Advanced financial engineering strategies integrating statistical inference to improve robustness of market risk assessment
Senior Model Risk Analyst, Model Risk Management, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 3595-3609
Article DOI: 10.30574/wjarr.2024.24.3.3852
Publication history:
Received on 10 November 2024; revised on 24 December 2024; accepted on 28 December 2024
Abstract:
Market risk assessment has become increasingly challenging as financial systems face heightened volatility, structural breaks, and nonlinear dependencies driven by global macroeconomic uncertainty. Traditional risk metrics such as Value-at-Risk, beta coefficients, and volatility estimators often fall short during turbulent periods because they rely on assumptions of normality, linearity, or stable correlations. As financial markets evolve, advanced financial engineering strategies that integrate statistical inference offer more resilient approaches capable of capturing complex dynamics and tail-risk behaviour. These strategies combine quantitative modeling, probabilistic estimation, and data-driven optimization to enhance the robustness and reliability of market risk assessment. This study provides a structured examination of advanced financial engineering techniques designed to improve market risk modelling under uncertain and rapidly shifting conditions. At a broad level, the discussion reviews limitations of conventional models, emphasizing how structural breaks, regime shifts, and asymmetric return distributions create vulnerabilities in traditional risk frameworks. The analysis then narrows to statistical inference–based methods, including Bayesian updating, shrinkage estimators, semiparametric density modeling, and advanced filtering techniques such as particle filters and unscented Kalman filters. These tools allow for adaptive parameter estimation and real-time incorporation of new information, improving risk sensitivity during volatile episodes. Further emphasis is placed on engineering strategies that integrate machine learning with statistical inference such as regularized regression, ensemble learning, and probabilistic neural networks to capture nonlinear interactions, high-dimensional dependencies, and hidden market states. Additionally, the study explores robust optimization frameworks, stress-scenario construction, and extreme-value theory to quantify tail exposures more accurately. By combining rigorous statistical inference with engineering-driven modelling structures, these advanced methods significantly strengthen market risk assessment, support proactive decision-making, and enhance the resilience of financial institutions in uncertainty-dominated environments.
Keywords:
Market Risk Assessment; Financial Engineering; Statistical Inference; Robust Modeling; Tail-Risk Analysis; Bayesian Techniques
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0