Asia-Pacific Financial Markets最新文献

The interactions among equity and commodity market prices and their volatility provide valuable information to market participants. This paper explores such dynamic interrelations in India, especially whether relationships have significantly changed with the onset of the COVID-19 pandemic and the Russia-Ukraine war of 2022. Based on a daily dataset from January 2017 to May 2022, VAR-MGARCH models and dynamic correlations are estimated with prices of gold, equity, and crude oil for spot and futures markets. Findings suggest that for gold, crude oil, and equity in spot and futures segments, there is evidence of significant persistence of volatility and spillover from past shocks. In general, volatility spillover is more pronounced in the spot than in the futures market. Evidence also indicates bi-directional spillovers between markets, but it is more prominent from the equity market to the crude oil and from crude oil to the gold market. However, the most notable finding of the study is that, like the period of the global financial crisis, the dynamic correlation between stock and crude oil markets has substantially increased during the COVID and war periods both in spot and futures markets. Also, during COVID, the property of gold acting as a hedge against stock has weakened.

Our study presents a method to dissect bond excess returns into components influenced by credit spreads and credit losses. Analyzing data spanning 48 years, we find that companies with higher accrual quality experience greater shocks from credit spreads and lesser shocks from credit losses. Conversely, firms with lower accrual quality face reduced credit spread shocks but heightened credit loss shocks. This indicates that high accrual quality firms benefit more from credit spread shocks, while those with lower accrual quality profit more from credit loss shocks. Notably, excluding credit spread shocks, future realized returns have a negative correlation with accrual quality. These accrual quality premiums are significant both statistically and economically, especially when credit spread shocks are not considered. Additionally, accrual quality has improved over the past 48 years due to enhanced accounting standards. Our findings reveal the importance of a reliable accrual quality metric and underscore the need to factor in credit spread shocks in asset pricing evaluations.

The present study aimed to investigate whether terrorism hampered foreign greenfield investment inflows in 14 MENA countries from 2011 to 2021. One-step system generalized method of moments, the instrumental variable of a two-stage least squares regression estimator, and instrumental variables of generalized method of moments were used in this study for more robustness. The findings showed a significant negative relationship existed between terrorism and foreign greenfield investment inflows. Meanwhile, a significant positive relationship exists between the interaction variable that captured the joint effect of terrorism and military expenditures on the foreign greenfield investment inflows. To increase the reliability of the results, the main model was extended with control variables; significant positive relationships between adjusted net national income per capita, the consumer price index, and the GDP growth rate and foreign greenfield investment inflows were identified. Meanwhile, significant negative relationships existed between military expenditure, trade openness, and foreign greenfield investment inflows. The findings showed that foreign investors were reluctant to invest in MENA countries affected by terrorism and reduced the amount of their investments. Furthermore, the results indicated that terrorism renders foreign investors attractive in host countries and negatively impacts foreign greenfield investment projects, trade openness, and military expenditure. To attract foreign investors, policymakers should focus on developing a stable macroeconomic environment and anti-terrorism measures to improve security, which will ensure sustainable economic growth.

Liquidity risk poses a distinctive and multifaceted challenge in the financial arena owing to its underlying multiple dimensions. The long-term 10-year bonds exhibit high trading activity, as evidenced by the trading frequency dimension, while the trading cost dimension and existing literature support the view that short-term bonds tend to be more liquid. In this study, the objective is to address this intricacy and explore the potential commonality across various liquidity dimensions. This is done by constructing an index of liquidity risk that stands independently from these dimensions. The liquidity risk index is formed by combining the major dimensions of liquidity: price impact, trading cost, and trading frequency, resulting in a single measure of liquidity risk. Using the first principal component extraction method, the illiquidity index is studied in a sample of six emerging Asian countries. The findings indicate that the principal component (PCA) index effectively measures aggregate liquidity risk. On the pricing dynamics, it is seen that that the PCA index is significantly affecting the yield spread of bonds with a maturity of 1-year and greater. For the 3-month and 6-month bonds, the illiquidity index fails to produce any significant impact. The study thus highlights that long and medium-term investors in bonds are more concerned with liquidity risk compared to short-term investors.

This study investigates the spillover effects between oil and stock prices from 2000 to 2022, utilizing the multivariate EGARCH model. The database includes three periods—the entire sample, the pre-pandemic era, and COVID-19. The analysis unveils insights into the dynamics of spillover effects. Findings reveal an asymmetry in spillover effects, with a prevailing negative impact trend from oil to stocks, notably affecting the Thai index negatively while positively impacting the Indonesian market. Considering the entire time frame, results address the dynamic spillover effects of oil on eight stock indices across 11 countries under analysis. Meanwhile, in the absence of a pandemic, there are only mutual relationships between oil and stock markets in five stock markets. During COVID-19, we witnessed an intensified spillover effect from oil prices to stocks, with only the Vietnamese stock market remaining unaffected. Notably, the overall spillover level peaked at 55% in 2018, decreasing to over 45% during the COVID-19 pandemic, indicating a close relationship between oil and stocks. Additional results confirm the stationarity of return data series and support the application of the multivariate EGARCH model, enhancing the study’s robustness and contributing to understanding the intricate dynamics of financial markets.

This work investigates the nexus between Covid-19 induced investor sentiment and daily futures volatility for six commodities listed in New York Mercantile Exchange, using the Google search volume index. Further, this work also examines the conditional volatility spillovers among commodity futures and investor sentiment during Covid-19. The evidence from nexus analysis suggest that Covid-19 has adversely affected the emotions of market participants leading to excess volatility in the commodity futures market. Among the six selected commodities, the nexus between negative sentiment of market participants and gasoline futures volatility was found to be more pronounced. Further, it was also observed that exchange rate volatility increases commodity futures volatility for gasoline, gold, natural gas and silver. The empirical analysis of conditional volatility spillovers among commodity futures and investor sentiment exhibited cyclical trends in volatility transmission suggesting Covid-19 induced economic and financial shock leads to abrupt fluctuations in the commodity futures market. The study also observed that 43.2% of total forecast error variance in futures volatility was due to contagion from Covid-19.

This study examines the effects of Capital Expenditure (CAPEX) and Research & Development Expenditure (R&D), on firm value, as determined by Economic Value Added (EVA). The study covers 982 Indian-listed firms from the manufacturing and service industries. The results have been estimated using fixed effects, and random effects models for the accuracy of the estimations. The findings of this study reveal varied results in the short and long run for both manufacturing and service firms. The manufacturing companies have a negligible short-term impact of CAPEX on firm value (investment year), but a strong and positive link develops over an extended period (Years 1 to 3 post-investment). On the other hand, R&D in manufacturing companies has no significant short- or long-term effect. There is no significant impact of CAPEX in service firms in the short run, R&D initially has a negative impact on EVA, but with time, CAPEX and R&D favorably impact EVA. The results of this study have implications for both managers and investors. Creating long-term value for stakeholders is every manager's job. Since the idea of the distinction between the cost of capital and the return on capital invested (ROIC) first emerged, the concept of value creation has endured. We show how excess revenue over cost of capital results in value creation in investment spending choices by using the EVA metrics and how It may be necessary for investors to consider the greater strategic advantages that come from R&D and CAPEX, especially for those who have a long-term perspective.

The paper empirically investigates the validity of Purchasing Power Parity (PPP) hypothesis for India with its 20 major trading partners using the Fourier non-linear quantile unit root (FNQKS) test. The study uses daily data for the period 1st January 2020–2nd February 2022. FNQKS test supports PPP in 15 out of 20 trading partners of India. The validity of PPP indicates that it is impossible to obtain unbounded gains from arbitrage in these trading partners because of the adjustment process even in the presence of heavy-tailed distributions, mean breaks, and non-linearity. It also indicates that the impact of shocks on the exchange rates is transitory. Therefore, no interventions in these foreign exchange markets need to be made by the relevant authorities. Thus, non-normal distributions, structural breaks, and non-linear mean reversion appear to be key features for adjustment process of exchange rates of these 15 trading partners.

This paper aims to examine the liquidity connectedness between major asset classes, including cryptocurrencies, oil, gold, stocks, and bonds, over the period from September 2014 to November 2022. Results from the time-varying parameter vector autoregression (TVP-VAR) show that the liquidity connectedness between the examined asset classes is generally low, with Bitcoin being the main transmitter of liquidity shocks while oil and bonds act as net receivers. Next, we employ the biwavelet analysis to investigate the co-movement between the liquidity connectedness index (TCI) and various uncertainty factors. Our findings suggest a weak correlation between the TCI and uncertainty factors, and especially no significant correlation between the TCI and geopolitical risk. However, some notable correlation still appears during the 2014–2015 and 2018–2021 periods. During the former period, the TCI plays the leading role, whereas during the latter period it is affected by various risk factors.

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.