Author: Garcia, RenéPublisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 11
Book Description
A Note on Hedging in ARCH and Stochastic Volatility Option Pricing Models
Author: Garcia, RenéPublisher: Montréal : CIRANO
ISBN:
Category :
Languages : en
Pages : 11
Book Description
A note on edging in ARCH and stochastic volatility option pricing models
Author: René GarciaPricing and Hedging Index Options Under Stochastic Volatility
Author: Saikat NandiPublisher:
ISBN:
Category : Hedging (Finance)
Languages : en
Pages : 48
Book Description
Pricing and Hedging Exotic Options in Stochastic Volatility Models
Author: Zhanyu ChenOption Hedging and Valuation Under Stochastic Volatility
Author: Joshua RosenbergPublisher:
ISBN:
Category : Foreign exchange rates
Languages : en
Pages : 292
Book Description
Hedging Options in a GARCH Environment
Author: Robert F. EnglePublisher:
ISBN:
Category : Hedging (Finance)
Languages : en
Pages : 52
Book Description
This paper develops a methodology for testing the term structure of volatility forecasts derived from stochastic volatility models, and implements it to analyze models of S & P 500 index volatility. Volatility models are compared by their ability to hedge options positions sensitive to the term structure of volatility. Overall, the most effective hedge is a Black-Scholes (BS) delta-gamma hedge, while the BS delta-vega hedge is the least effective. The most successful volatility hedge is GARCH components delta-gamma, suggesting that the GARCH components estimate of the term structure of volatility is most accurate. The success of the BS delta-gamma hedge may be due to mispricing in the options market over the sample period.
Estimation of a Stochastic Volatility Model Using Pricing and Hedging Information
Author: Jason FinkPublisher:
ISBN:
Category :
Languages : en
Pages : 23
Book Description
Estimation of option pricing models in which the underlying asset exhibits stochastic volatility presents complicated econometric questions. One such question, thus far unstudied, is whether the inclusion of information derived from hedging relationships implied by an option pricing model may be used in conjunction with pricing information to provide more reliable parameter estimates than the use of pricing information alone. This paper estimates, using a simple least-squares procedure, the stochastic volatility model of Heston (1993), and includes hedging information in the objective function. This hedging information enters the objective function through a weighting parameter that is chosen optimally within the model. With the weight appropriately chosen, we find that incorporating the hedging information reduces both the out-of-sample hedging and pricing errors associated with the Heston model.
A Note on Hedging with Local and Stochastic Volatility Models
Author: Fabio MercurioPublisher:
ISBN:
Category :
Languages : en
Pages : 13
Book Description
The behaviour of a smile model when applied to hedging should be consistent with market evidence that asset prices and market smiles move in the same direction (Hagan et al. 2002). Local volatility models are criticized because not consistent with this desired behaviour, and this has been an important driver towards the use of stochastic volatility models.In this work we perform a simple analysis showing that, if we take into account explicitly the correlation between stochastic volatility and underlying asset which is typical of the most common stochastic volatility models, the hedging behaviour of stochastic volatility models does not always conform with the desired behaviour of a smile model in hedging.With further simple tests we show that the behaviour of local volatility and stochastic volatility models calibrated to market skew is less different than assumed in current market wisdom. Both approaches, when used consistently with model assumptions, do not show the desired behaviour in hedging, while for both models the desired behaviour is obtained in market practice by hedging techniques which are not fully consistent with rigorous model assumptions.
Pricing and Hedging Long-Term Options
Author: Zhiwu ChenPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Recent empirical studies find that once an option pricing model has incorporated stochastic volatility, allowing interest rates to be stochastic does not improve pricing or hedging any further while adding random jumps to the modeling framework only helps the pricing of extremely short-term options but not the hedging performance. Given that only options of relatively short terms are used in existing studies, this paper addresses two related questions: Do long-term options contain different information than short-term options? If so, can long-term options better differentiate among alternative models? Our inquiry starts by first demonstrating analytically that differences among alternative models usually do not surface when applied to short term options, but do so when applied to long-term contracts. For instance, within a wide parameter range, the Arrow-Debreu state price densities implicit in different stochastic-volatility models coincide almost everywhere at the short horizon, but diverge at the long horizon. Using regular options (of less than a year to expiration) and LEAPS, both written on the Samp;P 500 index, we find that short- and long-term contracts indeed contain different information and impose distinct hurdles on any candidate option pricing model. While the data suggest that it is not as important to model stochastic interest rates or random jumps (beyond stochastic volatility) for pricing LEAPS, incorporating stochastic interest rates can nonetheless enhance hedging performance in certain cases involving long-term contracts.
Empirical Performance of Alternative Option Pricing Models
Author: Zhiwu ChenPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Substantial progress has been made in extending the Black-Scholes model to incorporate such features as stochastic volatility, stochastic interest rates and jumps.On the empirical front, however, it is not yet known whether and by how much each generalized feature will improve option pricing and hedging performance. This paper fills this gap by first developing an implementable option model in closed form that allows volatility, interest rates and jumps to bestochastic and that is parsimonious in the number of parameters. The model includes many known ones as special cases. Delta-neutral and single-instrument minimum-variance hedging strategies are derived analytically. Using Samp;P 500 options, we examine a set of alternative models from three perspectives: (1) internal consistency of implied parameters/volatility with relevant time-series data, (2)out-of-sample pricing and (3) hedging performance. The models of focus include the benchmark Black-Scholes formula and the ones that respectively allow for (i) stochastic volatility, (ii) both stochastic volatility and stochastic interest rates, and (iii) stochastic volatility and jumps.Overall, incorporating both stochastic volatility and random jumps produces the best pricing performance and the most internally-consistent implied-volatility process. Its implied volatility does not quot;smilequot; across moneyness. But, for hedging, adding either jumps or stochastic interest rates does not seem to improve performance any further once stochastic volatility is taken into account.