A Multi-Factor Timing Model

  • It is order flow (dV/dt), not trading volume V, that is the principle driving force in the auction market process.
  • While the greatest impact on market volatility arises from news, the volatility response is asymmetric.    That is, negative surprises increase volatility more than positive surprises.
  • During stressful market periods, algorithmic traders significantly reduce their liquidity provision.    Liquidity deficits increase volatility and widen bid-ask spreads.    When confronted by a negative shock, positional trading in put options can act to independently destabilize the auction and fuel an unusually convex down-auction.
  • During rallies, the market is essentially supported by huge call sales.    When GEX is rising, investors’ call overwriting is dominating, volatility is low, and market makers’ provision of liquidity is vigorous.
  • Taken together, the gamma and vanna hedging pursuits of options market makers play an important role in supporting carry, powering selloffs, and … in the case of vanna … energizing short-term market reversals when market makers are net short gamma. [1,2]    There exists extensive empirical literature which documents that the hedging demand of counterparties in the option market leads to the transfer of order imbalance from option market to stock market and is the driver of predictability of spot index returns.


Three Important Sentiment Indicators

  • Imbalances in the bid-ask limit order book for index options provides rich information for time-series forecasting.    While there is no consequential relationship between day-to-day changes in implied volatility and subsequent returns, order imbalances in short-maturity (“weekly”) S&P 500 index options speedily reflect heightened concerns about negative tail events and help predict future equity returns.
  • Volatility is a main driver of asset returns and risk premia.   The Cboe Volatility Index (VIX) index is a forward-looking measure of implied volatility.   Computed from VIX options, the Cboe VVIX Index (VVIX) has predictive power for VIX options returns and represents a measure of how tentative market participants are about the future of VIX.   Notably, while both VIX and VVIX peak during major financial crises, VVIX spikes during lesser times of economic uncertainty.   Because VVIX shows greater movement than VIX, demonstrates little correlation with VIX and reflects investors’ desire for early resolution of uncertainty, VVIX is an important predictor of future realized volatility.
  • Dark pools’ mean market share and short sell volume increases with the value of information and decreases in the absence of inside information.   During stressed market conditions, institutional order flow migrates from dark to lit venues … a condition known as “flight-to-transparency.”

Technical Notes Regarding Higher Order Greeks

[1] Gamma measures how much the price of a derivative accelerates when the underlying security price moves.   Market makers in products with gamma exposure, such as options, are commonly net short these products.   As a result, they have to buy additional securities when prices are rising and sell when prices are falling to ensure that their positions are delta-neutral.   Trading in the direction of the market price movement will exacerbate market swings.
[2] Vanna measures how much the price of a derivative accelerates as implied volatility (IV) changes.   In a sharp decline, IVs rise in response to investors’ demand for puts.   As IVs rise, dealer deltas rise, compellng dealers to sell into the decline.   When IVs ultimately collapse, dealers’ vanna exposure forces them to aggressively buy back their short positions … fueling a reversal to the mean.

References

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    2. Anselmi, G., Nimalendran, M., & Petrella, G. (2022). Order flow fragmentation and flight-to-transparency during stressed market conditions: Evidence from COVID-19. Finance Research Letters, 44, 102101.
    3. Baltussen, G., Da, Z., Lammers, S., & Martens, M. (2021). Hedging demand and market intraday momentum. Journal of Financial Economics, 142(1), 377-403.
    4. Bogousslavsky, V., & Collin-Dufresne, P. (2022). Liquidity, volume, and order imbalance volatility. Journal of Finance.
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    6. Cox, J.C., Smith, V.L., & Walker, J.M. (1982). Auction market theory of heterogeneous bidders. Economics Letters, 9, 319-325.
    7. Inoua, S. M., & Smith, V. L. (2021). Classical Theory of Competitive Market Price Formation.
    8. Jiang, G. J., & Pan, G. (2022). Speculation or hedging?—Options trading prior to FOMC announcements. Journal of Futures Markets.
    9. Lu, X., & Abergel, F. (2018). High-dimensional Hawkes processes for limit order books: modelling, empirical analysis and numerical calibration. Quantitative Finance, 18(2), 249-264.
    10. Niño, J., Hernández, G., Arévalo, A., León, D., & Sandoval, J. (2018). CNN with Limit Order Book Data for Stock Price Prediction. Proceedings of the Future Technologies Conference (FTC) 2018.
    11. Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2020). Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency
    12. Limit Order Book Data. IEEE Transactions on Emerging Topics in Computational Intelligence, 4, 774-785.
    13. Singh, M. (2022). Significance of Stock Derivatives on Volatility and Liquidity of Market: A Comprehensive Review of Literature. International Journal of Recent Advances in Multidisciplinary Topics, 3(1), 22-32.
    14. Squeezemetrics. (2020) The Implied Order Book: Measuring S&P 500 Liquidity with SPX Options. Squeezemetrics. https://squeezemetrics.com/monitor/download/pdf/The_Implied_Order_Book.pdf