Empirical Tools for Side-stepping Drawdowns
Ultimately, the only way to avoid drawdowns is to forecast them and then evade them. To mitigate unfavorable return distributions, nextSignals synthesizes data from four models:
- asymmetric depth in out-of-the money options as a measure of information content [1,2,3,4],
- significant increases in perceived tail risk as indicated by the CBOE SKEW Index® [5,6],
- inversion in VIX futures term structure [7,8,9], and
- descriptive statistics that measure volatility clustering (e.g. GJR-GARCH ) for predicting one-day ahead returns in aggregate indexes [10,11].
Geopolitical and Economic Policy Uncertainty
Tail events related to geopolitical and macroeconomic events are important drivers for the performance of many investment strategies. While prediction is nearly impossible, preparedness can be achieved by scenario planning.
Figure 1: The application of AI-human hybrid intelligence. [12,13,14,15,16]
Using the Information Content of Options Markets to Manage Risk
Dynamic volatility trading strategies have been shown to deliver higher Sharpe ratios, lower market risk, and higher abnormal returns.  In short, nextSignals employs a volatility-scaled time-series momentum strategy to manage a multi-factor portfolio .
Figure 2: Combining volatility forecasting and time-series momentum models (“nextSignals”)
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