Asia-Pacific Financial Markets

Kaur, Sukhmani; Aggarwal, Shalini; Arora, Vikas

doi: 10.1007/s10690-024-09455-wpmid: N/A

This document investigates the potential for international portfolio diversification between G7 stock markets and the BRICS counties, that is, Brazil, Russia, India, China, and South Africa. The authors propose a theoretical model that suggests risk-averse investors would seek diversification internationally. The study examines the long-term causality and short run causality between the stock market indices of G7 countries and the stock markets of each BRICS nation. Through unit root tests, the authors check the stationarity of the series. The study also employs the Johansen cointegration tests to examine the cointegration between the variables. Additionally, VECM is employed to assess the long-run causality and Wald test is used to understand short-run causality of the stock market indices. The results indicate a mixed response, revealing both short and long-run associations between the stock market indices of Brazil and Russia with the G7 stock market. The document provides valuable insights into the co-movement of G7 and BRICS stock markets, highlighting the potential for diversification benefits and identifying specific countries with stronger correlations. Policy-makers and capital market regulators can use the findings to develop robust policy frameworks and regulatory mechanisms to prevent potential stock market crashes and systemic failures.

Mandal, Satrajit; Bhattacharya, Sujoy

doi: 10.1007/s10690-024-09456-9pmid: N/A

This paper proposes a fuzzy jump-diffusion (FJD) option pricing model based on Merton (J Financ Econ 3:125–144, 1976) normal jump-diffusion price dynamics. The logarithm of the stock price is assumed to be a Gaussian fuzzy number and the risk-free interest rate, diffusion, and jump parameters of the Merton model are assumed to be triangular fuzzy numbers to model the impreciseness which occurs due to the variation in financial markets. Using these assumptions, a fuzzy formula for the European call option price has been proposed. Given any value of the option price, its belief degree is obtained by using the bisection search algorithm. Our FJD model is an extension of Xu et al. (Insur Math Econ 44:337–344, 2009) fuzzy normal jump-diffusion model and has been tested on NIFTY 50 and Nikkei 225 indices options. The fuzzy call option prices are defuzzified and it has been found that our FJD model outperforms Wu et al. (Comput Oper Res 31:069–1081, 2004) fuzzy Black-Scholes model for in-the-money (ITM) and near-the-money (NTM) options as well as outperforms Xu et al. (Insur Math Econ 44:337– 344, 2009) model for both ITM and out-of-the-money (OTM) options.

Samarakoon, S. M. R. K.; Pradhan, Rudra P.; Gunathunga, I. K. D.; Tripathy, Sasikanta

doi: 10.1007/s10690-024-09457-8pmid: N/A

This study delves into the ramifications of financial derivatives usage on the firm value among Indian non-financial firms, covering both the COVID-19 crisis period (2020–2021) and the preceding stable phase (2015–2019). This comparative analysis aims to discern how the strategic use of derivatives influences firm valuation across varying economic conditions. Analyzing 712 firm-year observations during the pandemic and extending to 1735 observations in the pre-COVID era, our findings reveal that while foreign exchange and interest rate derivatives consistently enhance firm value, the use of commodity derivatives exhibits a complex relationship with firm value, becoming notably negative during the pandemic. This suggests that derivatives’ effectiveness in risk management and value preservation is contingent upon both the type of derivative and the economic context. Our research underscores the critical role of derivatives in navigating financial uncertainties, offering nuanced insights that enrich our understanding of firm-level risk management strategies in both stable and turbulent times.

Sarma, Nilotpal; Tiwari, Priyanshu; Rajib, Prabina

doi: 10.1007/s10690-024-09458-7pmid: N/A

The primary purpose of this paper is to analyze the connectedness between edible oils and oilseeds from various international commodity markets and the U.S. Economic Policy Uncertainty Index (EPU). The TVP-VAR method has been adopted in this paper to analyze the inter-commodity connectedness and spillover relationships among them. We also study how the effect on the price of one edible oil or oilseed affects other edible oils in the international markets. For this purpose, daily closing prices of near-month contracts of 6 edible oil commodities and the Economic Policy Uncertainty (EPU) index have been considered for a period that starts from January 2013 to April 2023. Results show a moderate level of connectedness among the edible oil and oilseed commodities; however, connectedness increases during times of economic or geopolitical crisis. Results also show that soybean is the most dominant commodity in the edible oil and oilseed commodity nexus, and rapeseed meal is the commodity with the lowest transmission power.

Sehgal, Shivam; Singh, Jaspal

doi: 10.1007/s10690-024-09459-6pmid: N/A

The article aims to empirically examine the determinants of the 10-, 7-, and 5-year Indian government bond yields over the study period of 1999–2021. For this purpose, three equations were modeled using various independent variables to account for relevant global and domestic drivers. The results were estimated using the ARDL model to identify the long and short-run determinants of the bond yields. The findings demonstrate differences between domestic and global factors' long- and short-term effects across various bond maturities. The long-run drivers of Indian government bond yields include short-term interest rates, economic policy uncertainty, foreign exchange reserves, GDP growth rate, VIX, and oil prices. However, in the short run, all the domestic and global variables affected the bond yields, including external debt, inflation, and general government debt, which did not impact the yields in the long run. These findings have substantial policy implications for the central bank and government in formulating appropriate monetary and fiscal policy mixes while considering global risk scenarios and also for the international and domestic investors for better portfolio allocation.

Jonwall, Renu; Gupta, Seema; Pahuja, Shuchi

doi: 10.1007/s10690-024-09460-zpmid: N/A

This study aimed at differentiating the qualitative characteristics (Basic and Technical) of the Indian Socially Responsible (SR) funds. The study also compared the performance of SR funds with their benchmark indexes. The novelty of the current study is analyzing the impact of the market return and the Covid-19 outbreak on the returns of SR funds. The study used content analysis, independent t-test, and multiple linear regression analysis. The content analysis results highlighted that the majority of the SR funds adopt the Environmental, Social and Governance (ESG) integration approach, invest in large-cap, high-growth companies with good ESG score, and have an investment committee. The comparative analysis indicated that out of 14 SR funds, only four funds outperformed their benchmark index. The regression analysis showed that the selected four funds had a significant relationship with their respective benchmarks and a non-significant relationship with the Covid-19 outbreak. The current study contributes to SRI literature by identifying the differentiating characteristics of the Indian SR funds. It also contributes to the extant literature a comparative analysis, assessing the performance of the SR funds with their benchmark index. Further, determining the impact of the market return and the Covid-19 outbreak on the returns of SR fund is also a contributing factor of the present study. Findings are useful for individual investors, institutional investors and fund managers, as they can launch more SR funds on similar terms. Findings are useful for regulators and policymakers for framing new rules and regulations for boosting ESG adoption by the companies.

Si, Yuancheng; Nadarajah, Saralees

doi: 10.1007/s10690-024-09461-ypmid: N/A

In this study, we employ statistical analysis, hypothesis testing, and regression models to investigate the characteristics of opening price gap rates and price gaps in the stock market indices of Mainland China, utilizing historical data. To clarify, while both ’opening price gap rate’ and ’price gaps’ are central to our analysis, they represent distinct concepts. The opening price gap rate refers to the rate at which a stock’s opening price differs from its previous closing price, indicating initial market sentiment and potential momentum for the trading day. In contrast, price gaps, as defined in technical analysis, are specific chart patterns formed by two adjacent candlesticks on consecutive trading days. These patterns are characterized either by one candlestick’s low being higher than the following day’s high, or one candlestick’s high being lower than the following day’s low, creating a "blank" area on the price chart. This signifies a price range with no trading activity and is a crucial indicator of market sentiment and potential directional moves. Our study tested and validated thirteen related hypotheses. The findings reveal a significant correlation between the directionality of price gaps and the fluctuations in opening price gap rates, highlighting key characteristics of the market. Notably, price gaps significantly impact daily changes in trading volume and turnover. Furthermore, we validated the efficacy of the opening price gap rate as a stock-picking factor through back-testing. This research offers a new perspective for understanding stock market behaviors and has considerable implications for investment decisions and market analysis.

Khan, Anam; Verma, Renu; Yadav, Miklesh Prasad; Narain,

doi: 10.1007/s10690-024-09462-xpmid: N/A

BRICS nations are playing a critical role in the global economic setting, but to maintain sustained economic growth they are required to make relentless efforts towards certain challenges. These challenges pertain to diverse governance areas including political, socio-economic, and legal conditions. This paper unfolds the impact of the level of governance quality indicators on stock market development for BRICS nations during the period from 2007 to 2021. Using panel data regression, our empirical findings confirm that governance indicators are critical for the development of the stock market. Our results show that governance indicators such as Government Effectiveness, Rule of Law, and Voice and Accountability are significant variables affecting the stock market development. We find that giving citizens more autonomy to participate in the formulation and execution of policies, improves the development of stock markets. Similarly, lesser political influence will also lead to better growth of the stock market. Additionally, the study evidence that a stronger legal environment in BRICS nations promotes lesser corrupt practices such as insider trading, but at the same time hinders the growth of the stock market. Policymakers in BRICS nations should follow a consistent policy to improve their governance indicators which are now becoming essential for stock market development.

Friday, Ibanga Kpereobong; Pati, Sarada Prasanna; Mishra, Debahuti; Mallick, Pradeep Kumar; Kumar, Sachin

doi: 10.1007/s10690-024-09463-wpmid: N/A

Accurately predicting market direction is crucial for informed trading decisions to buy or sell stocks. This study proposes a deep learning based hybrid approach combining convolutional neural network (CNN), attention mechanism (AM), and gated recurrent unit (GRU) to predict short-term market trends (1 day, 3 days, 7 days, 10 days) across different stock indices (BSE, HSI, IXIC, NIFTY, N225, SSE). The architecture dynamically weights the input sequence with the AM model, captures local patterns through CNN and effectively models long-term dependencies with GRU thus aiming to accurately classify either "buy" or "sell" positions of stocks. The model is assessed using classification and financial evaluation metrics involving accuracy, precision, recall, f1-score, annualized returns, maximum drawdown, and return on investment. It outperforms benchmark models, and different technical indicators including average directional index, rate of change, moving average convergence divergence, and the buy-and-hold strategy, demonstrating its effectiveness in various market conditions. The proposed model achieves an average accuracy of 98% in predicting the 1 day-ahead direction, and an average accuracy of 88.53% across all prediction intervals. The model was also validated using the wilcoxon signed rank test that further supported its significance over the benchmark models. The CAG model presents a comprehensive and intuitive approach to stock market trend prediction, with potential applications in real-world asset decision-making.

Islam, Khalid Ul; Lone, Umer Mushtaq; Gulam, Younis Ahmed; Bhat, Suhail Ahmad

doi: 10.1007/s10690-024-09464-9pmid: N/A

This study empirically examines price discovery and volatility spillover between the spot and futures markets for India using both daily and intraday data of Nifty50 and its associated futures index. Within the Johansen cointegration framework, the study for the first time used the recursive cointegration method for examining the dynamics of the long-run relationship between the equity spot and futures markets. To analyze the volatility spillovers between the two markets the study employs BEKK–GARCH model. This model ensures the positive definiteness of the conditional covariance matrix and estimates the same with less number of parameters as compared to the traditional multivariate GARCH models including the VECH model. The empirical results show that there is a stable long-run relationship between the two markets. The Granger causality findings support the notion that the futures market plays a dominant role in causal relationships. There is also a two-way volatility spillover between the two markets. However, it is relatively seen that the futures market has strong transmission effects which are carried over to the spot market. This is intuitive because the futures market is more sensitive to new information than its counterpart due to differences in cost and liquidity. The results based on the latest data, offer a new perspective on the lead–lag relationship between India’s stock market futures prices and spot prices. These findings can benefit stock market stakeholders by protecting themselves from uncertainty and developing futures contracts that will increase the efficiency of the Indian equity market.