In addition to providing a full time series of estimated net climate costs, final Climate VaR output is reported as the summed present value of these net costs divided by the firm’s current valuation. This final output can be employed to understand the rank and magnitude of relative climate cost exposure between companies for a scenario as well as the “maximal drawdown” of the firm’s current valuation within that scenario.
Although not formulated as an explicit return forecasting tool, investors and risk managers are interested in the extent at which Climate VaR can be used as a signal to forecast investment risk and return. Important research to that end is historical back testing of Climate VaR output to investigate the extent that past Climate VaR values could serve to forecast historical returns.
This study aims at understanding the potential of Climate Value-at-Risk (Climate VaR) to serve as an early warning indicator for asset price movements. To test this, this research explores to what extent Climate VaR is able to predict historical financial returns. To provide a comprehensive view, this research can be extended to evaluate various asset classes, industries, and geographic regions.
Relevance for industry/investors/ecosystem:
As the world transitions to more sustainable practices, understanding the implications of climate risks on financial performance is important for investors, asset managers, and other financial entities. This study could contribute to better risk assessment tools that incorporate Climate VaR, to enhance portfolio management and investment decision-making processes. Furthermore, regulators are increasingly emphasizing the importance of considering climate risks, making this study timely and relevant.
MSCI (and other) data to be used:
- Historical market data for a diverse set of asset classes, including equities and bonds.
- Historical Climate VaR values (company/equity/debt/bond level).
- Company-specific industrial classification, location data, balance sheets or sustainability disclosures data if needed in the research.
- Economic indicators and other relevant macroeconomic data (public, non-MSCI).
Types of analysis to be conducted:
- Descriptive statistics to understand the data distribution and patterns.
- Regression analysis to ascertain the relationship between Climate VaR and historical returns.
- Time-series analysis to capture temporal shifts in the predictive power of Climate VaR.
- Sectorial analysis to identify industries or asset classes where Climate VaR has more pronounced effects.
Ideal candidate profile/degree:
- Enrolled in a Master’s program related to Finance, Economics, Statistics, Environmental Sciences, Mathematics, Physics, Computer Science, etc.
- Strong analytical and quantitative skills.
- Proficiency in statistical analysis tools/software, experience with Python.
- A keen interest in sustainability and its intersection with finance.
James Edwards, Climate Fixed Income Research, Climate Risk Center, MSCI New York
Qimeng Yin, Data Scientist Financial Modeling, Climate Risk Center, MSCI Zurich
Please note that this program is available to Master’s level students from the following institutions:
- ETH Zurich
- EPFL (École Polytechnique Fédérale de Lausanne)
- University of Zurich
- ZHAW Zurich University of Applied Sciences
- University of St. Gallen