International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Dynamic / Adaptive Risk Budgeting with Market Microstructure Signals

Document Type : Original Article

Authors
Sepideh Javadi Soufiani 1
HAMIDREZA KORDLOUIE 1
Mohammad Hosein Fatehi Dabanloo 1
Seyyed Behshad Yasini 1
Mohammad Hamed Khan Mohammadi 2
1 Finance Department, Isl.C., Islamic Azad University, Tehran, Iran
2 Finance Department, DB.C., Islamic Azad University, Tehran, Iran
10.22034/ijfma.2026.79165.2365
Abstract
This study proposes a Dynamic Risk Budgeting (DRB) framework that integrates time-varying market microstructure signals into portfolio construction to improve risk-adjusted performance in emerging markets. While classical portfolio optimization methods suffer from severe estimation error and instability, and static risk budgeting approaches rely on the restrictive assumption of stable risk structures, the proposed DRB framework dynamically updates target risk budgets in response to realized volatility and liquidity conditions.
Using daily data for 280 equities and exchange-traded funds listed on the Tehran Stock Exchange, portfolios are constructed under five competing strategies: equally weighted, minimum variance, maximum Sharpe ratio, static risk budgeting (ERC), and the proposed dynamic risk budgeting model. Conditional covariance matrices are estimated on rolling windows, while dynamic risk budgets are formed as convex combinations of inverse-volatility scores and standardized liquidity indicators. Portfolio weights are obtained by minimizing the deviation between realized and target risk contributions under realistic constraints.
The results provide strong evidence that the DRB portfolio dominates both traditional optimization methods and static risk budgeting in terms of risk-adjusted performance and downside risk control. Specifically, DRB achieves higher Sharpe and Sortino ratios, lower annualized volatility, and substantially reduced maximum drawdown relative to all benchmark strategies. These findings indicate that incorporating market microstructure information into the risk budgeting process significantly enhances portfolio resilience and efficiency, particularly in volatile and liquidity-constrained emerging markets. The proposed DRB framework offers a practical and robust alternative to static allocation rules and provides a foundation for future extensions based on machine-learning-driven risk forecasts.
Keywords
Dynamic Risk Budgeting
Risk Parity
Market Microstructure Signals
Realized Volatility
Liquidity
Portfolio Optimization
Emerging Markets

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