This paper studies liquidity and volatility commonality in the Canadian stock market. We show that five various liquidity measures display strong evidence of commonality at both market-wide and industry specific levels. Our findings extend the results of previous studies in liquidity commonality, and show that even after controlling for individual determinants of liquidity such as price, volume, and volatility, liquidity commonality remains. In addition to demonstrating liquidity commonality, we also investigated the causal relationship between liquidity and volatility. Our evidence indicates that depth, proportional effective spread, and liquidity changes predict volatility changes for bid-ask spread, depth, and proportional effective spread.
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http://dx.doi.org/10.1186/s40929-017-0016-9 | DOI Listing |
The development of China's National Carbon Market has strengthened the inherent link between the carbon market and the broader energy market, providing a potential for cross-market risk transmission resonance. Studying the risk spillover effects between China's National Carbon Market and the crude oil futures market is of significant practical importance, both in terms of carbon market development and carbon risk management. Based on the Maximal Overlap Discrete Wavelet Transform (MODWT), the price series are decomposed across multiple scales, and the risk spillover effects between the carbon market and the crude oil futures market are examined from both the time domain and the frequency domain.
View Article and Find Full Text PDFHeliyon
December 2024
School of Accounting, Zhongnan University of Economics and Law, Wuhan, China.
The Ghanaian banking sector, grappling with a spectrum of financial risks, presents a compelling case study for understanding the dynamics of risk and profitability in emerging markets. This study seeks to fortify the financial performance of Ghanaian banks through an innovative application of benchmark regression analysis, focusing on critical financial risk and performance metrics. Employing an explanatory research methodology, we harnessed a panel regression model to scrutinize secondary data extracted from the annual income statements of 23 banks, spanning nearly two decades from 2006 to 2023.
View Article and Find Full Text PDFEntropy (Basel)
August 2024
Computational Science Lab, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
The foreign exchange (FX) market has evolved into a complex system where locally generated information percolates through the dealer network via high-frequency interactions. Information related to major events, such as economic announcements, spreads rapidly through this network, potentially inducing volatility, liquidity disruptions, and contagion effects across financial markets. Yet, research on the mechanics of information flows in the FX market is limited.
View Article and Find Full Text PDFWe study the development of FinTech, defined as a set of innovations and an economic sector that apply recently developed digital technologies to financial services, with particular focus on payment and lending platforms, and digital asset management and online trading apps. We use mixed methods, including a theoretical exercise on the main balance sheet interactions involved in FinTech banking, and empirical insights from fieldwork in Latin America and the United States. Our analysis corroborates previous literature identifying several systemic risks in FinTech payment and lending platforms.
View Article and Find Full Text PDFPLoS One
June 2024
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!