Browsing by Author "Cortazar, Gonzalo"

Now showing 1 - 9 of 9
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    A multifactor stochastic volatility model of commodity prices
    (2017) Cortazar, Gonzalo; Lopez, Matias; Naranjo, Lorenzo
    We propose a novel representation of commodity spot prices in which the cost-of-carry and the spot price volatility are both driven by an arbitrary number of risk factors, nesting many existing specifications. The model exhibits unspanned stochastic volatility, provides simple closed-form expressions of commodity futures, and yields analytic formulas of European options on futures. We estimate the model using oil futures and options data, and find that the pricing of traded contracts is accurate for a wide,range of maturities and strike prices. The results suggest that at least three risk factors in the spot price volatility are needed to accurately fit the volatility surface of options on oil futures, highlighting the importance of using general multifactor models in pricing commodity contingent claims. (C) 2017 Elsevier B.V. All rights reserved.
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    Can oil prices help estimate commodity futures prices? The cases of copper and silver
    (ELSEVIER SCI LTD, 2010) Cortazar, Gonzalo; Eterovic, Francisco
    There is an extensive literature on modeling the stochastic process of commodity futures. It has been shown that models with several risk factors are able to adequately fit both the level and the volatility structure of observed transactions with reasonable low errors.
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    Credit Spreads in Illiquid Markets: Model and Implementation
    (ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2012) Cortazar, Gonzalo; Schwartz, Eduardo S.; Tapia, Claudio
    This paper presents a methodology for estimating a family of credit spread term structures in a market with few transactions. The authors propose partitioning the market into risk classes and modeling credit spread term structures for each risk class using a multifactor Vasicek model with some common and some risk class-specific factors. The approach uses information on the cross section and time series of corporate bonds in all the risk classes to estimate the term structure of credit spreads in each risk class. The model is jointly estimated using an extended Kalman filter and implemented using Chilean corporate and government bonds.
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    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.
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    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.
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    Term-structure estimation in markets with infrequent trading
    (WILEY-BLACKWELL, 2007) Cortazar, Gonzalo; Schwartz, Eduardo S.; Naranjo, Lorenzo F.
    There are two issues that are of central importance in term-structure analysis. One is the modelling and estimation of the current term structure of spot rates. The second is the modelling and estimation of the dynamics of the term structure. These two issues have been addressed independently in the literature. The methods that have been proposed assume a sufficiently complete price data set and are generally implemented separately. However, there are serious problems when these methods are applied to markets with sparse bond prices. We develop a method for jointly estimating the current term-structure and its dynamics for markets with infrequent trading. We propose solving both issues by using a dynamic term-structure model estimated from incomplete panel-data. To achieve this, we modify the standard Kalman filter approach to deal with the missing-observation problem. In this way, we can use historic price data in a dynamic model to estimate the current term structure. With this approach we are able to obtain an estimate of the current term structure even for days with an arbitrary low number of price observations. The proposed methodology can be applied to a broad class of continuous-time term-structure models with any number of stochastic factors. To show the implementation of the approach, we estimate a three-factor generalized-Vasicek model using Chilean government bond price data. The approach, however, may be used in any market with infrequent trading, a common characteristic of many emerging markets. Copyright (c) 2007 John Wiley & Sons, Ltd.
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    The valuation of multidimensional American real options using the LSM simulation method
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Cortazar, Gonzalo; Gravet, Miguel; Urzua, Jorge
    In this paper we show how a multidimensional American real option may be solved using the LSM simulation method originally proposed by Longstaff and Schwartz [2001, The Review of the Financial Studies 14(1): 113-147] for valuing a financial option and how this method can be used in a complex setting. We extend a well-known natural resource real option model, initially solved using finite difference methods, to include a more realistic three-factor stochastic process for commodity prices, more in line with current research. Numerical results show that the procedure may be successfully used for multidimensional models, expanding the applicability of the real options approach.
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    Thinly traded securities and risk management
    (2014) Bernales, Alejandro; Beuermann, Diether W.; Cortazar, Gonzalo
    Thinly traded securities exist in both emerging and well developed markets. However, plausible estimations of market risk measures for portfolios with infrequently traded securities have not been explored in the literature. We propose a methodology to calculate market risk measures based on the Kalman filter which can be used on incomplete datasets. We implement our approach in a fixed-income portfolio within a thin trading environment. However, a similar approach may be also applied to other markets with thinly traded securities. Our methodology provides reliable market risk measures in portfolios with infrequent trading.
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    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.