Asia-Pacific Financial Markets最新文献

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.

The study aims at investigating the volatility and connectedness aspect of the SME and main market indices of India and China i.e., SENSEX, BSE SME IPO, SZSE Composite and SME 300. GARCH and TARCH models are used to determine the symmetric and asymmetric volatility within the indices respectively using daily data from January 2013 to March 2024. The DCC-GARCH model is applied to analyse the inter-country and intra-country volatility spillover. While, TVP-VAR model measure the connectedness between the indices. The empirical findings reveal that the SME index of India gives higher returns than China. On the volatility front, the SME of India and China have same degree of volatility. In the long term, there is a significant the spread of volatility between the SENSEX and SME index in India. The findings of the study show the high degree of long-term dependence and interconnectedness of SME markets of India and China. Further, it is found that the main market and SME market indexes of India are the net receivers of volatility, and the index of the Chinese market is the net transmitter. The Total volatility spillover index between main and SME is low in India as Compare with China. This study can help to mutually beneficial economic stability, and risk management. Also, it can help investors to take better investment decision.

Identifying investment opportunities with high returns is crucial for individuals seeking to generate wealth and accumulate assets. The cryptocurrency market exhibits high volatility and significant price variations compared to the stock market, suggesting that price movements may be influenced by additional factors. Identifying and categorizing these factors helps in making precise forecasts about the increase in investors’ asset. Thus, this study aims to investigate the primary elements influencing cryptocurrency’s’ market performance. The qualitative approach included a literature review, sentimental analysis and in-depth interviews with investors. Non-probability sampling techniques were adopted in identifying the prospective respondents for in-depth interviews. Additionally, the bibliometric analysis helped in the collection of appropriate literature for a systematic literature review. A taxonomy was built, combining all codes received with the help of experts. Distinct determinants were identified and categorized as internal and external factors, which were then further subdivided. Internal variables include fear of missing out, money accumulation, flexibility, and understanding of patterns. External factors such as technological progress, community involvement, airdrops/roadmap, news/speculations, and government laws also have a role. Understanding the determinants helps investors and traders gain appropriate knowledge on investments and profit-making, thereby yielding wealth that could provide financial freedom and a better lifestyle. This study is novel because exploring, understanding, and predicting the cryptocurrency market is one of the latest and most widely spoken topics among researchers in the finance domain. Earlier studies have not emphasized empirically the concepts, strategies and factors impacting price fluctuations and investment behavior in the crypto market.

This study examines the time-varying nature of investor herd behavior over different market episodes in Sri Lankan stock market, that has been subjected to convulsed periods such as civil war, political instability, terrorist attacks and COVID-19 pandemic. The study employs Cross-Sectional Absolute Deviation methodology, applying quantile regression approach, to detect aggregate level herding using a survivorship-bias-free dataset of daily firm level returns from April 2000 to March 2022. The dataset is subdivided into market episodes corresponding to pre-war, bubble, crash, post-crash, pre-COVID crash, COVID bubble and post-COVID crash periods. Exhibiting an evolutionary herding pattern over market episodes, the results depict that herding appears in pre-war period irrespective of the market directions, persisting in bubble episode in upmarket days, which then, turning into negative herding in down market days in crash episode. Subsequently, herding gradually disappears in post-crash episode, reappears with greater intensity in pre-COVID crash episode and disappears in COVID bubble and post-COVID crash episodes. This study attributes such wax and wane nature of herding in financial markets to a survival action, a rational heuristic, in keeping with Adaptive Market Hypothesis. The study is of peculiar importance to investors, policymakers, regulators and researchers, as presence of herding misprices securities and invalidates the existing asset pricing models constructed on the assumptions of investor rationality.

Given the growing significance of socially responsible investing (SRI), the study aims to empirically examine the financial performance of socially responsible indices of India, China, the United States (US), and the United Kingdom (UK) vis-à-vis their respective market benchmark indices. The study uses various risk-adjusted performance measures such as Sharpe ratio, Jensen alpha, Treynor ratio, Information ratio, Modified Sharpe ratio, Sortino ratio, and Omega ratio to analyze the performance of SRI indices. The period of analysis extends from January 2018 to December 2021. The study performs various sub-period analyses including a crisis period analysis to assess the impact of the COVID-19 (coronavirus disease) crisis on the performance of select indices. Statistical tests such as the paired t-test and Levene’s F test are applied to examine the homogeneity of means and variances of sample indices. Robustness checks involve calculating performance metrics across varying sample sizes using a growing window procedure. The results highlight the outperformance of SRI indices over market benchmarks in India, the US, and the UK, suggesting that investors do not have to forgo financial performance to address their environmental, social, and governance (ESG) concerns. There is no statistically significant outcome observed for SRI performance in China. Empirical evidence from the crisis period analysis indicates that SRI can offer investors a hedge against market volatility. Overall, the findings suggest that there is no homogenous or universal outcome of SRI but rather varies depending on geographic region, study period, current market conditions, and extent of SRI adoption.

The paper is an extended contribution to the ongoing debate on cryptocurrency as a hedging instrument while investing in developed and emerging markets. At the edge of the 4th industrial revolution, the paper identifies diversification opportunities with technologically based assets and non-conventional assets like Cryptocurrency (BITW), Fintech (FINX), and Green Equity Index (QGREEN) with the Developed market (MSCI World Index) and Emerging market (MSCI Emerging Markets Index). The study is rigorous in methodology, including Granger Causality, Symmetrical and Asymmetrical Dynamic Conditional Correlation models, Diebold Yilmaz Spillover Index, and Network analysis. The study used robust statistical models like Granger Causality, Symmetrical, and Asymmetrical Dynamic Conditional Correlation models, Diebold Yilmaz Spillover Index, and Network analysis for a more accurate assessment of the investment alternatives. The results of the study aim to assist passive portfolio managers in investing in developed and emerging indices and looking for non-conventional investment options. The study assumes relevance for policymakers, as it deciphers the relevance of the cryptocurrency market vis-a-vis other emerging assets.

This study aims to demystify the financial sustainability social outreach trade-off debate in the case of Microfinance Institutions (MFIs) operating in India. In particular, the authors estimate the bias-adjusted efficiency of MFIs operating from 2010 to 2019 to scrutinize the mutual exclusivity between their twin aspects. Further, the study deploys bootstrap Data Envelopment Analysis (DEA) to estimate the robust efficiency estimate of individual MFIs. Further, the study uses a multi-dimensional approach to examine the trade-off debate between sustainability and outreach. Additionally, the study also ranks the MFIs based on their dual mission. The results suggest that the Indian MFIs are better at handling the financial dimension than the social aspect of MFIs. Moreover, the article claims the absence of a trade-off between the two goals of MFIs in India. Suryoday is the top-performing MFI in terms of financial and social aspects, followed by M-power. Further, the policymakers, top management, and microfinance professionals must redesign the regulatory and operational structure to ensure the maximum social outreach of MFIs without hampering their financial sustainability.