can yield only a % output reduction for that price increase. They notice that, empirically, real wages also fall by more than their model predicts, and infer that using a sticky-wage labor supply specification would not be the right mechanism by which to get their model to reduce output by the appropriate order of magnitude. Consequently they revise the perfect competition assumption. R&W's model is simple in outline if intricate in its implementation. Many firms with identical production functions produce a gross output, Y, and many identical households consume an aggregate consumption good, C, and undertake investment. Money is not present, and there is no unemployment. The output market clearing condition is that the sum of all households' consumption and investment, plus government purchases, equal aggregate Y minus materials. The Lagrange multiplier from the first-order conditions on the production function(s) is an endogenous mark-up variable. Collusive capacity throughout the entire economy permits producers to raise mark-ups beyond what perfect competition would permit. An oil price shock lets them increase mark-ups; depressing output following an oil shock in a magnitude and temporal pattern that somewhat parallels the empirical path of output response to an oil price shock in an impulse response function. The replication of the empirical pattern that this model generates is not particularly close in its details, but obtaining a closer match might require the specification of free parameters describing adjustment timings for which empirical evidence is scant. However, rely on collusion for the entire ten-fold difference in output response to an oil price shock between the purely competitive, aggregate model and the empirical estimate is not satisfying. As one source of contribution among several to the magnitude of GDP responses to oil price shocks, variable mark-up responses might be important in some sectors, but that topic is subject to empirical investigation. R&W observe that sectoral reallocation models such as Hamilton (1988) represent a different explanatory mechanism for the impact of oil price shocks but leave comparisons to other explanations for further research.

Finn's (2000) alternative specification of an aggregate model avoids tying the magnitude result to noncompetitive conditions. The outlines of Finn's model are similar, but she separates the quantity of capital installed in production from its utilization, which is a function of energy use. Consumers allocate part of their consumption to investment, which is used to purchase new capital equipment, which in turn requires energy for its use. The household's problem is to maximize lifetime utility by choosing current consumption and labor supply, next-period capital, and current capital utilization, the last of which implies both current investment and current energy consumption. An oil price shock causes sharp, simultaneous decreases in energy use and capital utilization. The decline in energy use works through the representative firm's production function directly, reducing output and labor's marginal product. The fall in labor's marginal product reduces the wage, which in turn reduces labor supplied. A permanent rise in the oil price causes lower of energy use, capital utilization and labor supply to be propagated into the future; working through the production function, these reductions depress capital's future marginal product, causing a fall in capital's future marginal return and reductions in investment and capital in the present but extending into the future. An indirect transmission channel, working through the capital stock, is related to capital's marginal energy cost, affecting returns on investment (ROI). This rise in capital's future marginal energy cost (since the price increase is permanent) prompts further reductions in ROI. The oil price increase's effects on output and wages are "potentially significant" even if energy's output share is low. These effects are potentially long-lived too, since they operate on the capital stock. Simulated responses of value added and real wages from Finn's model track the responses of those two variables in the U.S. data (1947-80) from R&W. The mechanism she uses to obtain output responses that are in line quantitatively with empirical estimates is reasonable and could contribute to the sectoral differences in input reallocations that are the core of the sectoral shifts transmission mechanisms.

The purpose in this paper is to shed light on the behaviour of the impact of oil shocks on the stock returns of listed Cement sector companies in Karachi. This study will examine the non-linear relationship between oil prices and stock returns. If there exists a non-linear relationship then what is the threshold level after which it becomes negative. Plan of the paper is introduction, followed by an overview of literature. Section III will describe the methodology and data and section IV will explain the empirical findings. Finally section V is the conclusion.

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