Asia-Pacific Financial Markets最新文献

This study used a Time-Varying Parameter VAR approach to analyze contagion risk among global stock markets and WTI crude oil during times of crisis. The examined markets included the United States, the Eurozone, the United Kingdom, China, Japan, India, Russia, and select MENA stock markets. The research highlighted the importance of dynamic metrics in assessing financial networks and crisis contagion risk, an area that has received limited attention in previous studies. The evidence demonstrates rapid and dynamic financial contagion resulting from lockdown measures, the spread of COVID-19, and the Russia–Ukraine war. The U.S. and major European markets were identified as net global contributors, while Chinese and MENA equity markets acted as net receivers. Furthermore, the origin of oil shocks was more likely attributed to Russian and Saudi markets. This research carries policy implications for policymakers and investors, emphasizing the importance of shock and contagion effects in portfolio diversification and risk hedging, particularly during times of crisis.

This study examines the impact of the global pandemic on the returns and volatility of China’s commodity futures market from December 2019 to April 2021. Our analysis reveals that the regimes of futures returns in the general commodity, industrial, and metal markets are positively correlated with the regimes of pandemic cases, while the regimes of pandemic cases are negatively correlated with the returns of energy and precious metal futures. In contrast, futures volatilities exhibit inverse relationships with pandemic cases. With the exception of precious metals, which are widely considered safe-haven assets, the risk level of the commodity futures market, as measured by return volatility, is heightened by the level of pandemic cases. Bivariate SVAR results suggest that the pandemic has a greater but short-run impact on futures returns, while its effects on futures volatilities are relatively lesser but long-lasting.

Despite an attractive interest rate differential between China and foreign countries, existing capital control might prevent currency carry trade strategies to be executed. We focus on the copper market to study if trades are taken in order to execute carry trade strategies. We find that copper value is related to carry trade through the onshore-offshore interest differential, while the pegged nature of the USD/CNY exchange rate makes traders indifferent to the forward risk premium. We rule out the possibility of high average payoff due to peso problems, because risk factors are insignificant, implying that carry traders are either fully hedged on FX risks, or they are unconcerned about FX risks.

This paper identifies the presence of anomalies in Chinese urban construction investment bonds (UCIBs) market using variable ranking portfolio analysis and finds that liquidity anomalies, downside risk anomalies, and historical return anomalies significantly exist. By conducting Fama–MacBeth regressions on the cross-sectional returns of UCIBs and anomalies, we find that only the 6-month momentum in the historical return anomaly can generate statistically significant risk premium which cannot be explained by long-established bond pricing factors, and thus it’s an anomaly for UCIBs. This paper also finds that portfolios constructed based on significant anomalies in the UCIBs market can generate more profits than other models through the out-of-sample cross-sectional return forecasting.

The primary goal of the study is to examine the factors affecting the financial leverage of unicorn start-ups in India. In order to achieve this goal, the study has employed the panel data techniques on the financial data of 25 start-ups unicorn of India from 2017 to 2021. The study has employed three proxies to measure the financial leverage namely short-run, long-run, and total debt ratio. The result of the study indicates that firm size and profitability are significantly negatively correlated with debt ratios, whilst tangibility, business risk, and firm age are positively and significantly associated. Moreover, short-term debt is found to be more prevalent in unicorn firms when we bifurcate total debt into short and long-term debt. As per the best of author’s knowledge, this is the first research that identified the financial choice of startups. Furthermore, this study provides a pathway for conducting future study in this domain on startup firms’ capital structure decisions. This study has major implications for unicorn managements in taking decisions regarding their finance choice that may lead them to plan adequately their capital structure more efficiently and effectively.

This study investigates the interconnectedness between cryptocurrency and the Indian stock market and explores the diversification, hedge, and safe haven potential of cryptocurrency. The study employs the wavelet approach on daily data from October 6, 2017, to October 5, 2022, to execute the empirical analysis. The findings confirm that, in a healthy economic environment, cryptocurrencies are not connected with the Indian stock market. However, during times of financial turmoil, Bitcoin, Ethereum, and Cardano are positively correlated with the stock market. Additionally, the study identifies Bitcoin, Ethereum, Dogecoin, and Cardano as competent in providing diversification, or hedge opportunities in normal economic situations. But during periods of financial stress, only Dogecoin may act as a safe haven asset. This is the first study to explore the time-varying correlations and causal dependencies between the stock and cryptocurrency markets in India using the wavelet approach. It extends the literature in finance by examining both normal and economic turmoil periods, providing insights for portfolio managers, policymakers, and investors on how to manage their portfolios during these periods.

The paper investigates the influence of the alumni social network of mutual fund managers on fund performance in India. The alumni networks of 211 managers managing 585 funds are constructed through seven network centrality measures for the period from April 2013 to March 2022. The study finds that fund managers who are more central to the network on average generate higher risk-adjusted excess return performance (alpha) and take a higher level of idiosyncratic risk. Although the centrality position of managers does not influence them in selecting small-capitalisation, value, and high market-beta stocks, more central fund managers tend to pick momentum stocks. The results affirm that the information advantages in the central position of alumni social networks improve fund performance, influence the managers’ investment style and enable higher risk-taking behaviour. The contribution of the paper is that the findings regarding investment style and fund flows are different than those of developed markets which may be relevant for other emerging markets.

In this paper, we have carried out the predictions for growth or spurt in Systemic Risk across the different categories of financial institutions in India relative to the change in the market prices. We have used Bayes Theorem along with Logistic regressions to work out the actual probabilities regarding the growth in Systemic Risk with the fall in stock prices and Wilcoxon Rank sum Test to validate the robustness of the models. In this paper, we have studied the period from July 2007 to December 2020. An important feature observed was any fall in closing prices beyond 30%, is contributing for 90% growth in systemic risk. A policy implication can follow—that it is imperative to monitor a sharp decline in market prices to the tune of 30% or more by regulators to avoid a crisis. We generally presume that state ownership of Banks particularly in India generates public confidence. Our paper has been able to support the theory of public confidence wherein the Public Sector Banks are contributing less towards the growth of Systemic Risk as compared to Private Banks and NBFCs. The NBFCs are the highest contributor of the growth in systemic risk which we have differentiated from our results. So, in coming days NBFCs are to be closely monitored by the regulators and suitable regulatory measures need to be placed.

The volatility of crude oil markets and the pressing need for sustainable energy solutions have sparked significant interest in forecasting methodologies that can better capture market dynamics and incorporate environmentally responsible indicators. In this study, we address the gaps in the literature by proposing novel hybrid approaches based on combining wavelet decomposition with machine learning techniques (ANN-Wavelet and SVR-Wavelet) and advanced machine learning techniques (XGBoost and GBM) with advanced clean energy indicators to predict crude oil prices. These hybrid models significantly advance the field by reducing noise and improving result accuracy. Besides, these approaches were used to determine the best model for predicting crude oil market prices. Additionally, we employed the SHapely Additive exPlanations (SHAP) algorithm to analyze and interpret the models, enhancing transparency and explainability. Subsequently, we applied SHAP to investigate the predictive value of various asset classes, including the volatility index (VIX), precious metal markets (gold and silver), fuel markets (gasoline and natural gas), as well as green and renewable energy indices, about crude oil prices. The results reveal that the wavelet-SVR model demonstrates consistent and robust forecasting performance with low RMSE and MAPE values. Additionally, the GBM model emerges as highly accurate, yielding shallow forecasting errors. Conversely, the wavelet-ANN and XGBoost models exhibit mixed performance, showing effectiveness in the Full Sample but reduced accuracy during the Russia–Ukraine conflict. Notably, green and renewable energy markets, such as CGA and NextEra energy (NEE), emerge as significant predictors in forecasting crude oil prices. This research provides critical guidance amidst the Russia–Ukraine conflict in predicting oil prices by emphasizing the importance of incorporating environmentally responsible indicators into investment portfolios and policy choices.

It is of great significance to empirical test the expectation hypothesis of the term structure of interest rates. Most existing empirical literature using cointegration test with monthly data. With the easier access to high frequency data, using high frequency data to empirical test can reduce information loss and get more reliable conclusion. This paper proposes a new method which is called functional cointegration test and empirical test the expectation theory hypothesis using Chinese treasure yield daily data which contains 3001 trading days from 2011 to 2022 with 14 different maturities. The empirical results show that all 91 groups of different long-term and short-term interest rates combinations have significant cointegration relationship. The expectation theory hypothesis valid for all long-term and short-term interest rates combinations in China. This paper provides a new functional data analysis perspective for the empirical test of the expectation theory hypothesis, and also explores the application of functional data analysis in economic field.