Asia-Pacific Financial Markets最新文献

{"title":"From Mean to Machine Learning: Evolution and Econometric Synthesis Review of Granger Causality Methods","authors":"Zhihui Lv,&nbsp;Xin He,&nbsp;Wing-Keung Wong","doi":"10.1007/s10690-026-09635-w","DOIUrl":"10.1007/s10690-026-09635-w","url":null,"abstract":"<div><p>Granger causality remains a central framework for studying predictive causal relations in economics and finance, but its methodological scope now extends far beyond classical linear vector autoregression. Existing surveys often examine linear tests, nonlinear tests, quantile causality, distributional causality, and machine-learning-based causal discovery as separate methodological strands. This review provides an econometric synthesis that connects these strands under a common predictive-causality logic. It argues that the evolution of Granger causality can be understood through three connected dimensions: the statistical object being predicted, the dependence structure being represented, and the algorithm used for estimation and inference. Based on this logic, the review develops a distributional hierarchy from conditional means to variances, quantiles, conditional densities, full distributions, and high-dimensional machine-learning settings. It further proposes a distribution, structure, and algorithm framework for comparing traditional econometric tests, nonlinear and distributional methods, sparse high-dimensional models, graph-learning approaches, and neural-network-based Granger causality. A bibliometric analysis of 2,170 Web of Science records from 2010 to 2025 complements the methodological review by identifying source-journal patterns, country-level contributions, co-citation clusters, keyword evolution, and recent shifts toward nonlinear, network-based, and data-driven causal analysis. The review also provides a method-software mapping that links empirical objectives to implementation tools in Stata, EViews, MATLAB, R, and Python. A central conclusion is that machine-learning approaches expand the scope of Granger-causality analysis, but their value depends on formal validation, robustness to structural breaks, post-selection assessment, and interpretable causal attribution. The paper contributes a critical and practical framework for selecting, implementing, and interpreting Granger-causality methods in modern macroeconomic and financial research.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1445 - 1485"},"PeriodicalIF":2.3,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human Capital Based Six-Factor Asset Pricing Model in the Era of Covid-19","authors":"Wing-Keung Wong,&nbsp;Riffat Mughal,&nbsp;Mustafa Afeef,&nbsp;Naveed Khan,&nbsp;Hassan Zada","doi":"10.1007/s10690-025-09579-7","DOIUrl":"10.1007/s10690-025-09579-7","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, the COVID-19 pandemic has heightened trade tensions and geopolitical risks, affecting stock market volatility and the factors that determine the pricing of financial securities. This study examines the role of human capital in the Fama-French (2015) five-factor model to explain excess portfolio returns. For this purpose, we collected daily stock prices data for 73 non-financial firms listed on the Pakistan Stock Exchange from July 2018 to June 2023. For estimation, we employed two models: first, the Fama-French time-series regression, second, the Fama–Macbeth (1973) rolling-window two-pass regression. We find that the six-factor model significantly explains time-series variations in excess portfolio returns. Notably, the human capital premium is significantly and negatively related to excess portfolio returns. However, a significant and positive coefficient for the COVID-19 dummy and a significant and negative coefficient for the interaction between human capital and COVID-19 across several portfolios indicate that, on average, during COVID-19, excess portfolio returns have improved. In contrast, increased expenditures on human capital are negatively related to excess portfolio returns. The results have meaningful implications for investors, portfolio managers, and policymakers. It is suggested that the human capital factor be incorporated into asset pricing. Further, it emphasizes developing strategies to maintain investor confidence during crises like the COVID-19 pandemic.</p>\u0000 </div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 1","pages":"25 - 63"},"PeriodicalIF":2.6,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147335895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Feature Importance of Quantile Connectedness among Islamic Sectoral Indices and Meme Assets","authors":"Adnan Aslam,&nbsp;Rayenda Khresna Brahmana,&nbsp;Xiu Wei Yeap","doi":"10.1007/s10690-025-09558-y","DOIUrl":"10.1007/s10690-025-09558-y","url":null,"abstract":"<div><p>This study investigates the feature importance of return and volatility connectedness between Islamic sectoral indices and meme assets across quantiles. Using daily data from August 2020 to March 2024, we adopt a multistage approach combining the quantile extended joint connectedness framework, robust OLS regression, and tree-based machine learning of feature importance. The findings reveal that Islamic indices act as net return transmitters in bearish and bullish markets, while meme assets are net receivers; both are disconnected in normal conditions. In volatility spillovers, Islamic indices lead during bearish and normal markets, whereas meme assets dominate during bullish periods. Feature importance analysis shows that the global financial stress index is the most influential driver of return spillovers, while cyber-attacks emerge as the primary determinant of volatility spillovers. These findings highlight that spillover dynamics, and thus portfolio diversification benefits, vary significantly across market states and should be incorporated into risk-sensitive asset allocation strategies.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1811 - 1856"},"PeriodicalIF":2.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10690-025-09558-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Modeling Electricity Prices with Stochastic Langevin Equations","authors":"Markus Hess","doi":"10.1007/s10690-025-09573-z","DOIUrl":"10.1007/s10690-025-09573-z","url":null,"abstract":"","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 1","pages":"347 - 348"},"PeriodicalIF":2.6,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10690-025-09573-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of an Asymptotic Expansion Method to Constrained Optimal Portfolio Problems Using Deep Learning","authors":"Makoto Naito,&nbsp;Kohta Takehara","doi":"10.1007/s10690-025-09555-1","DOIUrl":"10.1007/s10690-025-09555-1","url":null,"abstract":"<div><p>Unless utility functions are logarithmic or posed problems are single-period, it is very difficult to explicitly solve constrained optimal portfolio problems. This paper proposes a new numerical method for solving the multiple-period constrained optimal portfolio problems. This method combines an asymptotic expansion method applied to the optimal portfolio problem in complete markets by Takahashi and Yoshida (2004. <i>Statistical Inference for Stochastic Processes</i>, <i>7</i>(2), 153–188), a technique of reformulating the multiple-period constrained optimal portfolio problem into a corresponding backward stochastic differential equation by Hu et al. (2005. <i>The Annals of Applied Probability</i>, <i>15</i>(3), 1691–1712), and a method for solving the backward stochastic differential equation using machine learning by E et al. (2017. <i>Communications in Mathematics and Statistics</i>, <i>5</i>(4), 349–380) and Han et al. (2018. <i>Proceedings of the National Academy of Sciences</i>, <i>115</i>(34), 8505–8510). It is essential to decompose the backward stochastic differential equation into parts involving with the unconstrained optimal portfolio problems and the remainder. Numerical simulations suggest that the proposed method may outperform conventional methods such as E et al. (2017. <i>Communications in Mathematics and Statistics</i>, <i>5</i>(4), 349–380) and Han et al. (2018. <i>Proceedings of the National Academy of Sciences</i>, <i>115</i>(34), 8505–8510) in estimating the multiple-period constrained optimal portfolios.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1749 - 1780"},"PeriodicalIF":2.3,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10690-025-09555-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Conditional Correlation Models with Time-Varying Parameters Incorporating for Realized Covariance Matrices","authors":"Hideto Shigemoto,&nbsp;Takayuki Morimoto","doi":"10.1007/s10690-025-09552-4","DOIUrl":"10.1007/s10690-025-09552-4","url":null,"abstract":"<div><p>We propose novel realized dynamic conditional correlation (Realized DCC) models with measurement errors to forecast the covariance of asset returns. This study aims to forecast the conditional covariance matrix of asset returns and to ensure the bound, <span>(rho in [-1,1])</span>, of the forecasted correlation matrix by incorporating the measurement error in estimating the conditional correlation and using the BEKK and HAR models to estimate the conditional correlation matrix using the Fisher z transformation. The proposed models in this study can include measurement error into not only variance estimations but also correlation estimations. These models can keep the persistence of volatility and correlation at high levels when the asymptotic variances of realized volatility and realized correlation are small. By incorporating measurement errors, models can decrease the persistence when the asymptotic variances are large. Our empirical results show that our models which incorporate the measurement error into the correlation estimation make more accurate forecasts than other models which do not incorporate it into the correlation estimation.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1647 - 1677"},"PeriodicalIF":2.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Realized Volatility Moments Implied by Options with Applications to the Pricing of Realized Volatility Options","authors":"Frido Rolloos,&nbsp;Kenichiro Shiraya","doi":"10.1007/s10690-025-09556-0","DOIUrl":"10.1007/s10690-025-09556-0","url":null,"abstract":"<div><p>For small values of correlation a method is given to de-correlate the instantaneous volatility from the price process in stochastic volatility models. The result of the de-correlation is that the implied volatility skew is rotated into a smile. Once the implied volatility skew has been “symmetrized\", moments of realized volatility are implied from the symmetrized asset option prices. The implied moments are subsequently used in a Gram-Charlier expansion of the density of realized volatility. The Gram-Charlier density provides approximate prices for options on realized volatility and other volatility derivatives.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1781 - 1794"},"PeriodicalIF":2.3,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainties and Tail-dependency Risk Spillovers within the Green Bond Markets","authors":"Lei Zhou,&nbsp;Jun Cui","doi":"10.1007/s10690-025-09557-z","DOIUrl":"10.1007/s10690-025-09557-z","url":null,"abstract":"<div><p>In this study, we first examine the tail-dependency of green bonds in the U.S. and China using the time-varying <span>(Delta CoVaR)</span> approach over the period from January 3, 2017, to November 15, 2024. We then employ the <span>(R^2)</span> decomposed connectedness model to capture the dynamic spillovers mechanism. The results indicate that the spillovers are time-varying and event-driven, with China as a net transmitter and the U.S. as a net receiver. Lagged dynamics predominantly drive the net transmission of risks across the network, while contemporaneous measures strongly influence overall connectedness. These findings provide insights for risk management and policymaking.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1795 - 1809"},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study on Forecasting of Crude Oil Prices Employing Optimized Deep Learning Models and LUBE Method","authors":"Gollu Yaswanth,&nbsp;Hanumanthu Lohith,&nbsp;Mamuduri Jerusha,&nbsp;Sibarama Panigrahi","doi":"10.1007/s10690-025-09554-2","DOIUrl":"10.1007/s10690-025-09554-2","url":null,"abstract":"<div><p>Crude oil is an essential commodity for the entire world, and price changes have a big impact on the economy. Price volatility-related risks can be reduced for stakeholders using probabilistic crude oil forecasting. In this work, optimized deep learning (DL) models, namely convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and bidirectional LSTM (BiLSTM) are used to generate prediction intervals (PI) for crude oil prices (COP) using the lower upper bound estimation (LUBE) method. Using a proprietary loss function, the LUBE method optimizes the trade-off between prediction interval normalized average width (PINAW) and prediction interval coverage probability (PICP). To evaluate the true potential of optimized DL models in probabilistic forecasting of COP using the LUBE method, monthly, weekly, and daily COP are considered, and probabilistic forecasts are obtained at different confidence levels. Four probabilistic forecast accuracy measures, namely, PICP, PINAW, average coverage error (ACE), and accumulated width deviation (AWD), are used to answer five research questions relating to COP forecasting. Additionally, the Friedman and Nemenyi hypothesis test is used to draw reliable conclusions. Simulation results suggest the statistical superiority of the optimized LSTM model in reliable probabilistic forecasting of daily, weekly, and monthly COP using the LUBE method. The optimized LSTM model with the LUBE method provided almost 100% in PICP and the lowest PINAW than all other methods considered in this study in the probabilistic forecasting of monthly, weekly, and daily COP.</p></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1713 - 1748"},"PeriodicalIF":2.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Insights on Inclusive Growth Determinants in Pakistan: Examining the Role of Remittances and Renewable Energy in Achieving SDG-8","authors":"Ijaz Uddin,&nbsp;Muhammad Azam Khan,&nbsp;Muhammad Tariq","doi":"10.1007/s10690-025-09548-0","DOIUrl":"10.1007/s10690-025-09548-0","url":null,"abstract":"<div><p>Inclusive growth is closely linked to Sustainable Development Goal (SDG-8), which aims to achieve sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. Inclusive growth focuses on reducing inequalities and promoting human well-being by ensuring equal access to economic opportunities and the benefits of growth. This study investigates the factors influencing inclusive growth and the role of remittances (RM) and renewable energy in inclusive growth for Pakistan from 1991 to 2023. Machine learning models, namely Gradient Boosting and Random Forest, and Regression Algorithms (Ridge, Lasso), have been employed. In addition, we applied the fully modified ordinary least squares (FMOLS) and the dynamic OLS (DOLS) as analytical techniques for analysis. The empirical results of the machine learning models highlight the significant impact of remittances on inclusive growth, followed by carbon emissions, life expectancy, renewable energy, and inflation. The estimates of Ridge, Lasso, FMOLS, and DOLS show that remittances, renewable energy, and life expectancy have a positive effect on inclusive growth, while carbon emissions and inflation have a negative impact in Pakistan. The study provides valuable policy implications to achieve inclusive and sustainable economic growth in line with UN sustainable development goals.</p><h3>Graphical Abstract</h3>\u0000<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":54095,"journal":{"name":"Asia-Pacific Financial Markets","volume":"33 4","pages":"1527 - 1558"},"PeriodicalIF":2.3,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149163020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}