International price volatility transmission and structural change: a market connectivity analysis in the beef sector

Tanaka, Tetsuji; Guo, Jin

doi:10.1057/s41599-020-00657-x

Cited by 5 publications

(3 citation statements)

References 64 publications

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“…This is mainly attributable to the characteristics of the GARCH model. The model expression in Equation shows that the current volatility depends on the past information shock (ARCH term) and the past volatility (GARCH term) (Tanaka & Guo, 2020). In other words, the current volatility is a deterministic function of previous observations (Manera et al., 2016).…”

Section: Discussionmentioning

confidence: 99%

Stochastic Volatility Modeling of Daily Streamflow Time Series

Wang

Song

Zhang

et al. 2023

Water Resources Research

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The analysis of hydrological series is important for water resource management and allocation, as well as energy and food security in the face of climate change (Arriagada et al., 2019;Tencaliec et al., 2015). As one of the most important indicators in hydrological analysis, providing an important source of hydrological information for water resources engineering applications (Birsan, 2015;Shiau & Hsu, 2016). However, streamflow series have strong dynamic spatiotemporal characteristics, making accurate simulation and prediction difficult. This necessitates the use of highly complex tools to model the sophisticated features of streamflow series.In general, hydrological models are classified into two types: physical models and statistical models. Specifically, physical models are complicated because they take into account a large number of variables that influence hydrological processes. Statistical models simulate hydrological changes using observational data collected beforehand. Owing to their simple implementation, statistical models are easier to use in practice (Modarres & Ouarda, 2013b). Data-driven technologies, such as artificial neural networks, support vector machines, wavelet transforms, and time series analysis (TSA) models, have been receiving increasing attention in the field of hydrology (Hadi et al., 2020;Khan et al., 2020;Parisouj et al., 2020). In the application of the aforementioned data-driven models, the development of TSA models and new methods have consistently been the main theme in hydrological modeling (Fathian, Fard, et al., 2019;Fathian et al., 2018).In particular, linear time series models have been widely used to simulate hydroclimatic time series (Hu et al., 2020;H. Wang et al., 2021). Traditional time series models, such as the autoregressive integrated moving average (ARIMA) model, have primarily been developed for simulating the first-moment behavior of time series, but they ignore the second-moment information (volatility), leading to insufficient model fitting results (Fathian, Fard, et al., 2019). In reality, hydrological time series have strong volatility associated with environmental change.

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Section: Discussionmentioning

confidence: 99%

Stochastic Volatility Modeling of Daily Streamflow Time Series

Wang

Song

Zhang

et al. 2023

Water Resources Research

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“…By applying the Granger causality model, pioneered in econometrics and widely applied in other scientific fields, [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] we were able to decompose the reaction coordinate into a sequence of molecular motions. The four main components of the reaction coordinate, separated in time and happening as a domino effect, are depicted schematically in Figure 9: i) the calixarene cage tilts perpendicularly to the phenanthroline; ii) the Cu changes coordination to become planar; iii) the C-N bond distance shortens; iv) π-π stacking effects drive the movement of the product inside the cage and below the phenanthroline.…”

Section: All Structuresmentioning

confidence: 99%

“…Another aspect that has until now been neglected in chemistry is causality. While the concept is widespread across various scientific domains 41 -ranging from economics [42][43][44][45][46] and climate research [47][48][49][50][51] to biology 52,53 and medical studies [54][55][56] -it remains surprisingly absent in the field of chemistry. Although a handful of precedents in biomolecular simulations exists, [57][58][59] it has not been explored to study chemical reactions, not to mention transition-metal catalysis.…”

Section: Introductionmentioning

confidence: 99%

From Data to Chemistry: Revealing Causality and Reaction Coordinates through Interpretable Machine Learning in Supramolecular Transition Metal Catalysis

Talmazan,

Gamper,

Castillo

et al. 2024

Preprint

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Supramolecular transition metal catalysts with tailored reaction environments allow for the usage of abundant 3d metals as catalytic centres, leading to more sustainable chemical processes. However, such catalysts are large and flexible systems with intricate interactions, resulting in complex reaction coordinates. To capture their dynamic nature, we developed a broadly applicable, high-throughput workflow, leveraging quantum mechanics/molecular mechanics (QM/MM) molecular dynamics in explicit solvent, to investigate a Cu(I)-calix[8]arene catalysed C-N coupling reaction. The system complexity and high amount of data generated from sampling the reaction require automated analyses. To identify and quantify the reaction coordinate from noisy simulation trajectories, we applied interpretable machine learning techniques (Lasso, Random Forest, Logistic Regression) in a consensus model, alongside dimensionality reduction methods (PCA, LDA, tICA). Leveraging a Granger Causality model, we go beyond the traditional view of a reaction coordinate, by defining it as a sequence of molecular motions that led up to the reaction.

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Quantifying nonlinear effects of BRIC and G4 liquidity on oil prices

Zhou

Zhang

2022

Humanit Soc Sci Commun

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Research on oil prices has concentrated on demand and supply factors and has largely underestimated the importance of the financialization of commodities. To assess the impact of financial factors on oil prices, this article investigates the liquidity of Brazil, Russia, India, and China (BRIC) and that of a group of four developed economies (G4) consisting of the Eurozone, Japan, the United Kingdom, and the United States. An application of the single-state vector autoregressive (VAR) model to monthly data from the 1999–2020 sample period reveals that a positive shock to the liquidity of the BRIC countries leads to significant increases in real oil prices. These novel findings stem from a consideration of Markov-switching vector autoregressive (MSVAR) models, which shows that an unanticipated increase in the G4 liquidity is positively linked with real oil prices. The main findings are as follows. (1) We identify three regimes that are associated with the volatility of real oil prices and the liquidity measure, including a crisis regime that characterizes the subprime crisis and the COVID-19 pandemic. (2) Impulse response function analyses show that the impact of G4 liquidity under the crisis regime is almost twice as large as that during normal periods, while the impact of BRIC liquidity during such a crisis period is almost three times larger. (3) A shock to BRIC liquidity has a greater impact on real oil prices than a shock to the liquidity of the G4 economies. This analysis helps in assessing the importance of BRIC and G4 liquidity in relation to upsurges in the real oil prices.

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International price volatility transmission and structural change: a market connectivity analysis in the beef sector

Cited by 5 publications

References 64 publications

Stochastic Volatility Modeling of Daily Streamflow Time Series

Stochastic Volatility Modeling of Daily Streamflow Time Series

From Data to Chemistry: Revealing Causality and Reaction Coordinates through Interpretable Machine Learning in Supramolecular Transition Metal Catalysis

Quantifying nonlinear effects of BRIC and G4 liquidity on oil prices

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