Data Architecture and Sources

The dashboard aggregates data from four principal sources, each selected for its relevance to a specific asset class and its ability to proxy for a distinct systemic risk channel:

1. Public equities:
• Source: Alpha Vantage API (live market data).
• Indicators: Beta (systematic risk), Dividend Yield (income stability), Price/Earnings Ratio (valuation risk), Market Capitalization (liquidity proxy).
• Rationale: These metrics collectively capture market-implied volatility, income risk, valuation bubbles, and liquidity-key contributors to equity systemic risk.
2. Commodities:
• Source: Yahoo Finance (simulated for demonstration; can be replaced with live API).
• Indicators: Price volatility, supply/demand imbalances, and geopolitical risk proxies.
• Rationale: Commodities are sensitive to exogenous shocks (e.g., climate events, geopolitical tensions) that can propagate through supply chains and financial markets.
3. Sovereign debt:
• Source: World Bank API (simulated for demonstration).
• Indicators: Interest rate levels, fiscal health, and macroeconomic stability.
• Rationale: Sovereign risk is a key transmission channel for systemic crises, especially in the context of fiscal or monetary shocks.
4. Real estate:
• Source: Simulated (due to API restrictions; can be extended to use Zillow or other real estate APIs).
• Indicators: Price appreciation, physical climate risk, regulatory exposure, and liquidity.
• Rationale: Real estate is exposed to both transition and physical climate risks, as well as regulatory and liquidity constraints.

Risk Dimensions and Quantification

Each asset class is evaluated across four orthogonal risk dimensions, reflecting the multidimensional nature of systemic risk:

• Transition risk: Exposure to abrupt policy, technological, or market shifts (e.g., decarbonization, regulatory changes).
• Physical risk: Vulnerability to exogenous shocks such as climate events, natural disasters, or supply chain disruptions.
• Regulatory risk: Susceptibility to changes in law, regulation, or compliance standards.
• Liquidity risk: The risk of market illiquidity or inability to transact at fair value during periods of stress.

Each risk dimension is quantified on a normalized scale (typically 1–5), allowing for aggregation into a Systemic Risk Score (sum or weighted sum), which facilitates cross-asset comparison.

Theoretical and Practical Implications

This dashboard is grounded in the literature on systemic risk measurement (see, e.g., Acharya et al., 2017; Adrian & Brunnermeier, 2016), financial network theory, and climate-financial risk integration (Battiston et al., 2017). By integrating live and simulated data across asset classes, it enables:

• Early warning detection: Identification of emerging systemic threats before they materialize as crises.
• Portfolio stress testing: Scenario analysis for capital reallocation under disorderly transition, physical climate shock, or regulatory fragmentation.
• Policy and regulatory insight: Supports macro-prudential supervision and regulatory compliance by visualizing cross-asset risk transmission channels.

Limitations and Extensions

• Data quality and coverage: Some asset classes rely on simulated data due to API restrictions; future iterations can integrate premium APIs (e.g., Bloomberg, FactSet, MSCI ESG) for full coverage.
• Model risk: The aggregation and normalization of risk scores may mask tail risks or nonlinearities inherent in financial systems.
• Customization: The framework is extensible to include scenario toggles, time-series analysis, and user-defined weighting schemes.