Epidemiology of Volatility Transmission and Regime Change Risk Models
There are common pathways in the transmission of viruses and volatility. Volatility, like infectious disease, is transmitted through common sources of infection. Sources of volatility transmission include investor psychology, market contagion, and liquidity pressures. The science of epidemiology, the Kermack-McKendrick SIR Model (1927) and “Reproduction Ratio” are useful mechanisms to examine market contagion and volatility spikes – both UP and down.
Most quantitative risk models fail to detect regime changes. Because quant alpha and risk models rely heavily on historical data, off-the-shelf risk models are unable to rapidly respond to exogenous epochs like novel coronavirus (COVID-19). This presentation will discuss these issues. It will further demonstrate how asset managers are using Machine Learning to uncover correlated sources of risk and classify and monetize differentiated sources of potential alpha in ways that quantitative risk models cannot handle.
This event is offered in partnership with CFA Society New York.
Steven J. Lerit, CFA, Client Portfolio Manager, Washington Crossing Advisors
Richard P. Roche, CAIA, Managing Director, Little Harbor Advisors, LLC
10:00 a.m. - Opening Remarks
10:05 a.m. - Keynote Speaker, Richard P. Roche, CAIA
10:30 a.m. - Q&A
10:45 a.m. - Break
10:50 a.m. - Keynote Speaker, Steven J. Lerit, CFA
11:15 a.m. - Q&A
11:30 a.m. - Closing Remarks
- Explain Viral/Volatility Epidemiological Model (Kermack-McKendrick SIR)
- Discuss virus and VOL super-spreaders and the common vectors of transmission
- Understand why most quantitative risk models fail to detect regime change
- Understand fat-tail, multi-asset class and turbulence-enhanced risk models
- Describe how to identify and highlight unknown risk exposures
- Analyze monthly economic sector returns and sector correlation heat maps
This event qualifies for 1.5 hours of continuing education credit for CFA charterholders.