Pricing and hedging volatility smile under multifactor interest rate models

Kuo, I‐Doun

doi:10.1007/s11156-010-0172-5

Cited by 4 publications

(4 citation statements)

References 36 publications

(55 reference statements)

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“…specifically, it is similar to that employed in previous studies (Bakshi et al 1997;Dumas et al 1998;Christoffersen and Jacobs 2004;Christoffersen et al 2006;Chen et al 2009;Andreou et al 2010;Kuo 2011;Mozumder et al 2012) and relies on a simultaneous equation procedure to minimize a price deviation function with respect to the unknown parameters. Market option prices, …”

Section: Methodology For Estimating and Evaluating The Modelsmentioning

confidence: 99%

“…(12). As Bakshi et al (1997) discuss (see also Bates 1996Bates , 2000Dumas et al 1998;Kuo 2011), the loss function used in Eq. (12) may force the estimation to assign more weight to relatively expensive options (which are usually those that are in-the-money with long maturities), and less to relatively cheap options, which are usually short-term, at-the-money and out-of-the-money options.…”

Section: Out-of-sample Pricing Performancementioning

confidence: 99%

“…First, there is abundant empirical evidence to suggest that the models' hedging performance of different models is virtually indistinguishable, and also that their hedging-based rankings are in sharp contrast to their out-of-sample pricing performance (see discussions in Dumas et al 1998;Chernov and Ghysels 2000;Kim 2009;Kuo 2011;Andreou et al 2008Andreou et al , 2010, making it more difficult for us to draw sensible conclusions. Second, Christoffersen and Jacobs (2004) suggest that the aforementioned ambiguity may be due to inappropriate choice of the loss function used to calibrate the models for hedging purposes, and suggest the best possible parameter estimates for a hedging exercise are likely to be obtained using a hedging based loss function.…”

Section: Hedging Analysismentioning

confidence: 99%

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Assessing the performance of symmetric and asymmetric implied volatility functions

Andreou

Charalambous

Martzoukos

2013

Rev Quant Finan Acc

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The nal publication is available at Springer via http://dx.doi.org/10.1007/s11156-013-0346-z Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis study examines several alternative symmetric and asymmetric model specifications of regression-based deterministic volatility models to identify the one that best characterizes the implied volatility functions of S&P 500 Index options in the period 1996-2009. We find that estimating the models with nonlinear least squares, instead of ordinary least squares, always results in lower pricing errors in both in-and out-of-sample comparisons. In-sample, asymmetric models of the moneyness ratio estimated separately on calls and puts provide the overall best performance. However, separating calls from puts violates the put-call-parity and leads to severe model mis-specification problems. Out-of-sample, symmetric models that use the logarithmic transformation of the strike price are the overall best ones. The lowest out-of-sample pricing errors are observed when implied volatility models are estimated consistently to the put-call-parity using the joint data set of out-ofthe-money options. The out-of-sample pricing performance of the overall best model is shown to be resilient to extreme market conditions and compares quite favorably with continuous-time option pricing models that admit stochastic volatility and random jump risk factors.JEL classification: G13, G14.

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Section: Methodology For Estimating and Evaluating The Modelsmentioning

confidence: 99%

Section: Out-of-sample Pricing Performancementioning

confidence: 99%

Section: Hedging Analysismentioning

confidence: 99%

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Assessing the performance of symmetric and asymmetric implied volatility functions

Andreou

Charalambous

Martzoukos

2013

Rev Quant Finan Acc

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“…Black's implied volatility in the final column shows that volatility smile and volatility term structure are observed in the Euribor options. Zeto (2002), Kuo and Paxson (2006), and Kuo and Lin (2007) test several IVDFs based on their choices with selected data. However, none of their tests consider the relationship between option volatility and its underlying price, shown in Figures 1 and 2.…”

Section: Datamentioning

confidence: 99%

Implied deterministic volatility functions: An empirical test for Euribor options

Kuo

Wang

2009

Journal of Futures Markets

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This study proposes the implied deterministic volatility function (IDVF) for the volatility as the function of moneyness and time in the Heath, Jarrow, and Morton (1992) model to price and hedge Euribor options across moneyness and maturities from 1 January 2003 to 31 December 2005. The IDVF models are extended to two-and three-factor models, indicating that they are potential candidates for interest rate risk management. Based on the criteria of in-sample fitting, prediction, and hedging, it is found that two-factor IDVF models provide the best in-sample and prediction performance, whereas three-factor IDVF models yield the best results for hedging. Correctly specified multifactor models with the volatility as the function of moneyness and time can replace inappropriate onefactor models.

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Three-point volatility smile classification: Evidence from the Warsow Stock Exchange during volatile summer 2011

García-Machado

Rybczyński

2015

Investigaciones Europeas de Dirección y Economía de la Empresa

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This paper studies the behavior of the smile in the Warsaw Stock Exchange (WSE) during the volatile summer of 2011.We investigate the volatility smile derived from liquid call and put options on the Polish WIG20 index which option series expired on September 2011. In this period, the polish index has dropped about 20% in two weeks time. By linear interpolation, implied volatilities for moneyness points needed were calculated, then we construct 355 smile curves for calls and puts options to study and make some kind of smile-types classification. We propose seventeen types-smiles which represent all possible cases of three points (three moneynesses) graphical patterns. This classification is made basing upon relationship higher/equal/lower values of implied volatility for each of three points. Furthermore, we distinguish the convexity of pattern. We can note that smiles, smirks and ups are convex in shape, while reversed ones and downs are concave functions.Este artículo analiza el comportamiento de la sonrisa de la volatilidad en la Bolsa de Varsovia (WSE) durante el volátil verano de 2011, derivada de las opciones más líquidas sobre el índice polaco WIG20, cuyas series expiraron en septiembre de 2011. En ese período, el índice había caído aproximadamente un 20% en tan sólo dos semanas. Mediante interpolación lineal construimos 355 curvas de sonrisas para poder estudiar una posible clasificación o tipología de las mismas. Proponemos 17 tipos de sonrisas, las cuales representan todos los casos posibles de tres monetizaciones de patrones gráficos. Esta clasificación se basa en la relación valores superiores/iguales/inferiores de la volatilidad implícita para cada uno de los tres puntos. Además, se distingue la convexidad de cada una. Destacamos que las sonrisas completas, las sonrisas asimétricas y las inclinadas hacia arriba son de forma convexa, mientras que las invertidas y las inclinadas hacia abajo son funciones cóncavas

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Pricing and hedging volatility smile under multifactor interest rate models

Abstract: Jump-diffusion models, HJM models, Volatility smile, Euribor options, G12, G13,

Cited by 4 publications

References 36 publications

Assessing the performance of symmetric and asymmetric implied volatility functions

Assessing the performance of symmetric and asymmetric implied volatility functions

Implied deterministic volatility functions: An empirical test for Euribor options

Three-point volatility smile classification: Evidence from the Warsow Stock Exchange during volatile summer 2011

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