Identifying the spatial effects and driving factors of urban PM2.5 pollution in China

The effect of natural and anthropogenic factors on haze pollution in Chinese cities: A spatial econometrics approach

Global renewable energy development: Influencing factors, trend predictions and countermeasures

How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques

Environmental regulation, economic growth and air pollution: Panel threshold analysis for OECD countries.

Spatial linkage analysis of the impact of regional economic activities on PM2.5 pollution in China

Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Investor sentiments and extreme risk spillovers from oil to stock markets: evidence from Asian countries

Since 2017 Finland and China have been developing a future-oriented partnership based on mutual economic and business interests. The starting point for this work is the recognition of significant differences in size, culture, and political as well as economic systems between the two sides. The Sino-Finnish cooperation in energy transition can provide a good example of mutually beneficial partnership where countries complement each other in terms of know-how and resources. This paper brings together the main findings from the policy study on energy transition in Finland and China. It aims to identify potential policy initiatives for expanding Sino-Finnish cooperation towards green growth.

A Synthesis of Energy Transition Policies in Finland, China

Yan-Ran Ma

Papers

Spatial linkage analysis of the impact of regional economic activities on PM2.5 pollution in China

Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?

Oil financialization and volatility forecast: Evidence from multidimensional predictors

Investor sentiments and extreme risk spillovers from oil to stock markets: evidence from Asian countries

A Synthesis of Energy Transition Policies in Finland, China