Academic journal article Academy of Accounting and Financial Studies Journal

Estimating the Volatility Reducing Hedge Ratios Using OLS: Evidence from the Spot and Silver Futures Market

Academic journal article Academy of Accounting and Financial Studies Journal

Estimating the Volatility Reducing Hedge Ratios Using OLS: Evidence from the Spot and Silver Futures Market

Article excerpt

INTRODUCTION

Given the volatile nature of the market, market participants constantly try to manage the volatility to reduce their risk exposure. It is well documented that unhedged commodity price exposure may have an adverse impact on the firm's profitability (Carter, Rogers, and Simkins, 2004).

As a result, effectively hedging the volatility of commodity prices continues to draw considerable interest from academics, practitioners and economist. One such commodity that is of particular interest to some market participants is silver. Silver is a precious metal and the prevailing spot and futures prices of silver not only reflect the current supply and demand conditions but also reflect investors' future expectations of macroeconomics events such as inflation and other business and economic conditions. What sets silver apart from other precious metals such as gold is that silver has many uses and the demand for silver can change rapidly due to various reasons. Derived demand theory suggests that the changes in demand for particular products have implications for commodity prices which are often used as intermediate inputs into the final product (Harper, Jin, Sokunle and Wadhwa, 2013). For instance, silver can be transformed from its natural state and used in the technology and medical industries for such purposes as solar energy, water purification, and X-Ray devices. Moreover, silver is also used in the electronics, and automobile industries to produce components for computers and antifreeze materials. In addition; silver can also be used as an investment vehicle by investors who seek to diversify their investment portfolio. Silver's multiple industrial and investment uses have the potential of making its price more volatile than other commodities.

In response to the need to manage volatility, researchers have sought to develop models that estimate spot and futures hedge ratios that reduce risk. There is considerable debate in the literature as to the appropriate models to be used. Some researchers advocate the use of advanced econometric models, while others support the OLS model (Lien, 2005; Lien, Tse, and Tsui (2002).

The significance for studying silver prices in the spot and futures market is that financial managers often seek to minimize inventory holding costs and reduce the price volatility for storable products. For example, consider a firm that seeks to purchase silver for the use in its finished good production processes. The firm's financial managers might be concerned that an exogenous shock or external events that impact the business cycle might have an adverse effect on silver prices in the future. This adverse effect could have a significant impact on the profitability of the firm and a potential negative effect on shareholder wealth. In order to offset the risk, the difference between spot and future prices, the firm's financial management might engage in the futures market to reduce their risk exposure.

Hedging is comprised of both long and short positions. Long hedges are characterized by the purchase of a futures contract in anticipation of a future price increase, while short hedges are characterized by the selling of a futures contract in anticipation of a future price decline. The outcome of hedging is that the financial managers seek to minimize the variance associated with the price movement of silver.

This study contributes to the debate of estimating the risk reducing hedge ratios in the futures market by evaluating the basis movements in spot and futures prices for silver in markets that are believed to be efficient. In order to evaluate the relationship in price movements for silver purchased on the spot and in the futures market, we conducted a bivariate regression. But as a preliminary measure, we first evaluate both price series for stationary. We do this because it is widely known that if spot and future prices follow a random walk then the estimators with or without drift are correctly specified (Chen, Lee and Shrestha, 2003). …

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