During the last thirty years, many developments contributed to the process of international financial market integration. Removal of capital controls in the 1970s by the major developed countries provided the initial impetus for financial market integration by facilitating cross-border capital movement. Economic liberalization and financial deregulations of the 1980s opened up national markets making way for greater financial integration. As globalization and integration of international goods markets advance with lessening of tariffs and other constraints, there will be further impetus to the changes in the financial markets.
While the financial integration is generally thought to be advancing well among developed countries, evidences have emerged to suggest that markets of many developing countries in the Far East and Latin America are not lagging behind in getting themselves integrated with global financial markets. Massive inflows of capital into these countries following their economic liberalization and financial deregulation in the early 1990s played a key role in this respect and these inflows are not likely to diminish as these countries continue to deregulate and liberalize their financial markets.
The predictability of asset returns is one of the more controversial topics in financial economics. According to the Efficient Markets Hypothesis (EMH), all information in investors’ information set should be incorporated into asset prices, thus leaving asset returns unpredictable. However, researchers and practitioners have identified a number of variables that seem to predict returns, at least in-sample. Some examples of variables that have been shown to predict aggregate market returns are inflation (Fama and Schwert, 1977), the default spread and the term premium (Fama and French, 1989) and the dividend-price ratio (e.g. Campbell and Shiller, 1988; Fama and French, 1988). Other financial ratios, such as the book-to-market ratio (e.g. Kothari and Shanken, 1997) and the earnings ratio (e.g. Lamont, 1998) have also been shown to predict returns. Recently, other variables that are not price-based have also been found to predict the aggregate stock market, such as the output gap (Cooper and Priestley, 2008). Traditionally, these variables have been thought to capture time-varying risk, a concept which is not in conflict with a modern interpretation of informationally efficient markets. In addition there is a growing literature that abandons the idea of informational efficiency; this literature argues that behavioral biases induce stock return predictability. In one recent example Baker and Wurgler (2007) argue that investor sentiment predicts returns. Another recent example is Hong, Torous and Valkanov (2007) who find that certain industries lead the aggregate market; they argue that this is due to underreaction to information.
In this paper I examine how changes in the oil price affect stock markets. Oil is by far the most important commodity and as of September 2008 energy accounted for around 75% of the Goldman Sachs Commodity Index (S&P GSCI). Crude oil accounted for 40% of the entire index and Brent crude oil for another 15%, thus leaving oil products with a weight of around 55% of the entire commodity index. The individual commodities in the index are weighted by their respective world production quantities. Given its large weight, it is not surprising that changes in oil prices may be an important factor for fluctuations in the economy. It is also plausible that oil price changes are important for understanding changes in stock prices. However, no consensus seems to have been reached regarding the impact of oil price changes on stock returns. Two recent papers, Driesprong, Jacobsen and Maat (2008) and Pollet (2004), document that oil price changes predict stock returns. Driesprong et al. (2008) show that the predictability is strongest for developed markets and less pronounced for emerging markets.
In the United States there is a trend toward increasing physical inactivity, television viewing, use of electronic devices, and consumption of convenience foods which are high in fat and sugars. The culmination of these trends may be contributing factors to the increased prevalence of obesity throughout the United States. Body mass index (BMI) is often used as an indicator of obesity. BMI is calculated as weight in kilograms divided by height in meters squared. Adults are classified as overweight or obese when their BMI is > 25 and > 30, respectively. Children and adolescents are classified as overweight or at risk for being overweight when their BMI is greater than the 95 th or 85 th percentile, respectively, as determined by the Centers for Disease Control (CDC) BMI-for-age growth charts.
According to the 1999 – 2000 National Health and Nutrition Examination Survey (NHANES) data released in 2002, 64% of adults 20 years of age and older and 15% of children and adolescents 6 – 19 years of age are overweight. This is a 10% increase for children and adolescents and a 16% increase for adults classified as overweight since the NHANES II survey conducted between 1976 – 1980. 5 Of the 64% of adults who are overweight, 31% (about 59 million people) are classified as obese. There are genetic and environmental (behavioral) factors that contribute to body composition of an individual.
