Asia-Pacific Financial Markets最新文献

We analyse the asymmetric impact of oil prices on the stock markets of the BRICS nations. Employing the Nonlinear Autoregressive Distributed Lag (NARDL) model, we examine the weekly data spanning from October 29, 2010, to May 28, 2021 for West Texas Intermediate (WTI) spot prices in USD per barrel, alongside stock price data from official stock market indices websites. The findings reveal a substantial long-run association of oil prices with stock markets of BRICS nations except South Africa with significant asymmetry observed in both short and long-term impacts. Specifically, fluctuations in oil prices exhibit divergent effects on stock markets within these nations necessitating nuanced policy responses. Investors and portfolio managers are encouraged to adopt nonlinear models for forecasting and portfolio management leveraging asymmetric effects for risk mitigation strategies. These suggestions underscore the importance of recognizing the nonlinear and asymmetric nature of oil price dynamics in shaping investment decisions and formulating effective policy measures to mitigate associated risks in BRICS stock markets.

This study examines the viability of Bitcoin as legal tender with particular emphasis on the experience of El Salvador. In particular, we examine the challenges, benefits, and costs, of using Bitcoin as legal tender in a country. Our analysis underscores the significant costs of using Bitcoin to process routine transactions. These costs, both temporal and financial, form a considerable barrier to Bitcoin’s widespread adoption and use as legal tender. Given the uncertain impacts that the successful adoption of Bitcoin may cause, we suggest that there is little reason for policymakers to actively drive Bitcoin adoption.

The paper examines the extent of connectivity and shock transmission among twenty international public REITs using both static and dynamic econometric measures. The result shows that the dominant status of the United States REIT market as a primary source of shock to the international REIT market is in contest, mostly with the Belgian, Russian, and French REITs. Our results also indicate that Belgium, Germany, and France are the leading sources of shock in the European Union REIT market. Singapore dominates the ASEAN Economic Community REITs; the United States leads the North American Free Trade Agreement REITs while Russia and the United States compete as leaders in the Asia–Pacific Economic Cooperation REIT market. Results from the dynamic spillover demonstrate that return and the volatility spillover are vulnerable to key global news—positive or negative—with the COVID-19 pandemic having a substantial influence on international REITs than oil price shocks and geopolitical news. We further find out that altogether, the 20 international REITs are highly connected, and their level of linkages increases in periods of heightened global market uncertainties. However, the connectedness levels of the markets reduce among the economic blocs, suggesting that market integration, in the perspective of REITs, is not well-achieved under established economic groupings. We believe these findings are significant for the recalibration of the international REIT market as it resets itself from the uncertainties ushered in by the insurgency of the COVID-19 pandemic. The findings also have relevant implications for the investor community.

The results of the study indicate a positive correlation between the level of distraction of institutional investors and the expropriation behavior of major shareholders. Furthermore, when independent institutional investors with stronger monitoring motives become distracted, they significantly increase the incentive for major shareholders’ expropriation. Companies with stronger external monitoring exhibit a more pronounced effect of increased expropriation by major shareholders when institutional investors are distracted. Finally, the study finds that the internal capital market makes state-owned enterprises more susceptible to major shareholders’ expropriation when institutional investors are distracted compared to private enterprises. In summary, this paper broadens the research on the influence of the “tunneling” motivation of major shareholders, verifies the impact of institutional investors’ limited attention on the capital market, and explores the external governance role of institutional investors.

Research in corporate finance suffers from bounded rationality due to static modeling. Adopting factor analysis, an unsupervised machine learning approach, and balance sheet information (accounts) over time, we find underlying dynamic latent corporate finance decisions. Our study identifies three latent corporate finance decisions adopted by executives in Pakistan, (1) long-term capital investment, (2) short-term debt credit, and (3) financial flexibility. The order of the decisions and the empirical tests highlight agency problems rooted in familial ownership concentration. We find that long-term capital investment and short-term debt credit decisions significantly reduce the firms’ present and future performance. Conversely, managers do not embrace financial flexibility, despite its ability to improve performance. The study highlights a contradiction, firms bounded by ownership concentration prefer control over performance and do not make decisions to optimize and protect minority shareholders’ wealth, depicting a moral hazard problem.

Building upon the empirical studies by Liu (2:57–60, 2010) and Liu and Mancino (2012), we investigate the determinants influencing the term structure of interest rates in seven European countries: Austria, Belgium, Britain, France, Germany, Italy, and Spain. We use two methods, namely principal component analysis (PCA) for covariance matrix estimated by realized volatility estimator and PCA of integrated volatility estimated by Malliavin-Mancino (MM) estimator using Fourier series method proposed by Malliavin and Mancino (6:49–61, 2002; 37: 1983–2010, 2009), to examine spot rates and forward rates derived from zero-coupon bond data. The results of the study confirm that although three factors account for the majority of spot rate variability, a more significant number of factors is essential to capture forward rate dynamics adequately. This research complements the results established by earlier studies, providing a more comprehensive understanding of interest rate dynamics across these European markets.

This study examines the capital structure adjustment process followed by Indian Firms. Our study focuses on investigating when a firm changes its capital structure. We discover a pattern of capital structure adjustments among Indian firms, where the financial surplus or deficit of Indian firms drives the decision to adjust the capital structure. The results show that the capital structure adjustment speed for Indian firms measured using book-value-based leverage is around 39% when firms have an above-target debt with a financial surplus and about 26% when firms have a below-target debt with a financial deficit. The adjustments occur when firms have above-target/below-target debt with a financial surplus/deficit. Our results show that Indian firms adjust their capital structure conditioned upon the firm’s financial surplus/deficit.

In this paper, we examine the effects of return and volatility shocks captured from Bitcoin to other seven types of major cryptocurrencies by employing the daily data spanning from June 2011 to June 2020. We examine return and volatility transmission from Bitcoin to other cryptocurrencies using ARMA-GARCH model and extension of the asymmetric model of ARMA-TGARCH and ARMA-EGARCH. Moreover, we apply Dynamic Conditional Correlation and Asymmetric Dynamic Conditional Correlation (DCC and ADCC) models to measure the time-varying nature of conditional correlation. The results of the study show strong evidence of shocks transmission from Bitcoin to other cryptocurrencies in terms of both returns and volatility spillover, except for some less inefficient cryptocurrencies. In addition, the majority of the cryptocurrencies also reflect strong evidence about time-varying dynamic conditional correlation with asymmetric effects that adds ups the significant novelty in the existing literature from the methodological perspective as well.

This study innovates in credit default prediction in Pakistan by developing, calibrating, and recalibrating machine learning-based credit scorecards for non-financial listed firms, leveraging extensive financial ratio analysis. This study innovates in credit default prediction in Pakistan by developing, calibrating, and recalibrating machine learning-based credit scorecards for non-financial listed firms, leveraging extensive financial ratio analysis. Identifies 12 key financial ratios out of 71 remained vital for default prediction, with Random Forest and Artificial Neural Networks leading in scorecard performance. This marks Pakistan’s first detailed scorecard approach as a potential alternative to traditional banking systems. Offers advanced risk assessment tools (credit scorecards) for improved credit risk management, aiding policymakers and finance professionals in decision-making. This research distinguishes itself through a detailed longitudinal study of non-financial Pakistani firms and a comprehensive evaluation of machine learning algorithms for default prediction. By exploiting various financial ratios to develop scorecards (an alternative of Internal Ratings-based – IRB System), it offers new insights into risk evaluation and significantly advances financial risk management. Acknowledging data limitations and variable exclusions, it sets the stage for further exploration of credit risk environment in context of Pakistan.