Now showing 1 - 7 of 7
  • Publication
    Option Return Predictability with Machine Learning and Big Data
    (Oxford University Press, 2023)
    Bali, Turan G.
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    Beckmeyer, Heiner
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    Drawing upon more than 12 million observations over the period from 1996 to 2020, we  nd that allowing for nonlinearities signi cantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizeable pro ts in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures o er substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing.
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    Scopus© Citations 5
  • Publication
    Essays in Derivatives Markets
    (Universität St. Gallen, 2023-02-20)
    Rebalancing of leveraged ETFs and delta-hedging of equity options are two distinct and economically significant sources of order flow and liquidity demands. Liquidity Provision to Leveraged ETFs and Equity Options Rebalancing Flows finds that delta-hedging effects are persistent, those stemming from leveraged ETFs are decreasing significantly over time. These dynamics arise from different intermediation structures, generating heterogeneous levels of information asymmetry. While leveraged ETF providers generate perfectly predictable flows, option delta-hedgers have flexibility in deciding the strategic timing of their rebalancing, resulting in less predictable flows. Credit Variance Risk Premiums studies the pricing of variance risk in credit markets by employing a unique data set of credit swaptions. Returns of credit variance swaps are negative and economically large, irrespective of the credit rating class. Shorting credit variance swaps yields annualized Sharpe ratios well above their counterparts in other asset classes. The returns remain highly statistically significant when accounting for transaction costs and cannot be explained by established risk-factors and structural model variables. Commodity Tail Risks investigates the cross-section of tail risks in commodity markets. Left and right tail risks play an equally important role in commodity markets. For commodity producers, negative price jumps (left tail risk) might have devastating consequences. For commodity consumers though, e.g., companies that process commodities, right tail risks matter more. Both left and right tail risk implied by option markets are large. Moreover, both risks are priced in the cross-section of commodity futures returns. The option market has witnessed significant growth in recent years. Whereas risk factors in equity markets have been studied in detail, the same does not apply for option returns. Option Return Predictability with Machine Learning and Big Data finds that option returns are highly predictable. Allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power. Most of the factor models assume a linear structure. There are, nonetheless, no obvious theoretical or intuitive justifications for this assumption. An Autoencoder Based Factor Model for Option Returns adopts a novel latent factor model, which allows for non-linearities, and applies it to index options. The model excels in explaining variation in risk compensation and out-of-sample trading strategies.
  • Publication
    Option Factor Momentum
    We document profitable cross-sectional and time-series momentum in 56 option factors constructed from monthly sorts on daily delta-hedged option positions. Option factor returns are highly autocorrelated, but momentum profits of strategies with longer formation periods are mainly driven by high mean returns that persistently differ across factors. Momentum effects are the strongest in the factors' largest principal components, consistent with findings for stock factor momentum. Finally, we find a new form of momentum in options markets: momentum in single delta-hedged option returns. Option factor momentum fully subsumes option momentum, whereas option momentum cannot explain option factor momentum. Our findings provide insights into the channels that drive option momentum and have implications for designing profitable option trading strategies.
  • Publication
    Credit Variance Risk Premiums
    (SoF-HSG, 2019-06-04) ;
    This paper studies variance risk premiums in the credit market. Using a novel data set of swaptions quotes on the CDX North America Investment Grade index, we find that returns of credit variance swaps are negative and economically large. Shorting variance swaps yields an annualized Sharpe ratio of almost six, eclipsing its counterpart in fixed income or equity markets. The returns remain highly statistically significant when accounting for transaction costs, cannot be explained by established risk-factors, and hold for various investment horizons. We also dissect the overall variance risk premium into payer and receiver variance risk premiums. We find that exposure to both parts is priced. However, the returns for payer variance, associated with bad economic states, are roughly twice as high in absolute terms.
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