Findings from the 7th Annual OptionMetrics Research Conference (ORC2018) at Fordham University in New York City.
OptionMetrics hosted the 7th Annual OptionMetrics Research Conference (ORC2018) on Monday, October 15, 2018, at Fordham University in New York City. The event drew together researchers from across the globe to present ideas and research in the expanding options field. OptionMetrics Founder and President David Hait welcomed guests and officiated the conference.
Jared Woodard, Ph.D., global investment strategist, Bank of America Merrill Lynch, was the opening keynote, offering insights on global asset allocation and key market trends based on current and historical macroeconomic data, global fund flows, and investor positioning. He discussed how rising populism in Europe, increased household debt in China, a bubble in technology and e-commerce stocks, and a significant level of corporate debt worldwide may all be catalysts for the recent spikes in volatility.
Some highlights from other presenters included:
- Demand for Lotteries: The Choice Between Stocks and Options, Pedro A. Garcia Ares, Ph.D., University of Exeter, Lias Filippou, Ph.D., John M. Olin Business School, Washington University; Fernando Zapatero, Ph.D., Marshall School of Business, University of Southern California – Researchers show that the availability of options to retail investors displaces lottery stocks. They find evidence that uninformed traders (e.g., gamblers) may drive lottery trading in OTM options. They also find that the lottery features of OTM options are likely unrelated to the underlying securities, as we observe systematic violations of arbitrage conditions.
- What Information Does Risk Neutral Skewness Contain? Evidence from Momentum Crashes, Paul Borochin, Ph.D., CFA, University of Connecticut; Yanhui Zhao, Ph.D., University of Wisconsin-Whitewater – Researchers construct an RNS factor-mimicking portfolio and find that a momentum strategy that avoids high skew factor loading stocks has superior performance to both standard and risk-managed momentum strategies.
- Improved Forecasting of the Implied Volatility Surface, Xun Gong, Michel van der Wel, Ph.D., Dick van Dijk, Ph.D., Erasmus University Rotterdam – Existing literature documents that the volatility surface can be modelled by a limited number of factors using simple regression techniques, and that these factors are persistent. However, regression techniques leave substantial serial correlation in the residuals. Gong, Wel, and Dijk propose an autoregressive model and "equilibrium correction" style model that uses the information of the deviation from put call parity to directly exploit the serial correlation. They apply the models to S&P500 index options and options of 95 stocks, and show that the new models improve the existing model with a 40% decrease of both in-sample RMSE and out-of-sample RMSFE.
- Understanding Returns to Short Selling Using Option-Implied Stock Borrowing Fees, Dmitriy Muravyev, Ph.D., Boston College; Joshua Pollet, Ph.D. and Neil Pearson, Ph.D., University of Illinois at Urbana-Champaign - Measures of short sale constraints and short selling activity strongly predict stock returns, an exploitable predictability that is difficult to explain. Researchers seek to resolve this puzzle by using measures of the stock borrowing costs paid by short-sellers. They show in portfolio sorts that the returns to short selling, net of stock borrowing costs, are much smaller than the gross returns to shorting or a typical long-short strategy. Option-implied borrowing fees, which reflect option market makers’ borrowing costs and the risks of changes in those costs, are on average only slightly higher than quoted borrowing fees. This finding indicates that the risk of changes in borrowing fee does not command a substantial risk premium. Option-implied borrowing fees predict future fees and stock returns, including returns net of quoted borrowing costs.