Co-movements of Oil, Gold, the U.S. Dollar, and Stocks

The financial markets are getting more integrated and co-movements in financial markets as well as co-movements of financial indexes have become of immense interest to financial analysts and portfolio managers. Several researchers (for example, Johnson and Soenen (2002), King et al (1994), Ewing et al (1997), and Forbes and Rigobon (2002), Engsted and Tanggaard (2007), among others) have analyzed different aspects of this interdependence among the financial markets and among financial indexes over the recent years. Most of the studies have used co-integration and error correction modeling techniques to isolate and identify the long-run equilibrium relationships among these variables.

Recently, Diebold and Yilmaz (2009) have used a simple but different technique to capture the so-called spillover effects or the interdependence among the economic/financial variables using the forecast error variance decomposition methodology. They have noted that spillover effects are time varying and the nature of the spillover indices depend on the measurement standards used. Understanding these relationships is important for the portfolio investors who want to diversify their investment opportunities and policy makers pursuing stabilization of the economy.

In this study, we will use the spillover indexes developed by Diebold and Yilmaz to analyze the interdependence of returns and volatilities of four major indexes: gold prices, the US dollar, price of oil, and the stock prices as measured by Dow Jones Industrial Average. We will examine to what extent these indexes are interrelated, and whether returns/volatilities of one index can predict movements in other indexes. We also want to examine the recently observed inverse relationship (reverse causality) between the US dollar and oil prices.  While previous studies show that after 1973 oil prices and the value of the US dollar have moved in the same direction (i.e., increase in oil prices have lead to the US dollar appreciation), the examination of  the recently observed inverse relationship is certainly of interest.

 In addition, we also want to explore the statistical properties of these variables using time varying parameter error correction model: In general, X_t=a_tX_t-1+e_1t   and Y_t=b_tYt_-1+e_2t  being a pair of I(1) series and Z_t= X+bY being the cointegrating I(0) error correcting term. In stead of standard error correction model, we build a Time Varying Parameter error correction model by containing a term a_tZ_t-1  in the equation, where a_t is constrained to lie inside the region [-1,1] most of the time.

We will use daily data for several years and employ the appropriate econometric techniques to identify and extricate new information regarding the relationship among these variables. Data were collected on the daily price of NYMEX crude oil futures (CL) and the US Dollar Index (DXY), price of gold and D-J index. The data were obtained from a Bloomberg terminal using daily closing prices from January 1989 through September 2009. The data set has more than 5200 observations. We believe that appropriate estimation and inferences from this statistical study will enhance our understanding about the co-movement of such important financial variables.