Does Net Buying Pressure Affect the Shape of Implied Volatility Functions?

Bollen, Nicolas P. B.; Whaley, Robert E.

doi:10.2139/ssrn.319261

Cited by 375 publications

(713 citation statements)

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“…If option dealers sell these options to end users and cannot fully hedge themselves, they expose themselves to the risk of a decline of the index. To compensate for that they are asking higher prices; see Bakshi et al (2003), Bollen and Whaley (2004), Gârleanu et al (2009) and the references therein. We follow Gârleanu et al (2009) and assume our agents A are option dealers with expected exponential utility preferences with absolute risk aversions γ a > 0, a ∈ A.…”

Section: Simulation Of Option Prices In a Discrete Heston Modelmentioning

confidence: 99%

Equilibrium Pricing in Incomplete Markets Under Translation Invariant Preferences

et al. 2016

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We provide results on the existence and uniqueness of equilibrium in dynamically incomplete financial markets in discrete time. Our framework allows for heterogeneous agents, unspanned random endowments and convex trading constraints. In the special case where all agents have preferences of the same type and all random endowments are replicable by trading in the financial market we show that a one-fund theorem holds and give an explicit expression for the equilibrium pricing kernel. If the underlying noise is generated by finitely many Bernoulli random walks, the equilibrium dynamics can be described by a system of coupled backward stochastic difference equations, which in the continuous-time limit becomes a multi-dimensional backward stochastic differential equation. If the market is complete in equilibrium, the system of equations decouples, but if not, one needs to keep track of the prices and continuation values of all agents to solve it. As an example we simulate option prices in the presence of stochastic volatility, demand pressure and short-selling constraints.JEL classification: C62, D52, D53

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Section: Simulation Of Option Prices In a Discrete Heston Modelmentioning

confidence: 99%

Equilibrium Pricing in Incomplete Markets Under Translation Invariant Preferences

et al. 2016

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“…Possible exogenous factors to consider are implied asset prices (Garcia et al 2003), the bid-ask spread, net buying pressure (Bollen and Whaley 2004), trading volume, other stock or index returns, and interest rates. Another strategy would be to include IV time series for options with fixed specifications as predictors.…”

Section: Predictor Variables and Factorsmentioning

confidence: 99%

Semi-parametric forecasts of the implied volatility surface using regression trees

Audrino

Colangelo

2009

Stat Comput

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We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model.

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“…The evidence in these papers is, however, based on the index put options market, which is very different from the single stock option market. The main buyers of OTM index puts are institutional investors, which use them for portfolio insurance (Bates, 2003;Bollen and Whaley, 2004;Lakonishok et al, 2007;Barberis and Huang 2008). Because institutional investors comprise around two-thirds of the total equity market capitalization (Blume and Keim, 2012), their option trading activity strongly impacts the pricing of put options (Bollen and Whaley, 2004) by making them expensive.…”

Section: Introductionmentioning

confidence: 99%

“…The main buyers of OTM index puts are institutional investors, which use them for portfolio insurance (Bates, 2003;Bollen and Whaley, 2004;Lakonishok et al, 2007;Barberis and Huang 2008). Because institutional investors comprise around two-thirds of the total equity market capitalization (Blume and Keim, 2012), their option trading activity strongly impacts the pricing of put options (Bollen and Whaley, 2004) by making them expensive. The results of Dierkes (2009) and Polkovnichenko and Zhao (2013) reiterate this evidence and suggest that overweighting of small probabilities partially explains the pricing puzzle present in the equity index option market.…”

Section: Introductionmentioning

confidence: 99%

“…Contrary to the index put market, trading activity in single stock calls is concentrated among individual investors (Bollen and Whaley, 2004;Lakonishok et al, 2007). Moreover, individual investors' demand for single stock options is speculative in nature (Lakonishok et al, 2007), whereas Mitton and Vorking (2007) provide important support to the link between preference for skewness and individual investor trading activity.…”

Section: Introductionmentioning

confidence: 99%

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