Browsing by Author "Han, Yu"

Now showing 1 - 4 of 4
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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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    Full-color fluorescent carbon quantum dots
    (AAAS, 2020) Wang, Liang; Li, Weitao; Yin, Luqiao; Liu, Yijian; Guo, Huazhang; Lai, Jiawei; Han, Yu; Li, Gao; Li, Ming; Zhang, Jianhua; Vajtai, Robert; Ajayan, Pulickel M.; Wu, Minghong
    Quantum dots have innate advantages as the key component of optoelectronic devices. For white light–emitting diodes (WLEDs), the modulation of the spectrum and color of the device often involves various quantum dots of different emission wavelengths. Here, we fabricate a series of carbon quantum dots (CQDs) through a scalable acid reagent engineering strategy. The growing electron-withdrawing groups on the surface of CQDs that originated from acid reagents boost their photoluminescence wavelength red shift and raise their particle sizes, elucidating the quantum size effect. These CQDs emit bright and remarkably stable full-color fluorescence ranging from blue to red light and even white light. Full-color emissive polymer films and all types of high–color rendering index WLEDs are synthesized by mixing multiple kinds of CQDs in appropriate ratios. The universal electron-donating/withdrawing group engineering approach for synthesizing tunable emissive CQDs will facilitate the progress of carbon-based luminescent materials for manufacturing forward-looking films and devices.
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    Impact of News on Crude Oil Futures
    (2017-04-21) Han, Yu; Ensor, Katherine; Ostdiek, Barbara; Turnbull, Stuart
    Crude oil futures are worlds the most actively traded commodity futures, with more than 3 billion barrels per year in open interest. In this thesis we use related news to model the price dynamics of oil futures. We examine the empirical patterns of oil market news data processed by Thompson Reuters News Analytics, plus the intraday trading data of the WTI futures price traded on NYMEX. Then we build a three factor stochastic model for futures prices on the whole curve, using interest rate, convenience yield and spot price. The Kalman filter was used to obtain quasi-maximum likelihood estimators. We found that news can significantly explain the price movements and volatility clustering, as well as its skewness and kurtosis. We also found that negative news has an higher explanatory power of price dynamics than positive news, indicating an asymmetrical behavior of information with different tones.
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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.