When Risk Data Is Sparse: Modeling Emerging Market Credit Risk
Date: Tuesday, December 2, 2014
Time: 11:00 am EDT | 4:00 pm BST | 11:00 pm HKT
Duration: 60 minutes
With certain asset classes—particularly illiquid markets such as emerging markets sovereign debt — risk data can be sparse, making it difficult to effectively measure portfolio risk. In this presentation, we address this issue by demonstrating the use of risk information from one market to forecast risk in another market. In particular, we present a framework for predicting changes in sovereign and corporate spreads using information from FX, equity and synthetic credit markets. Following that, we illustrate the value of stress testing and detection of early warning signals. Numerous examples will be provided throughout the presentation. This approach provides multi-asset class portfolio managers and risk managers with a more complete view of the risk landscape.
Key Learning Objectives:
- How the relationships between asset classes help measure risk
- Examine relationships within and between market observables e.g. CDS spreads, bond spreads, currencies and equities
- Demonstrate the use of information from one market in a model to estimate likely changes in another
- Reveal the benefits of stress testing and detection of early warning signals
- Provide examples from the financial sector
Eric Kavanagh, The Bloor Group
Robert Stamicar, Senior Director – Risk Management Research, Axioma, Inc.