Risk Factor Modeling Methodology

mathematical foundation and objectives risk factor modeling in financial analysis is built upon a robust mathematical framework designed to address several key challenges in understanding and managing investment risk dimensionality reduction transforming the complex universe of financial instruments into a smaller set of explanatory factors risk decomposition breaking down the total risk of an instrument or portfolio into its constituent components return attribution explaining asset returns through exposure to systematic risk factors predictive modeling providing a framework for forecasting potential future returns and risks cross asset consistency ensuring a unified approach to risk across different asset classes and markets the mathematical foundation combines multivariate statistics, regression analysis, and financial theory to produce a coherent model of risk that accurately represents the underlying financial dynamics linear factor model framework theoretical basis the linear factor model is based on the fundamental assumption that asset returns can be decomposed into systematic and idiosyncratic components r i = \alpha i + \sum j(β {i,j} × f j) + ε i this framework provides a parsimonious representation of the complex relationships between assets and their underlying risk drivers regression based factor exposure estimation mantarisk has 2 regression based methodologies to estimate the factor exposures the lasso regression is used for estimating the exposure of instrument to the risk factor set it has the advantage of ensuring sparsity of the solution the ordinary least square methodology is used for estimating the betas to a given benchmark furthermore, there are additional constraints on the exposure if there are deemed economically reasonable for example, the exposure of an equity will be constrained to take only positive or zero values to its country ordinary least squares (ols) methodology the ols approach estimates factor exposures by minimizing the sum of squared residuals objective function \min \beta \sum i(r i \alpha i \sum j(\beta {i,j} × f j))² analytical solution β = (f^tf)^{ 1}f^tr statistical properties unbiased estimator when standard assumptions are met minimum variance among linear unbiased estimators (blue) asymptotic normality for inference and hypothesis testing lasso regression methodology the lasso (least absolute shrinkage and selection operator) approach adds a penalty term to the objective function to promote sparsity objective function given λ the regularization parameter controlling the strength of the penalty \min \beta \sum i(r i \alpha i \sum j(\beta {i,j} × f j))² + \lambda\sum j|\beta i,j| advantages automatic variable selection (setting some coefficients exactly to zero) improved prediction accuracy through bias variance tradeoff robustness to multicollinearity among factors hierarchical regression set construction multi level factor selection the regression set is constructed hierarchically to ensure both relevance and parsimony level 1 core factors (e g , country, sector, currency, rating, etc ) level 2 extended factors (e g continents, neighboring sectors, etc ) asset class specific factor selection different asset classes require specialized factor selection approaches equity instruments market factors based on geographical exposure sector factors based on industry classification currency factors fixed income instruments country of risk bond sector (government, corporate) credit rating currency etf instruments underlying index as primary factor additional factors based on etf composition tracking error minimization to find if there is a tracked index not found by other methods funds risk factors found by the correlation based factor selection (see below) fund breakdowns (asset class, currency, geography, sector, etc ) this specialized approach ensures that the factor model captures the unique risk characteristics of each asset class correlation based factor selection when specific asset characteristics are unavailable or in order to find additional risk factors, correlation based methods identify relevant factors correlation computation \rho {i,j} = \frac{\text{cov}(r i, f j)}{\sigma i × \sigma j} factor ranking and selection a factor s i is put in the regression set if s i = \\{f j |\rho {i,j}| ≥ \text{threshold}\\} \quad \text{for}\quad j = 1\ldots m sign constraints positive correlations may indicate positive exposures negative correlations may indicate hedging relationships constraints are be imposed to ensure economically meaningful results this approach ensures that even assets with limited metadata can be properly modeled within the factor framework model selection criteria mantarisk performs 2 regressions for the 2 levels defined above the level containing the optimal model is then selected using the bayesian information criterion (bic) bic formula given rss the sum of the squared residuals, the bic is defined as \text{bic} = n \times \ln(\text{rss}/n) + k \times \ln(n) level based selection given threshold l a level specific penalty to favor simpler models, a level is selected if \text{bic} l < \text{bic} \text{current} \text{threshold} l this approach balances model fit and complexity, preventing overfitting while capturing the essential factor relationships conclusion methodological significance the risk factor modeling methodology represents a principled approach to understanding and managing investment risk by combining statistical rigor with financial theory, it addresses the unique challenges of financial markets complexity reduction through parsimonious factor representation risk transparency through decomposition into interpretable components cross asset consistency through a unified modeling framework the resulting factor model provides a solid foundation for portfolio optimization, performance attribution, and risk management, ensuring that investment decisions are based on a comprehensive understanding of the underlying risk dynamics