Ziyu Wang — An Operations Research Analyst Solving Problems Under Complex Constraints

In recent years, subsectors across finance and the real economy have been transitioning from extensive expansion to refined management. During this shift, many practitioners face an increasingly prominent practical challenge: amid heightened market volatility, how should risks be decomposed and quotas allocated to achieve sound business operations? Faced with continuously tightening constraints and increasingly diverse and complex risk dimensions, how can decisions remain clear and actionable? Drawing on his professional expertise and frontline experience, Ziyu Wang, an expert in operations research and analysis, has delivered an impressive solution that has attracted widespread industry attention.

Ziyu Wang completed his undergraduate studies at Boston University, earning a Bachelor of Arts in Economics with a minor in Mathematics. His economics training equipped him with a macro perspective to understand market dynamics and resource allocation, while his minor in Mathematics provided him with rigorous tools to break down complex problems. After pursuing a Master of Science in Finance at the University of Southern California, he further strengthened his quantitative analytical capabilities in asset pricing, risk management, and portfolio construction. Building upon this academic foundation, he developed a work framework that leveraged data mining to uncover business fundamentals, applied quantitative analysis to assess risks, and used optimization frameworks to support decision-making. Drawing on this framework, he established the theoretical underpinnings for the “Manual on Risk Budget Allocation and Optimization Strategy Analysis of Multi-Asset Portfolios.” He also refined cross-disciplinary analytical reasoning into a reusable methodological system.

The “Manual on Risk Budget Allocation and Optimization Strategy Analysis of Multi-Asset Portfolios” is written against the real-world backdrop of the asset management industry’s transition from single-asset allocation toward multi-asset, multi-strategy and multi-factor frameworks. It identifies the limitations of the traditional mean-variance model, including poor parameter stability, one-dimensional risk characterization, and the tendency toward excessive concentration in investment weights. The manual proposes a complete analytical system centered on risk budgeting and implemented via optimization solvers. It provides analytical and practical implementation plans for risk allocation and portfolio optimization to asset management institutions, pension funds, insurance capital, bank wealth management products, family offices, and high-net-worth clients.

At the methodological level, several core designs within this framework directly address long-standing industry pain points. To resolve the problem of overly abstract model outputs that are difficult for decision-makers to apply, it introduces the concept of the risk-budget feasible region, translating mathematical optimization results into decision boundaries intuitive to portfolio managers. To tackle the rigidity of static risk budgets, which fail once market conditions shift after initial setup, it constructs a dynamic risk-budget framework. This framework incorporates macroeconomic regimes, market volatility structures, liquidity, and credit spreads into adjustment rules, allowing risk budgets to contract or expand alongside changing market environments. To address the blind spot of conventional methods, which focus solely on normal market fluctuations and neglect tail risk, it introduces stress risk budgeting, using historical extreme scenarios and Monte Carlo simulation to set upper bounds for tail risk. To overcome the tradeoff limitations of single-objective optimization, it further develops a multi-objective risk-budget optimization model. Beyond risk-budget constraints, the model simultaneously accounts for return targets, drawdown control, and turnover control, and employs intelligent optimization algorithms to solve for the Pareto frontier.

Once the theoretical framework was established, Ziyu Wang conducted comprehensive validation using real-world data. He selected publicly available market data covering equities, bonds, commodities, and REITs, alongside private equity-related indices, to test the performance of risk-budgeting strategies across different market regimes. The results demonstrated that, compared with market-cap weighting and equal-weight allocation, the risk-budgeting strategy he developed yielded notable advantages in volatility control, drawdown reduction, and improvements in risk-adjusted returns. The diversification benefit was especially pronounced amid rising inflation and a regime shift where stock-bond correlation turned positive. More remarkably, he extended risk budgeting from the asset-class level to macro factors, style factors and industry factors to resolve the problem of risk homogenization within asset categories. He also mapped out a full implementation pathway spanning risk preference definition, budget decomposition, asset-factor mapping, stress testing, attribution analysis, and rebalancing.

Ziyu Wang’s methodology, centered on risk budgeting and optimization frameworks, holds value beyond the financial investment sector. Its logic of risk decomposition, resource quota allocation, and constrained decision optimization is transferable across industries. It can provide analytical insights for refined management scenarios in the real economy, including resource scheduling, project risk management, and multi-business-line budget allocation.

At a time when the asset management industry is shifting from extensive expansion to refined management and corporate operations are shifting from experience-driven to data-driven models, Ziyu Wang’s practice offers a valuable benchmark. The risk budgeting and optimization analytical framework he built is valuable not merely embodied in a manual or one-off model validation. More importantly, it demonstrates a viable path to embed quantitative methods directly into decision workflows. For institutions eager to improve the precision of risk allocation and the efficiency of resource deployment, professionals who can dive deep into model mechanics while viewing business contexts holistically are rare driving forces amid the industry transformation. Ziyu Wang’s research endeavors are ongoing, yet his direction is clear: applying operations research and analysis to convert complex decision-making problems into quantifiable, solvable and verifiable systems, enabling quantitative analysis to deliver tangible decision-making value.