Global Finance Journal最新文献

This paper provides empirical evidence on the finance-growth nexus, making key contributions by focusing on previously understudied Emerging Markets and Developing Economies (EMDEs) and employing mixed-frequency data. Utilizing panel forecasting models for 50 countries from 1990 to 2019, we examine the empirical link between macroeconomic indicators (e.g., aggregate production) and financial indicators (e.g., stock market indexes). Our results support the notion that financial indicators can indeed serve as robust predictors of macroeconomic indicators. Further, the use of mixed data sampling (MIDAS) models enhances the results, confirming the presence of valuable predictive information in higher-frequency data, even for lower-income countries. These findings bear particular significance for policymakers and investors, given the persistent challenge of accessing timely and reliable data on real indicators in EMDEs.

This study applies the panel smooth transition regression model to a 13-year sample of 16 Taiwanese non-life insurance companies to examine market competition's impact on Asset risk.

Underwriting risk Investment risk and differentiate between financial holding companies (FHCs) and non-FHCs (NFHCs). For NFHCs, increased competition reduces asset risk in high-leverage firms, supporting the modified moral hazard hypothesis. For FHCs, greater competition lowers asset risk only above a leverage threshold, indicating superior risk management and affirming the competition stability hypothesis. The effect on underwriting and investment risks depends on operational tenure; below a certain threshold, competition increases underwriting and investment risk, whereas competition above the threshold decreases risk, showing that experience improves risk management. This study offers key insights into how competition influences risk across different types of insurance companies in Taiwan.

We evaluate the performance of expected return proxies during extreme credit market conditions and extreme phases of business cycles when realized returns on banks stocks are large in absolute value. We construct three sets of expected return proxies for individual bank stocks: (i) characteristic-based proxies; (ii) standard risk-factor-based proxies; and (iii) risk-factor-based proxies in which betas depend on firm characteristics. Based on the newly developed minimum error variance (MEV) criterion (Lee et al., 2020), the best performing expected return proxy is the risk-factor-based model that allows betas to vary with firm characteristics. We also examine whether these three expected return proxies can capture actual returns during either extreme credit market or extreme business-cycle conditions. We find that both risk-factor-based proxies explain returns better than characteristic-based proxies during these periods.

This study investigates the contribution of the limit order book to the price discovery process of blue-chip stocks traded on Borsa Istanbul. Using various price series, including the last trade price, best prices of the order book, and price steps beyond the best price levels, we measure the contribution of orders beyond the best prices to price discovery. This contribution is evaluated through information shares. Our findings highlight the significant informational role of the order book in price discovery, emphasizing its importance alongside trading activity for a comprehensive understanding of the market. Additionally, this analysis is conducted across distinct stock characteristics, specifically return, size, volume, and illiquidity, revealing notable variations in the information share of the limit order book.

The FinTech sector is growing rapidly, prompting a need to explore effective investment diversification strategies for stocks in this sector. The existing literature has identified the benefits of using clean energy stocks to diversify stock portfolios and the purpose of this research is to estimate how useful clean energy stocks are for diversifying an investment in FinTech stocks. This study uses a QVAR model to estimate the dynamic return connectedness between FinTech stocks and clean energy stocks for the period September 2016 to April 2024. Total connectedness is time varying and is higher in the tails than at the median. The onset of the COVID-19 pandemic had a large but short-term impact on connectedness. Under normal market conditions, systemic risk increases by 3.5% per year. FinTech is a net transmitter of shocks to nuclear energy but is mostly unaffected by shocks from wind, solar, and nuclear energy stocks illustrating the diversification benefits of these sub-sectors. Portfolio analysis shows that adding solar, wind, and nuclear energy to a portfolio with FinTech can produce higher risk adjusted returns and lower drawdown than an investment solely in FinTech stocks. These results are robust across various portfolio rebalancing frequencies (daily, weekly, monthly). For example, a minimum connectedness portfolio rebalanced daily has an average annual return of 11% and a Sharpe ratio of 0.37. These values are higher than their respective values for an investment solely in FinTech stocks (5.4%, 0.11). Thus, clean energy stocks do provide diversification benefits for investments in FinTech stocks.

Companies' risk preference and risk performance, which reflect their inclination to seek higher returns, significantly influence their decisions and behaviors. The current development of digital transformation is an effective strategy to improve enterprises' competitiveness. Studies have earlier examined the functions of digitalization, such as improving business operations and efficiency. Using data from 2847 listed companies in China from 2011 to 2019, this study examines the extent of digital transformation in enterprises and its impact on their risk performance behavior. The results show that digital transformation significantly improves enterprises' risk performance. Mechanism testing shows that optimized corporate governance processes and increased investment in research and innovation act as positive intermediaries through which digitalization affects the level of corporate risk performance. These findings contribute to our understanding of the role of enterprises' digital transformation behavior and recommend relevant policies to facilitate a more effective path for enterprise development and reform.

The backdrop of this research is the high global uncertainty that has amplified the demand for safe-haven assets, particularly in the East Asian market. This paper redefines the concept of a “safe-haven” currency to align with contemporary geopolitical and trade policy uncertainties, diverging from the traditional volatility index (VIX) risk measure. We investigate the risk aversion properties of East Asian currencies under these nonmarket risks using dynamic heterogeneous panel data analysis and robustness checks with double machine learning. Empirical results reveal that no East Asian currency qualifies as a safe haven under geopolitical risk and trade policy uncertainty. However, the Japanese yen (JPY) maintains its status under the VIX indicator. This study highlights the insufficiency of traditional safe havens like the JPY and underscores the importance of considering nonmarket risks, challenging the effectiveness of traditional investment strategies amid modern geopolitical and policy uncertainties. The findings suggest that investors should prioritize nonmarket risks and call for reform in the global monetary system to enhance currency resilience. The novel methodological approach to evaluating safe-haven currencies addresses the need for diversified currency portfolios to mitigate nonmarket risks.

This study aims to investigate the tail risk dependence of individual banks in Asian emerging markets. Using value at risk and conditional value at risk to measure tail risk and employing the least absolute shrinkage and selection operator regression to build the network, this study analysed interconnectedness at three levels: system-wide, country level and individual bank level. This study yields three key findings. First, banks in Asian emerging markets have a notably high tail risk network, particularly during more extreme market conditions. Second, the smaller and more interconnected banks are the most systemically important in the region, rather than the largest banks. Third, the time-varying results suggest that tail risk dependence, primarily attributed to cross-country connectivity, increased after the global financial crisis but has decreased in recent years.

The study investigates the interconnectedness and risk spillover among a diverse range of financial assets, including thirty-three cryptocurrencies, thirteen sectoral indices, six exchange rates, four precious metals, and six energy commodities. Using diverse methodologies, including partial correlation network, dynamic causality index, Granger causality network, cross-quantilogram and Bayesian graphical VAR model, the findings reveal intriguing insights, such as cryptocurrencies exhibiting a negative relation with other asset classes, minimal interconnectedness during the COVID-19 pandemic, and their vulnerability to shocks. Moreover, there is a stronger dependence structure from energy commodities and exchange rates to other classes, while moderate temporal dependencies exist between cryptocurrencies and other assets. These results emphasize the need for understanding and managing risks in the cryptocurrency market and highlight the interconnected nature of financial markets. The interconnectedness among various asset classes is mainly driven by variables representing market and economic sentiment, uncertainty and business confidence.

This study proposes an advanced model-free deep reinforcement learning (DRL) framework to construct optimal portfolio strategies in dynamic, complex, and large-dimensional financial markets. Investors' risk aversion and transaction cost constraints are embedded in an extended Markowitz's mean-variance reward function by employing a twin-delayed deep deterministic policy gradient (TD3) algorithm. This study designs a DRL-TD3-based risk and transaction cost-sensitive portfolio that combines advanced exploration strategies and dynamic policy updates. The proposed portfolio method effectively addresses the challenges posed by high-dimensional state and action spaces in complex financial markets. This methodology provides two optimal portfolios by flexibly controlling transaction and risk costs with (i) the constituents of the Dow Jones Industrial Average and (ii) the constituents of the S&P100 index. Results demonstrate a strong portfolio performance of the proposed DRL portfolio compared to those of several competitors from the traditional and DRL literatures.