Center for Computational Finance and Economic Systems (CoFES)

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https://hdl.handle.net/1911/109327

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Browsing Center for Computational Finance and Economic Systems (CoFES) by Author "Han, Yu"

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    Dynamic jump intensities and news arrival in oil futures markets
    (Springer Nature, 2020) Ensor, Katherine B.; Han, Yu; Ostdiek, Barbara; Turnbull, Stuart M.; Center for Computational Finance and Economic Systems
    We introduce a new class of discrete-time models that explicitly recognize the impact of news arrival. The distribution of returns is governed by three factors: dynamics volatility and two Poisson compound processes, one for negative news and one for positive news. We show in a model-free environment that the arrival of negative and positive news has an asymmetric effect on oil futures returns and volatility. Using the first 12 futures contracts, our empirical results confirm that the effects of negative and positive news are described by different processes, a significant proportion of volatility is explained by news arrival and the impact of negative news is larger than that of positive news.
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    Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
    (SSRN, 2019) Weylandt, Michael; Han, Yu; Ensor, Katherine B.
    Financial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly-traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.