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Asian Journal of Applied Financial Econometrics Asian Journal of Applied Financial Econometrics
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    AIMS AND SCOPE

    Financial econometrics is a rapidly evolving field of econometric research, while Asia has become an increasingly important and dynamic region in global finance. The Asian Journal of Applied Financial Econometrics (AJAFE) advances research in this field, with particular emphasis on Asia and emerging financial markets. AJAFE aims to provide a leading scholarly platform for innovative and substantive research that strengthens the link between econometric methods and financial applications while addressing challenges related to global sustainability.

    The AJAFE publishes rigorous theoretical and empirical contributions that develop, evaluate, and apply econometric methods to address complex problems in modern financial and economic systems. Particular emphasis is given to research involving estimation, inference, prediction, and calibration in dynamic and high-dimensional settings. Contributions addressing experimental and behavioural finance through advanced econometric techniques, particularly in the context of sustainable finance, are strongly encouraged. Submissions are expected to demonstrate clear contributions to empirical knowledge, methodological innovation, or both.

    AJAFE is supported by an international editorial team comprising distinguished scholars from Asia and other regions worldwide. This broad international representation facilitates access to highly qualified reviewers and helps uphold the journal’s rigorous academic standards. The editorial team is committed to ensuring that all submissions are evaluated fairly, independently, and promptly.

    Open Access

    AJAFE operates under a Diamond Open Access model, fully funded by Ho Chi Minh University of Banking (HUB), under which authors are not required to pay any submission, article processing, or publication fees.


    SỐ MỚI NHẤT LATEST ISSUE

    Asian Journal of Applied Financial Econometrics Asian Journal of Applied Financial Econometrics

    Asian Journal of Applied Financial Econometrics
    Xem số mới nhất View current issue

    • Beyond p-values: A comparative framework for estimation-based inference using the a priori procedure and gain–probability analysis Beyond p-values: A comparative framework for estimation-based inference using the a priori procedure and gain–probability analysis

      Tonghui Wang, S.T. Boris Choy, David Trafimow, Xiangfei Chen, Ziyuan Wang

      DOI: 10.63065/ajafe.2026.1.2.001

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose - This paper compares three inferential paradigms, Null Hypothesis Significance Testing (NHST), the A Priori Procedure (APP) and Gain–Probability (G–P) analysis, and advocates a conceptual shift away from binary hypothesis testing toward inference frameworks grounded in estimation and probability.
      Design/methodology/approach - NHST, which relies on p-values and dichotomous decision rules, is contrasted with APP, which emphasizes pre-data estimation reliability, and with G–P analysis, which quantifies the probability that one outcome exceeds another and the extent to which it does. APP is derived for parameter estimation in linear regression models under skew-normal error structures, including results on the distribution of regression estimators. The G–P framework is examined for gamma-distributed variables in both independent and dependent settings. Both are supported by simulation studies and real-world applications.
      Findings - Beyond their conceptual advantages, the APP and G–P paradigms are highly applicable in practice, as shown by the simulation studies and empirical illustrations. The shift they represent aligns with broader methodological reforms that prioritize estimation, transparency and interpretability.
      Originality/value - The paper extends APP to linear regression with skew-normal errors and G–P analysis to gamma-distributed variables under independence and dependence. Together, these frameworks offer a rigorous and interpretable alternative to conventional significance testing by prioritizing estimation accuracy, probabilistic interpretation and practical relevance.

    • A tutorial on chaotic dynamics for economists A tutorial on chaotic dynamics for economists

      Hung T. Nguyen

      DOI: 10.63065/ajafe.2026.1.2.002

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose – Presenting a simplest tutorial for new comers to the field of chaotic dynamics in economics and finance. 
      Design/methodology/approach – To attract economists, the paper is “written in a way that doesn’t kill it”. 
      Findings – Elaborating in simplest terms the complex notion of chaotic dynamics. 
      Originality/value – Aiming at attracting economists to chaotic dynamical approach to economics and finance. 

    • Short-term predictions for country-level energy demand with top-down and bottom-up stacking Short-term predictions for country-level energy demand with top-down and bottom-up stacking

      Haoyang Li, Erkal Ersoy, Boulis Ibrahim, Mark E. Schaffer

      DOI: 10.63065/ajafe.2026.1.2.003

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose - This paper examines whether stacking (Wolpert, 1992) improves short-term forecasts of global oil consumption and electricity demand during periods of major shocks, including COVID-19, geopolitical conflicts, and trade disruptions. Given diverging trends between OECD and non-OECD economies, we evaluate forecast performance separately across these regions.
      Design/methodology/approach - We combine XGBoost, random forest, elastic net, multi-layer perceptron, and a unit fixed-effects model within a stacking framework using a non-negative OLS meta-learner. We implement both a top-down approach, which models aggregate demand directly, and a bottom-up approach, which models disaggregated components and aggregates predictions, to assess the value of decomposition.
      Findings - We find that stacking provides a robust benchmark, consistently matching or outperforming the best individual learner across most specifications. In the top-down setting, stacking generally delivers the strongest performance, particularly for global and non-OECD oil demand and global electricity demand. Bottom-up stacking improves accuracy for most oil sub-aggregates and performs comparably to the best base learner for electricity demand by source. Comparing strategies, bottom-up stacking performs better for electricity demand, while both approaches perform similarly for oil demand. Results by demand size suggest that retransformation bias from the log scale (Duan, 1983) and cross-country heterogeneity are particularly important for large economies. Overall, while no single model dominates across all settings, stacking provides a simple and reliable approach to improving forecast accuracy.
      Originality/value - Our contribution is threefold. First, we provide a systematic comparison of stacking methods for forecasting global energy demand across OECD and non-OECD economies. Second, we evaluate the relative performance of top-down and bottom-up strategies within a common framework. Third, we examine how forecast performance varies with the scale of energy demand, highlighting the role of retransformation bias (Duan, 1983) and cross-country heterogeneity.

    • Nonlinear associations between esg uncertainty and financial performance: Explainable machine learning evidence from listed real estate firms in Vietnam Nonlinear associations between esg uncertainty and financial performance: Explainable machine learning evidence from listed real estate firms in Vietnam

      Pham Thuy Tu, Mai Ngoc Dung, Duong Hong Thin, Tran Thi Huong Thao, Tran Huu Thuan

      DOI: 10.63065/ajafe.2026.1.2.004

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose – This study investigates how ESG-related informational uncertainty is associated with the financial performance of listed real estate firms in Vietnam. It examines whether this relationship is nonlinear and heterogeneous while evaluating the usefulness of explainable machine learning in an emerging market.
      Design/methodology/approach – A balanced monthly panel of 28 listed real estate firms covering 2013-2024 is analyzed. Eight machine-learning algorithms are compared, with Particle Swarm Optimization used for hyperparameter tuning. Model interpretability is achieved using SHAP, complemented by partial dependence plots and accumulated local effects to identify nonlinear and heterogeneous associations between ESG uncertainty and return on equity (ROE).
      Findings – XGBoost delivers the strongest out-of-sample predictive performance, indicating that the ESG uncertainty-performance relationship is inherently nonlinear. Explainable AI reveals a state-dependent pattern: ESG uncertainty is negatively associated with ROE at low levels, becomes broadly neutral as firms adapt, and is associated with more favorable outcomes only among firms with stronger adaptive capacity under high uncertainty. Large firms are more sensitive to ESG uncertainty, medium-sized firms display more stable adaptive responses, and small firms exhibit weaker responses. These findings represent model-implied associations rather than causal effects.
      Originality/value – This study shifts the ESG-performance literature from static ESG levels to ESG-related informational uncertainty and integrates explainable machine learning with SHAP to uncover nonlinear and heterogeneous effects, providing new evidence from Vietnam's listed real estate sector.

    • Does monetary policy still affect consumption? Time-varying transmission of credit and interest rates in Vietnam Does monetary policy still affect consumption? Time-varying transmission of credit and interest rates in Vietnam

      Chiu Meng-Hsiang, Vu Tuan Anh

      DOI: 10.63065/ajafe.2026.1.2.005

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose – This study assesses whether monetary policy still affects household consumption in Vietnam; it also assesses the relative contribution of credit and interest-rate shocks (in reduced form) and quantifies how the volatility of consumption’s own shocks evolves over time.
      Design/methodology/approach – The study estimates a Bayesian time-varying parameter vector autoregression with stochastic volatility (TVP-VAR-SV) using monthly data from 2013M4 to 2025M12 with four endogenous variables. Dynamic effects are analysed through impulse response functions (IRF), forecast error variance decomposition (FEVD), and the stochastic volatility (SV) of the consumption shock, together with robustness checks. The dataset comprises 153 monthly observations (2013M4–2025M12); retail sales and credit are drawn from FiinPro-X, the refinancing rate from the State Bank of Vietnam, and the CPI from the IMF's IFS.
      Findings – The transmission of monetary policy to consumption is weak: consumption responses to both credit and interest-rate shocks are largely statistically insignificant, and neither channel dominates persistently. The most robust finding is that the magnitude of consumption shocks varies markedly over time, with a high-volatility cluster persisting throughout 2020–2023, peaking in September 2022 at roughly eight to eleven times the normal-period level. These findings are consistent across alternative specifications.
      Originality/value – This study is the first to assess the time-varying response of Vietnamese household consumption to monetary shocks using monthly retail-sales data. It traces how the relative importance of the credit and interest-rate channels shifts across different states of the economy; through the stochastic-volatility component, it separately quantifies the volatility of consumption’s own shocks, distinct from changes in the transmission mechanism; and it extends the TVP-VAR-SV framework – so far applied in Vietnam mainly to financial variables – to the transmission of monetary policy to consumption.

    • Rounding-free differential privacy for discrete count data: A field-wrapped discrete-Laplace mechanism Rounding-free differential privacy for discrete count data: A field-wrapped discrete-Laplace mechanism

      Thanh-Phong Lam

      DOI: 10.63065/ajafe.2026.1.2.006

      Tóm tắt Abstract | Bài viết Article | PDF
      Tóm tắt Abstract

      Purpose – This paper develops a differentially private mechanism for releasing discrete banking aggregates that stays in the data’s native modular domain and avoids the rounding bias of continuous-noise mechanisms.
      Design/methodology/approach – The mechanism adds two-sided geometric (discrete Laplace) integer noise to each cell count and reduces modulo the field order q. Pure ε-differential privacy follows from post-processing immunity of the modular map, and a closed-form per-cell error 1/sinh (ε) is derived. We evaluate it against uniform, continuous Laplace, and Gaussian baselines on synthetic State Bank of Vietnam frequency tables. The Python and R pipeline is reproducible.
      Findings – The mechanism is provably pure ε-DP and unbiased, with per-cell error governed by ε alone, independent of the field order. It matches the utility-optimal discrete Laplace and stays below the rounded continuous Laplace at every budget. The uniform alternative is worse by up to two orders of magnitude and ignores ε.
      Originality/value – The paper contributes a discrete, modular-closed differential privacy mechanism for banking disclosure. Releases reconcile exactly under modular addition, and the guarantee needs no computational-hardness assumption, giving regulators a provable, reproducible tool for confidential aggregate reporting.