| This dissertation is composed of three interrelated body chapters. Its goal is to identify underlying sources of return volatility movement and analyze important problems in the economics of commodity markets by applying various time series econometric models to commodity market price data.; Chapter 2 investigates stochastic properties of daily cash price changes for six commodities: corn, soybeans, live cattle, live hogs, unleaded gasoline, and gold. We use the FIGARCH conditional variance model and the semi-parametric local Whittle estimation method to explore the daily cash return volatility behavior. We apply the long memory models to the temporally aggregated daily cash returns and compare the volatility dynamics at various sample frequencies.; Chapter 3 is concerned with commodity futures return volatility at daily and higher sample frequencies. In particular, strong intra-day periodicity in the high frequency return volatility is observed. We examine the high frequency futures return volatility pattern after removing the intra-day seasonality using the Flexible Fourier Form (FFF) filter and compare the volatility movement with the daily futures return volatility process.; Chapter 4 introduces a newly suggested volatility measure, the realized volatility, and applies the volatility measure to commodity futures market price data. The realized volatility is calculated as the sum of high frequency squared returns and exhibits some ideal statistical properties. Taking advantage of the stochastic properties of the realized volatility measure allows us to study important economic determinants for commodity futures market risk behavior. |
