Examinando por Materia "Oil prices"

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    El sector energético como activo refugio : un análisis del XLE tras la invasión de Rusia a Ucrania
    (Universidad EAFIT, 2025) Vera Cortés, Alfonsina del Mar; Puerta Álvarez, Henry Daniel
    This study analyzes whether the U.S. energy sector, represented by the XLE ETF, can be considered a safe haven asset in the context of disruptive geopolitical and economic events, such as the Russia-Ukraine war and the COVID-19 pandemic. Using daily data from 2019 to 2025, the behavior of XLE, the S&P 500, gold (XAU/USD), oil prices (WTI and Brent), and inflation expectations (sourced from the FRED database) are examined. Through statistical and graphical tools, the analysis shows that XLE experienced a significant recovery after the onset of the conflict, remaining relatively stable compared to the volatility of other assets such as oil and gold. In contrast, gold—traditionally viewed as a safe haven—lost stability after the war began, with a delayed recovery. The differing behavior of WTI and Brent highlights the influence of regional geopolitical factors. Inflation expectations also played a key role in shaping asset dynamics. The results suggest that, although gold remains relevant as a safe haven, the U.S. energy sector—particularly the XLE—has gained prominence as a potential defensive alternative for investors during periods of high uncertainty.
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    Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach
    (Universidad EAFIT, 2017-12-05) Cortés, Lina M.; Mora-Valencia, Andrés; Perote, Javier; lcortesd@eafit.edu.co
    This paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by modeling the prices of options for West Texas Intermediate (WTI) crude oil that were traded in the period between January 2016 and January 2017. For these series we extract the implicit RND in the option prices by applying the traditional Black & Scholes (1973) model and the semi-nonparametric (SNP) model proposed by Backus, Foresi, Li, & Wu (1997). The results obtained show that when the average market price is compared to the average theoretical price, the lognormal specification tends to systematically undervalue the estimation. On the contrary, the SNP option pricing model, which explicitly adjust for negative skewness and excess kurtosis, results in markedly improved accuracy.