Asia-Pacific Financial Markets最新文献

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.

This study measures the immediate impact of Russia–Ukraine war on the global stock markets for the first four months since Russia’s first invasion attempt on February 24, 2022. Daily closing stock indices have been used from selected stock markets of six different continents. By applying event study method, it observes mixed impact on different stock markets. Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH 1,1) indicates the presence of significant volatility and leverage effect in all the markets. Regression estimates show significantly positive impact of VIX and negative impact of oil on the abnormal returns of the global stock markets. Diversifying energy supply and source, accelerating deployment of renewables and promoting electronic vehicles and machines might bring positive result for the financial market. It is expected that this research will provide policymakers, regulatory authorities, investors and all concerned stakeholders a precise guideline to handle the immediate impact of war on the stock prices and to formulate appropriate strategies to keep investment free from risk and uncertainties.

In this paper, we are interested in the optimal investment and reinsurance strategies of an insurer who wishes to maximize the expected power utility of its terminal wealth on finite time horizon. We are also interested in the problem of maximizing the growth rate of expected power utility per unit time on the infinite time horizon. The risk process of the insurer is described by an approximation of the classical Cramér–Lundberg process. The insurer invests in a market consisting of a bank account and multiple risky assets. The mean returns of the risky assets depend linearly on economic factors that are formulated as the solutions of linear stochastic differential equations. With this setting, Hamilton–Jacobi–Bellman equations that are derived via a dynamic programming approach have explicit solution obtained by solving a matrix Riccati equation. Hence, the optimal investment and reinsurance strategies can be constructed explicitly. Finally, we present some numerical results related to properties of our optimal strategy and the ruin probability using the optimal strategy.

This study explores the time-varying effects of crude oil prices (OP) and gold prices (GP) on the Turkish stock market using a weekly data series from November 26, 1989 to July 10, 2022. For this purpose, we develop a new hybrid technique, the rolling window-based nonparametric quantile causality test, which allows the investigation of time-varying causality at various quantiles. The results reveal that (i) under all market conditions, there is time-varying causality from crude OP and GP to Turkish stock market returns (SMR) and volatility. (ii) The causal effects of both crude OP and GP on stock market volatility are larger than their causal effects on SMR. (iii) The crude OP have a greater impact on SMR than the GP, while the GP has a greater impact on stock market volatility than the crude OP. (iv) Both crude OP and GP have the strongest (least) causal impact on SMR and volatility under normal (bullish) market conditions. (v) Crude OP and GP have a greater impact on stock market volatility than on stock returns under all market conditions. Overall, our results highlight that OP and GP have a strong impact on the Turkish stock market, and this impact varies by returns and volatility. Therefore, financial investors should consider the volatility of crude OP and GP in the Turkish stock market.

This study established the direction, magnitude, and duration of the causality between CDS and selected Chinese stock sector at industry level. A nonparametric causality-in-quantile test and a CQ correlation test were applied to the data sampling over the daily period January 2, 2019, to January 6, 2023 covering a period marked by global shocks, including the outbreak of COVID-19 and Russia–Ukraine conflict. The empirical results reveal that CDS advances to play its economic role as a risk transfer, and to effectively predict the returns of sectors stock under bad market conditions. Moreover, the time-varying CQ correlations suggest that such amplified connectedness could be driven by extreme market circumstances in both the upper and lower quantiles. The findings provide important recommendations for investors, regulatory authorities, and policymakers to understand the pivotal roles of market sentiments in inducing co-movement between sovereign CDS spreads and selected Chinese stock sector.

The current research delves into the implications of exchange rate fluctuations on commodity trade between Pakistan and Japan, utilizing the nonlinear autoregressive distributed lag method. While prior studies have predominantly utilized the symmetric cointegration approach, the current study posits that the limited assumption of symmetry between the exchange rate and the trade flows could have hindered the empirical findings. The research investigates both the symmetric and asymmetric effects of exchange rate fluctuations on 102 Pakistani industries that import from Japan and 62 industries that export to Japan at the industry level from 1980 to 2020. The outcomes indicate that in nearly half of the importing and exporting industries that engage in trade with Japan, there is evidence of a significant impact of asymmetric exchange rate fluctuations on trade flows in both the short and long term. The study suggests that policymakers should consider the industry-specific impact of exchange rate fluctuations on trade flows and implement targeted policies accordingly. Particularly, industries that benefit from currency depreciation should be encouraged through export incentives, while industries negatively affected by currency volatility should be provided with hedging mechanisms and other forms of support to mitigate their losses.