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:

  1. asymmetric depth in low strike options as a measure of information content  [1,2,3,4],
  2. significant increases in perceived tail risk as indicated by the CBOE SKEW Index® [5,6],
  3. inversion in VIX futures term structure [7,8,9], and
  4. 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. [17]  In short, nextSignals employs a volatility-scaled time-series momentum strategy to manage a multi-factor portfolio [18].

 

Figure 2: Combining volatility forecasting and time-series momentum models (“nextSignals”)

 


References

  1. Huang, Tao and Li, Junye and Wu, Fei, Asymmetric Variance Premium, Skewness Premium, and the Cross-Section of Stock Returns (November 20, 2018).
  2. Sepp, Artur, Volatility Modelling and Trading (July 17, 2016). Global Derivatives Workshop Global Derivatives Trading & Risk Management, Budapest, 2016.
  3. Augustin, Patrick and Brenner, Menachem and Subrahmanyam, Marti G., Informed Options Trading Prior to M&A Announcements: Insider Trading? (October 26, 2015).
  4. Tsai, Wei-Che and Chiu, Ying-Tzu and Wang, Yaw-Huei, The Information Content of Trading Activity and Quote Changes: Evidence from VIX Options (November 14, 2014). Journal of Futures Markets (2014).
  5. Liu, Zhangxin (Frank) and Faff, Robert W., Hitting SKEW for SIX (May 29, 2016). 2013 Financial Markets & Corporate Governance Conference; 27th Australasian Finance and Banking Conference 2014 Paper.
  6. Marabel Romo, Jacinto, Pricing Volatility Options Under Stochastic Skew with Application to the VIX Index (March 1, 2015). The European Journal of Finance.
  7. Johnson, Travis L., Risk Premia and the VIX Term Structure (January 27, 2016). Journal of Financial and Quantitative Analysis 52 (2017), 2461-2490.
  8. Donninger, Chrilly, Modeling and Trading the VIX and VSTOXX with Futures, Options and the VXX (May 22, 2015).
  9. Zhu, Yingzi and Zhang, Jin E., Variance Term Structure and VIX Futures Pricing (March 2005). 
  10. Engle, R. F., 2009. Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press.
  11. Ederington, Louis H. and Guan, Wei, Forecasting Volatility (January 2004).
  12. Caldara, Dario and Matteo Iacoviello, “Measuring Geopolitical Risk,” working paper, Board of Governors of the Federal Reserve Board, January 2018
  13. Moskowitz, Tobias J. and Ooi, Yao Hua and Pedersen, Lasse Heje, Time Series Momentum (September 1, 2011). Chicago Booth Research Paper No. 12-21; Fama-Miller Working Paper.
  14. Cheng, Enoch and Struck, Clemens, Time-Series Momentum: A Monte-Carlo Approach (April 4, 2019).
  15. Babu, Abhilash and Levine, Ari and Ooi, Yao Hua and Pedersen, Lasse Heje and Stamelos, Erik, Trends Everywhere (September 1, 2018). Journal of Investment Management.
  16. Malmgren, Pippa, Geopolitics for Investors (March 17, 2015). CFA Institute Research Foundation M2015-1.
  17. Guo, Dajiang, Dynamic Volatility Trading Strategies in the Currency Option Market Using Stochastic Volatility Forecasts (April 23, 1999).
  18. Lim, Bryan and Zohren, Stefan and Roberts, Stephen, Enhancing Time Series Momentum Strategies Using Deep Neural Networks (April 9, 2019).