Testing for intrinsic multifractality in the global grain spot market indices: A multifractal detrended fluctuation analysis

Grains account for more than 50% of the calories consumed by people worldwide, and military conflicts, pandemics, climate change, and soaring grain prices all have vital impacts on food security. However, the complex price behavior of the global grain spot markets has not been well understood. A recent study performed multifractal moving average analysis (MF-DMA) of the Grains & Oilseeds Index (GOI) and its sub-indices of wheat, maize, soyabeans, rice, and barley and found that only the maize and barley sub-indices exhibit an intrinsic multifractal nature with convincing evidence. Here, we utilize multifractal fluctuation analysis (MF-DFA) to investigate the same problem. Extensive statistical tests confirm the presence of intrinsic multifractality in the maize and barley sub-indices and the absence of intrinsic multifractality in the wheat and rice sub-indices. Different from the MF-DMA results, the MF-DFA results suggest that there is also intrinsic multifractality in the GOI and soyabeans sub-indices. Our comparative analysis does not provide conclusive information about the GOI and soyabeans and highlights the high complexity of the global grain spot markets.Comment: 23 pages including 14 figure

Impact of carbon markets on industrial competitiveness: An analysis of selected industries in Beijing

Although the booming carbon markets provide additional incentives to reduce greenhouse gases, their impacts on the society and economy have attracted increasing attention. Based on 2014–2016 daily carbon market trading price data, this study estimates the direct and indirect carbon emissions cost incurred by Beijing carbon market and explores its impact on industrial competitiveness via an evaluation model. Our results show that the impact of the carbon emissions cost is negligible, and the proportion of the three most affected industries’ added values to Beijing’s gross domestic product is only 10%, indicating that the economic impact is limited. However, the impact on the production and supply of power, gas and water industry could reach as high as 3.02% in three years. Compared with the European carbon market, the trading price of Beijing’s carbon market is relatively low, and the price cap could possibly increase to 100 Yuan per ton. However, each 10-Yuan increment in the carbon price will increase the impact on industry competitiveness by 1.68%. This study provides a scientific basis for exploring the impact of China’s carbon market on industry competitiveness and will be of significant value to policy makers

Price bubbles in Beijing carbon market and environmental policy announcement

This paper examines price bubbles in the relatively new carbon emission trading scheme of Beijing carbon market by employing a recently proposed econometric test which can stamp the occurrence and burst of financial bubbles. We find multiple bubbles in Beijing carbon market over the sample period between January 2014 to April 2018, and that the occurrences of carbon price bubbles are closely related to the announcements of environmental policies by the Chinese government. Comparing our results to the EU ETS, we find that the volatility of carbon price in Beijing market is higher than EU, and interestingly, the bubbles in Beijing market occur when the price volatility is relatively low, while in EU market the bubbles correspond to the peaks of volatility. Our empirical results provide insightful policy implications in the context of the actual China’s carbon market reform. To achieve effective stabilization of carbon price, policymakers should publicize alert notifications of the price fluctuations, and strengthen the carbon markets supervision and promote its improvement

FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning

Federated learning (FL) has emerged as a new paradigm for privacy-preserving computation in recent years. Unfortunately, FL faces two critical challenges that hinder its actual performance: data distribution heterogeneity and high resource costs brought by large foundation models. Specifically, the non-IID data in different clients make existing FL algorithms hard to converge while the high resource costs, including computational and communication costs that increase the deployment difficulty in real-world scenarios. In this paper, we propose an effective yet simple method, named FedCLIP, to achieve fast generalization and personalization for CLIP in federated learning. Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters. Lightweight adapters can make the most use of pretrained model information and ensure models be adaptive for clients in specific tasks. Simultaneously, small-scale operations can mitigate the computational burden and communication burden caused by large models. Extensive experiments are conducted on three datasets with distribution shifts. Qualitative and quantitative results demonstrate that FedCLIP significantly outperforms other baselines (9% overall improvements on PACS) and effectively reduces computational and communication costs (283x faster than FedAVG). Our code will be available at: https://github.com/microsoft/PersonalizedFL.Comment: Accepted by IEEE Data Engineering Bulletin; code is at: https://github.com/microsoft/PersonalizedF