Forthcoming Articles

International Journal of Computational Economics and Econometrics

International Journal of Computational Economics and Econometrics (IJCEE)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Computational Economics and Econometrics (2 papers in press)

Regular Issues

  • Quantile asymmetric connectedness across the Iranian stock market   Order a copy of this article
    by Roozbeh Balounejad Nouri 
    Abstract: This study examines the quantile asymmetric connectedness among different industries in the Tehran Stock Exchange using daily data from late November 2008 to April 2024. The quantile vector autoregressive (QVAR) approach is employed to capture the nonlinear and asymmetric behaviour of total and pairwise connectedness among industries. The results reveal that inter-industry linkages have significantly evolved over time, with currency fluctuations and economic-political developments playing a crucial role in shaping sectoral performance. The primary metals and investment industries are identified as net transmitters of shocks, while the petrochemical and sugar industries act as net receivers. The findings indicate that the degree of connectedness varies across different market conditions, confirming the presence of quantile-dependent asymmetries. Overall, the results enhance the understanding of asymmetric risk transmission across industries and may assist policymakers and investors in designing more informed strategies for sectoral risk management and portfolio diversification.
    Keywords: stock market connectedness; QVAR approach; different industries; Tehran Stock Exchange.
    DOI: 10.1504/IJCEE.2027.10080984
     
  • State-dependent graph portfolio optimisation using selective state space models and regime-adaptive graph learning   Order a copy of this article
    by Nguyen Luong Gia Bao, Ngo Phu Thanh 
    Abstract: Emerging-market portfolio allocation demands models that retain long-horizon signals while adapting to changing cross-asset dependence. We introduce SDGP, a reinforcement-learning architecture coupling a Mamba-3 temporal encoder with a learned sparse graph reconstructed from asset representations at each decision step. On the 100-stock VN100 universe over 2020-2025, six-fold walk-forward tests across five seeds give SDGP the highest pooled point estimates among the evaluated learned models for cumulative return, annual return, Sharpe, Sortino, and Calmar, including a Sharpe ratio of 0.7479. However, all Holm-adjusted HAC and stationary-bootstrap tests fail to distinguish the return-series differences, and every bootstrap interval includes zero. Allocation diagnostics show that SDGP is materially active, with 40.87 effective holdings, whereas the standalone encoders remain close to equal weight. Fixed-checkpoint diagnostics show only small changes in weights and Sharpe when graph controls are varied or the graph contribution is zeroed. The evidence therefore supports SDGP as a competitive active-allocation architecture while establishing neither statistical superiority nor a uniquely identified graph-performance channel.
    Keywords: portfolio allocation; deep reinforcement learning; selective state space model; graph neural network; emerging market.
    DOI: 10.1504/IJCEE.2027.10081973
     

 

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