Asia-Pacific Financial Markets最新文献

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.

To find out how carbon performance and disclosure practices affects corporate’s financial performance, this study investigates its non-linear influence on financial performance by considering non-financial 100 businesses that are listed on National Stock Exchange 200 Index in India for the consecutive 12 years, i.e., from 2010 to 2021. It employs two indicators of environmental-related information like carbon performance which is measured in terms of greenhouse gas reduction, and disclosure practices which is measured in terms of environmental disclosure score. It uses the dynamic panel data regression analysis technique to estimate the parameters. The empirical outcomes show an obvious non-linear impact of carbon performance on corporate financial performance, which is proxied by Tobin’s Q. This indicates that at the initial stage, carbon performance decreases financial performance, but later on, further increase in the carbon performance is found to improve corporates financial performance in the long duration. But, in case of disclosure practices it shows no effect on market-based economic performance, i.e., Tobin’s Q. Therefore, the study recommends the investors to be courageous and patience because carbon performance will decrease financial performance at the lower level, but can give benefits in the long run. This paper also suggests the regulators to incorporate environmental standards, and introduce severe forfeits for ecological wrongdoers with the aim of enhancing companies’ environmental disclosure activities.

The study probes comparative and additional value relevance of total comprehensive income and other comprehensive income, respectively, in the Indian context after adopting IFRS-converged accounting standards. The study sample comprises of 367 Indian non-financial companies. The period of study is F.Y. 2016–2017 to F.Y. 2019–2020. Results reveal both net profit and total comprehensive are value relevant, but the supremacy of neither of the two could be established based on our findings. Furthermore, other comprehensive income comes out to be additionally value relevant. The study's findings determine the decision usefulness of OCI and thus, provide useful insights to standard setting bodies.

This study develops a novel periodic regime-switching model (the PRS model) to improve the forecasting of stock market volatility by accounting for the information from non-trading and trading periods, including regular trading and after-hour trading. Empirical analysis of the Taiwan Futures Exchange (TAIFEX) demonstrates the significant improvements of the PRS model in both in-sample and out-of-sample periods. Our results also show that the introduction of after-hour trading sessions has provided valuable information for volatility forecasting in subsequent regular trading sessions, emphasizing the importance of considering diverse information flows across different trading and non-trading times. The PRS model effectively captures the dynamics of non-trading and trading sessions and the influence of unusual news arrivals and jumps on market volatility, contributing to investment and risk management strategies.

This paper examines the trading and non-trading returns to diagnose the impact of market microstructure changes on market quality. The daily data of ten agricultural commodities traded on the National Commodity and Derivative Exchange (NCDEX) were used for the study. The data has been divided into three categories: year-wise, pre- and post-reform, pre-ban, and post-ban period. The study employs variance ratio analysis, and the results suggest high daytime and opening variances. A first-order autocorrelation detects the return predictability in the data series. A Value at Risk (VaR) and Expected Shortfall (ES) methods were employed to get more detail about the downside risk of the series. It suggested that daytime return has more risk compared to overnight return. Overall, this study suggests that market microstructure effects are visible in the Indian agricultural commodity market and hardly observe any improvement in the market quality. Since we reveal the impact of policy changes on market quality, the results will be useful for policymakers.

This study examines the empirical link between remittance inflows and India's economic growth, particularly emphasizing the association's asymmetries as the prior studies were neglected. Therefore, it formulated a growth function that assesses the non-linear influence of the remittance inflows on economic growth by endogenizing the gross fixed capital formation, official exchange rate, and export growth. Relying on the annual time series data for India, the work uses the Non-Linear Auto-Regressive Distribution Lag (NARDL) model to expose the non-linear influence of the remittance inflows on economic growth by controlling the gross fixed capital formation, official exchange rate, and export growth for the period ranges from 1975 to 2021. The outcomes show the presence of the long-run relationship among the variables vector. Further, the results indicate an asymmetric impact of remittance inflows on economic growth both in the long run and short run. Moreover, the findings reveal a rise in the remittance inflows leads to an increase in economic growth, whereas a fall in the remittance inflows ends up in a reduction of economic growth. Additionally, the outcomes show a negative and significant impact of gross fixed capital formation and official exchange rate on economic growth in the long run. It also observed an insignificant negative influence of export growth on the specified growth model.