Asia-Pacific Financial Markets最新文献

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.

This study investigates the relationship of fund managers’ performance persistence with (a) personal characteristics of managers and (b) fund characteristics. The study uses a sample of fund managers from India to create a comprehensive dataset of manager returns from December 2006 to March 2022. Using the four factor performance model of Carhart (1997), we investigate the persistence in manager performance across (a) managerial characteristics and (b) fund characteristics based on one month holding period returns over previous 24-months estimation period. The study indicates considerable persistence among the top decile fund managers who are male, MBA-postgraduate, undergraduate with technical qualifications, and also from top institutions. It is also evident among managers who are old, and possess long experience. We also find evidence of persistence in the performance of managers from foreign funds, Indian funds, and also for the joint venture predominantly Indian funds. This study allows investors in mutual funds to make more informed decisions. It is also useful for recruiters and policymakers who are responsible for appointing mutual fund managers and making policy recommendations in light of continuing regulatory changes. This can be considered one of the earliest studies to analyse the relationship of fund managers performance persistence with (a) personal characteristics of managers and (b) fund characteristics from the perspective of an emerging Indian economy.

Recently, interest in socially responsible investing has grown, including new investment vehicles such as environmental, social, and governance exchange-traded funds (ESG ETFs). Despite their rising popularity, few studies have attempted to examine the performance characteristics of these stylized funds. This study aimed to fill this knowledge gap by elaborating on the performance attributes of ESG ETFs and examining fund managers’ security selection and market timing skills. Our results suggest that these funds generally underperform relative to conventional ETFs in many aspects. Additionally, the market timing skills of fund managers require improvement but are comparable to those of conventional ETFs. These results are robust to selecting the individual funds and alternative indices used in the sample. Furthermore, both the security selection and market timing skills of ESG ETF managers deteriorated significantly during the COVID-19 pandemic. Finally, the results indicate a slightly weaker cointegrated relationship between ESG ETFs and their benchmark indices when compared to conventional ETFs, suggesting that potential investors in ESG ETFs should carefully inspect the funds to make informed decisions.

The influence of Covid-19 pandemic crisis on rates of return is analyzed in this paper in the light of possible data manipulation related to reporting systems provided by the administration in the USA, Turkey and Poland. The study used various methods of analyzing the relationship of a discrete, non-discrete and dichotomous data nature between the studied variables. As a result, the strongest reaction of the market was observed in Turkey followed by the USA and Poland. It can be concluded that the reaction of the surveyed markets was influenced by the data manipulations. The added value of the article is related to the use of various methods to study phenomena and detect the impact of data manipulation on the markets.

In this study, we decompose momentum indicators for the Japanese stock market into two components, high-to-price and price-to-high. High-to-price has a lower downside risk and higher Sharpe ratio than price-to-high. We find that a conventional momentum strategy combines the characteristics of high-to-price in a bull market and those of price-to-high in a bear market. In particular, the large drawdowns of momentum strategies reported in previous studies seem to be largely owed to those of price-to-high in bear markets. It is possible that the mechanism generating factor returns differs among the three strategies.

Predicting the stock market remains a difficult field because of its inherent volatility. With the development of artificial intelligence, research using deep learning for stock price prediction is increasing, but the importance of applying a prediction system consisting of preparing verified data and selecting an optimal feature set is lacking. Accordingly, this study proposes a GA optimization-based deep learning technique (CNN-LSTM) that predicts the next day's closing price based on an artificial intelligence model to more accurately predict future stock values. In this study, CNN extracts features related to stock price prediction, and LSTM reflects the long-term history process of input time series data. Basic stock price data and technical indicator data for the last 20 days prepare a data set to predict the next day's closing price, and then a CNN-LSTM hybrid model is set. In order to apply the optimal parameters of this model, GA was used in combination. The Korea Stock Index (KOSPI) data was selected for model evaluation. Experimental results showed that GA-based CNN-LSTM has higher prediction accuracy than single CNN, LSTM models, and CNN-LSTM model. This study helps investors and policy makers who want to use stock price fluctuations as more accurate predictive data using deep learning models.