Authors: Harsh Panchal
Abstract: Emerging economies represent increasingly significant components of the global financial system; however, investment decision-making within these markets remains constrained by elevated volatility, market inefficiencies, liquidity limitations, institutional uncertainty, and asymmetric information. Traditional financial risk models, including Capital Asset Pricing Model (CAPM), Value at Risk (VaR), Generalised Autoregressive Conditional Heteroskedasticity (GARCH), and mean–variance portfolio optimisation, were primarily developed under assumptions associated with mature financial markets. Their applicability in emerging economies requires critical evaluation due to differences in market structures, investor behaviour, and macroeconomic conditions. This study evaluates the effectiveness of traditional financial risk assessment models and portfolio optimisation approaches in emerging economies through an operations research perspective. The research develops an integrated analytical framework combining quantitative risk modelling, mathematical optimisation techniques, and multi-objective decision-making approaches. The study investigates how optimisation methods, including quadratic programming, Conditional Value at Risk (CVaR) optimisation, and risk-adjusted performance measurement, can enhance portfolio allocation decisions under uncertain market environments. The research contributes to the financial risk management literature by examining the limitations of conventional models when applied to emerging markets and proposing an operations research-based framework capable of incorporating market constraints, downside risks, and investor preferences. The findings suggest that while traditional models provide valuable foundations for portfolio construction, advanced optimisation techniques improve robustness by accounting for non-normal return distributions, extreme market events, and dynamic volatility patterns. The proposed approach provides practical implications for institutional investors, financial regulators, and policymakers seeking improved risk management strategies in emerging economies. This study extends existing research by bridging financial econometrics and operations research methodologies, demonstrating how mathematical optimisation can support more resilient investment decisions in financially developing markets.
