ECONOMIC MODELING
Milliman Economic Scenario Generator
Milliman Economic Scenario Generator (ESG) offered this client flexible capabilities with a cloud-native solution driven by the leading mathematical modeling of Milliman’s expert R&D team.
With deep customization capabilities, Milliman Economic Scenario Generator (ESG) provided the long-term volatility modeling at the time required.
By providing an effective solution alongside individualized support, the client found a better way to satisfy regulators.
Leveraging Milliman Integrate architecture, Milliman Economic Scenario Generator was integrated into this insurer’s framework with the ability to deliver up to 10,000 scenarios at D+1
We offer some valuable insights into scenario reduction and trajectory selection for stochastic ALM valuation that could help improve results and minimize computation.
This paper investigates how the choice of financial data can impact the calibration and the simulation of credit spread (credit default) scenarios within an economic scenario generator, as well as the insurance liability valuation metrics.
Increasing use of stochastic economic scenarios for valuation of liabilities has put more pressure on the operational process of carriers, but the RNG can help.
We highlight a Libor market model with constant elastic volatility, showing an interesting trade-off between parameters used and quality of results.
Many insurance companies are struggling to overcome the computational challenges involved in computing the SCR under the Solvency II regime.
With better understanding of climate transition risk exposure and stress tests proposed by regulators, internal models can help provide insights.
This paper explores options available to address the challenge of deriving market-consistent but stable long-term volatility assumptions for valuation of liabilities.
Challenges for companies in the alternative asset space include building an internal model and accounting for regulatory treatment for capital set aside.
We describe a recent realistic modelling approach of equity volatility that offers advantages when using real world economic scenarios to analyze balance sheets.
Least Squares Monte Carlo (LSMC) is a widely used proxy modelling technique in the European insurance industry.
Sensitivity testing with dependence has the potential for a wide range of applications in reporting, such as for Solvency II, IFRS 17, and balance sheet valuation.
We present a calibration technique for one complex risk neutral model, relying on neural networks and significantly reducing computational time.
The best 'one-size-fits-all' economic scenario generator solution is one that provides the choice of different methods.
The European Insurance and Occupational Pensions Authority (EIOPA) in 2019 published a study on market and credit risk modelling, observing that the equity risk shocks applied by the surveyed internal models are overall higher than those using the standard formula.
The principles-based approach under International Financial Reporting Standard 17 (IFRS 17) is both a blessing and a curse.
