Asia-Pacific Financial Markets最新文献

This paper investigated exchange rate and stock price volatility connectedness and spillover in Brazil, Russia, India, China, and South Africa (BRICS) during pandemic-induced crises. We first extracted volatility using the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. Then volatility connectedness and spillover were investigated by using (Diebold and Yilmaz, International Journal of Forecasting, 28(1), 57–66, 2012) method. We find that exchange rate volatility and stock return volatilities are connected during pandemic-induced crises. The study also finds volatilities spillover among countries in the sample. Russia has strong volatility connectedness with India in these financial markets. The direction of volatility spillover is from Russia to India. Similarly, Brazil has strong volatility connectedness with South Africa and the direction volatility spillover is from Brazil to South Africa. Finally, China has a weak volatility connection with the remaining BRICS countries. Thus, the volatility transfer in these financial markets and across BRICS countries has economic implications.

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

This study aims to examine antecedents of the pension schemes in Indian context. The relationship between the factors underlying the perception of subscribers towards the pension plan: financial knowledge, investment strategy, and satisfaction of investors (employees) is examined. Further, the effect of financial security, future financial goals, risk appetite, and secured returns on the investment strategy and satisfaction are explored. After checking the measurement properties of the structured survey instrument using the structural equation modeling with Lisrel package, data collected from 480 employees working in various administrative units of a State in the northern part of India, were analyzed. The Hayes’s PROCESS was used in analyzing the moderated moderated-mediation complex model and the results reveal that (i) financial knowledge is positively related to (a) investment strategy, and (b) investor satisfaction. The investment strategy mediated the relationship between financial knowledge and employee satisfaction. Further, the results indicate that future financial goals (first moderator) and financial security (second moderator) moderated the relationship between financial knowledge and investor satisfaction mediated through investment strategy. The results also documented that risk appetite moderated the relationship between investment strategy and investor satisfaction; and secured returns moderated the relationship between financial knowledge and employee satisfaction. The novelty of this study stems from the three-way interaction between the financial knowledge, future financial goals, and financial security in influencing the financial strategy. The implications for research and practice are discussed.

Tobin’s Q is an established measure of firm performance, based on investor confidence. However, the association between Tobin’s Q and credit ratings is not well-established in the literature. Using a sample of Korean listed firms over the 2001–2016 sample period, Probit regression analysis shows that overall, Tobin’s Q is positively associated with credit ratings. However, for firms with a > 1 (1 <) Tobin’s Q ratio, a negative (positive) relationship exists. Moreover, in independent regressions, a threshold level if found where the effect of Tobin’s Q on credit ratings changes from being positive (0.2), to negative (0.3). To the best of our knowledge, we are the first to demonstrate that credit rating agencies are nuanced when making default risk assessments. Specifically, that in South Korea, a threshold level exists, at which increasing Tobin’s Q values reduce credit ratings. Empirical evidence of the different association between Tobin’s Q (market confidence) and credit ratings can extend the literature and offer insights to market participants. Furthermore, because Tobin’s Q is a commonly used proxy for financial performance in accounting lectures, the study has practical implications for academics in classrooms.

This study explores the effectiveness of entropy as a proxy of aggregate market risk, in explaining the cross-section of excess returns in asset pricing model, after controlling for established factors like market excess returns, size, book to market and momentum. The analysis considers Indian firms, given that Indian capital markets are characterized by relatively thin trading and higher volatility compared to developed markets. Entropy is estimated using Shannon Entropy. Factor mimicking portfolio is constructed based on Shannon Entropy, whose returns are used as additional risk factor in Fama–French–Carhart four factor asset pricing model. Gibbons Ross Shanken-F statistic and Adjusted R² are used to judge the efficacy of this factor in capital asset pricing model. All analysis is done using built in functions of python. Market beta, size and Book-to-Market are found to impact equity returns significantly. Entropy factor also impacts equity returns, but to a lesser extent. Explanatory power of asset pricing model is found to improve after inclusion of entropy factor, as indicated by GRS-F Statistic and Adjusted R². Entropy augmented Capital Asset Pricing Models can be used by firms to decide hurdle rate for project evaluation and by asset managers for identifying over-valued/under-valued securities. This is the first study that investigates the role of entropy in explaining asset returns, in addition to other established priced factors. This study is limited to Shannon Entropy only. Other forms of entropy may improve results further, and should be explored in future research.

This study investigates the impacts of industry competition and market share on the long-run performance of firms conducting seasoned equity offerings (SEOs). These two factors are related to the “market dominance” and “expense preference” hypotheses, which suggest that dominant (low-competitive and high-market-share) firms would perform well after SEOs if they can bring their market advantages into full play and poorly if managers intend to hold more funds to expend, respectively. The results show that dominant SEO firms tend to outperform their matching firms and challenging (high-competitive and low-market-share) firms, supporting the market dominance hypothesis. This finding implies that firms with advantages in the product market can increase their competence via SEOs due to their ample resources. We contribute to the literature by showing that business risk can affect the performance following financing activities, a result that can help long-run investors select more promising SEO stocks.

The main economic challenge is rising aggregate demand, which leaves the economy short on resources and leads to expanding fiscal and external account deficits. The current study uses autoregressive distributed lag (ARDL) model to evaluate the twin deficit hypothesis in the context of Pakistan in an effort to find an answer to this question. The study uses augmented ARDL, popularized by McNown et al. (Appl Econ 50:1509–1521, 2018) and Sam et al. (Econ Model 80:130–141, 2019), to address the degenerate problems that might arise while applying the ARDL approach. Two separate models were estimated, one with the current account balance as the dependent variable and the other with the balance of trade. In the long run, both models confirm the conventional interpretation of twin deficit hypothesis in Pakistan, with the causality running only from the fiscal deficit to the balance of trade. Other control variables in both models are crucial in understanding the current account balance and balance of trade. According to models, an increase in the exchange rate, as measured by the log of the nominal effective exchange rate, improves both current account and trade balance, verifying the elasticity approach in the long run. The openness of the economy is found to worsen current account balance, and the result is statistically significant. Contrarily, openness has been improved trade balance, but the result is statistically insignificant. To control a large and persistent external deficit, the government has to reduce its fiscal deficit, and such a strategy would be successful when monetary policy is accommodative.

While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of ‘foreboding’ emanating from catastrophic events has not been explored in literature. This paper explores “foreboding”, which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing ‘foreboding’ or ‘fearful apprehension’, for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions.

Regardless of the asset class, applying multi-period dynamic portfolio optimization to real investment activity is challenging due to theoretical and structural complexities. In terms of a bond portfolio based on a stochastic interest rate model, some literature exists that focuses on theoretical aspects of multi-period dynamic bond portfolio optimization, such as deriving analytical solutions for optimal portfolios, to be sure, but no empirical studies analyzed the actual bond market. Additionally, a methodology that considers realistic investment constraints has not been developed thus far. In this paper, we propose a new framework for multi-period dynamic bond portfolio optimization. As bond return can be approximated by a linear combination of factors that constitute a stochastic interest rate model, we apply linear rebalancing rules that consider transaction costs, in addition to self-financing and short sales constraints. Then, as an empirical analysis, we conduct an investment backtest by analyzing discount bonds estimated from Japanese interest-bearing government bonds. The results indicate that multi-period optimization represents a relatively high performance compared to single-period optimization. Further, the performance improves as the investment horizon and investment utilization period are extended up to a certain point.

This paper develops a Structural Satellite version of the Financial-Macroeconometric Model of India (SSFMMI) to examine whether the surge in Nonperforming Assets (NPAs) in Indian Public Sector Banks (PSUs) post-2015 is due to macroeconomic shocks or better classification of loans and cleaning of bank balance sheets. Specifically, the paper analyses the impact of a rainfall shock, domestic food price shock, world oil price shock, fiscal shock, and monetary shock using counterfactual policy simulations and an out-of-sample forecasting framework to validate the impact of these macroeconomic shocks on NPA levels. The paper's outcomes suggest that the late surges in NPAs are not due to macroeconomic shocks and, therefore, that Indian banks are resilient to such shocks. However, the study reveals that the rise in domestic fuel prices and world food prices can cause a surge in NPAs levels.