Asia-Pacific Financial Markets最新文献

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.

This study proposes a method to infer the parameters of the constant elasticity of variance (CEV) model from the market values of stock after the extension from the asset process of the Merton model in the structural credit risk model to that of the CEV model. The state space model is used, which consists of an asset process (system equation) and the call option pricing a stock value (observation equation), for the inference. However, it is usually difficult to apply the Markov chain Monte Carlo (MCMC) method to estimate the parameters of the CEV model because the observation equation of the state space model has no analytical formula. Our method solves this parameter estimation problem by applying the MCMC combined with a finite difference method of partial differential equations, where the stock value obtained as a CEV option price is numerically solved. This study estimates the parameters from the real stock values of the US financial institutions as an empirical analysis. Furthermore, we analyze the default probability and measure the credit risk of bank portfolios.

This paper examines the effect of economic policy uncertainty (EPU) on the Indian capital market using the generalized autoregressive conditional heteroscedastic mixed data sampling (GARCH-MIDAS) approach. This study also disintegrates the Global EPU (GEPU) on its components using identity functions such as up, down, and composite parts dependent on the adjustment in the heading of the EPU and GEPU and tests the linkages among these parameters and the Indian securities exchange instability. Our empirical study shows that GEPU positively and significantly impacts the Indian capital market's volatility. That indicates that the Indian capital exchange volatility will also be unstable when the global economic policy uncertainty is higher. Further, based on the dynamic directions of EPU and GEPU, our results show that, in diverse situations, directional GEPU may present differently in predicting the uncertainty in the Indian capital market. This is primarily so when EPU and GEPU climb in the same period when our approach can obtain more powerful prediction precision.

In this study, we construct an optimal currency portfolio using the implied return distribution in the mean-variance approach and examine the performance through a backtest. We estimate the implied expected spot return, implied volatility, and implied correlation from currency option price data, and propose a method of constructing a fully forward-looking optimal currency portfolio without historical data. We implement the backtest from January 2006 to October 2020 on a currency portfolio comprising seven currencies (the Japanese yen, the Swiss franc, the euro, the British pound, the Australian dollar, the New Zealand dollar, and the Canadian dollar) against the US dollar and US-dollar interest rate, and examine the usefulness of the proposed method. We find that the proposed method yields a higher performance than the conventional method in previous studies that use historical data. Furthermore, it is evidenced that the main factor in the performance gap between the proposed and the conventional methods is the high predictive power of the spot return.

Certain predominant Banks astride across India stayed esoterically stable while graphing nascent opportunities in retail banking sectors to intensify their financial values. And concomitantly, Money lending facilities i.e., Consumer Loan services are one of those offerings which are supposed to meet the customer’s needs and requirements. But due to a lack of proper ‘customer education’, customers are made to purchase third-party financial products like mutual funds or insurance policies as mandatory at the time of disbursement of loans by the bank officials, which results in financial losses to the customers. The study is carried out to explore various loan facilities provided by major banks so that customers can avail of the loan facilities as per their requirements. We have studied and surveyed all consumer loan services available, and on the basis of certain parameters such as Interest Rates, Equated Monthly Installment (EMI), Processing Fee, Pre- Payment Charges, Charges for Late Payments of EMI, Cheques/ Electronic Clearing Service (ECS) Return Charges, Mandate Life Insurance Policies, etc. The study concluded that customers should be exposed to all types of terms and conditions regarding third-party financial products at the time of loan disbursement so that every customer can be protected from mis-selling third-party financial products. Therefore, this study will enhance the literature towards’ customer education’ and will spread awareness of certain terms and conditions to the customer at the time of disbursement of loans in their account.

The uncertainty caused by high volatile crude oil prices and the higher level of deregulations worldwide has significant effects on the economic growth of a country. The financial markets of many developing countries experienced a severe downturn during the oil price shocks in March-April 2020. Traditional predictive approaches, which assume linearity and stationarity of time series in the long run, fail to accurately capture short-term fluctuations. This paper presents an efficient algorithm based on ARMA denoising and taking advantage of the wavelet transformation. By decomposing the time series and extracting the intricate underlying structure, wavelet denoising minimizes distortions and enhances forecasting accuracy. The results demonstrate a substantial improvement in performance compared to conventional forecasting techniques.