Browsing by Author "Ortega, Hector"

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    How good are analyst forecasts of oil prices?
    (2021) Cortazar, Gonzalo; Ortega, Hector; Valencia, Consuelo
    Even though there is a wide consensus that having good oil price forecasts is very valuable for many agents in the economy, results have not been fully satisfactory and there is an ongoing effort to improve their accuracy. Research has explored many different modeling approaches including time series, regressions, and artificial intelligence, among others. Also, many different sources of input data have been used like spot and futures prices, product spreads, and micro and macro variables.
  • No Thumbnail Available
    Item
    How Valuable Is Market-and Firm-Specific Information for Calculating Bond Spreads in an Emerging Market?
    (2022) Cortazar, Gonzalo; Ortega, Hector; Romero, Rodrigo
    The determinants of corporate bond credit spreads are investigated in Chile as an example of an emerging market with relatively few actors and thin trading. Both market-level and firm-level factors are considered. Three models previously used to analyze the highly developed US market are applied to Chilean inflation-indexed bond trade data, and the results for the two markets are compared. The determinants found to be significant for Chile form the basis for the design of a new multifactor regression model that is used to explain Chilean bond spreads. The results are evaluated with an out-of-sample test, and the root-mean-square error is calculated to compare the model's results with those obtained by the method commonly applied in illiquid markets by repeating the last recorded transaction for days on which no data are available. The proposed formulation is found to reduce the degree of error.
  • No Thumbnail Available
    Item
    Time-Varying Term Structure of Oil Risk Premia
    (2022) Cortazar, Gonzalo; Liedtke, Philip; Ortega, Hector; Schwartz, Eduardo S.
    We develop a framework to estimate time-varying commodity risk premia from multi-factor models using futures prices and analysts' forecasts of future prices. The model is calibrated for oil using a 3-factor stochastic commodity-pricing model with an affine risk premia specification. The WTI oil futures price data is from the New York Mercantile Exchange (NYMEX) and analysts' forecasts are from Bloomberg and the U.S Energy Information Administration. Weekly estimations for short, medium, and long-term risk premia between 2010 and 2017 are obtained. Results from the model calibration show that the term structure of oil risk premia moves stochastically through time, that short-term risk premia tend to be higher than long-term ones and that risk premia volatility is much higher for short maturities. An empirical analysis is performed to explore the macroeconomic and oil market variables that may explain the stochastic behavior of oil risk premia, showing that inventories, hedging pressure, term premium, default premium and the level of interest rates all play a significant role in explaining the risk premia.