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Do stock prices and volatility jump? Reconciling evidence from spot and option prices
, 2001
"... This paper studies the empirical performance of jumpdiffusion models that allow for stochastic volatility and correlated jumps affecting both prices and volatility. The results show that the models in question provide reasonable fit to both option prices and returns data in the insample estimation ..."
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Cited by 97 (2 self)
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This paper studies the empirical performance of jumpdiffusion models that allow for stochastic volatility and correlated jumps affecting both prices and volatility. The results show that the models in question provide reasonable fit to both option prices and returns data in the insample estimation period. This contrasts previous findings where stochastic volatility paths are found to be too smooth relative to the option implied dynamics. While the models perform well during the high volatility estimation period, they tend to overprice long dated contracts outofsample. This evidence points towards a too simplistic specification of the mean dynamics of volatility.
Estimating Functions for Discretely Sampled DiffusionType Models. Chapter of the Handbook of financial econometrics, AitSahalia and Hansen eds. http://home.uchicago.edu/ lhansen/handbook.htm Birgé
 in Festschrift for Lucien Le Cam: Research Papers in Probability and Statistics
, 2004
"... Estimating functions provide a general framework for finding estimators and studying their properties in many different kinds of statistical models, including stochastic process models. An estimating function is a function of the data as well as of the parameter to be estimated. An estimator is obta ..."
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Cited by 26 (9 self)
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Estimating functions provide a general framework for finding estimators and studying their properties in many different kinds of statistical models, including stochastic process models. An estimating function is a function of the data as well as of the parameter to be estimated. An estimator is obtained by equating the estimating function to zero and solving the resulting
Explaining the level of credit spreads: optionimplied jump risk premia in a firm value model
, 2005
"... Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jumpdiffusion firm value model to assess the level of credit spreads that is generated by optionimplied jump risk premia. In our compound option pricing model, an equity index ..."
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Cited by 22 (2 self)
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Prices of equity index put options contain information on the price of systematic downward jump risk. We use a structural jumpdiffusion firm value model to assess the level of credit spreads that is generated by optionimplied jump risk premia. In our compound option pricing model, an equity index option is an option on a portfolio of call options on the underlying firm values. We calibrate the model parameters to historical information on default risk, the equity premium and equity return distribution, and S&P 500 index option prices. Our results show that a model without jumps fails to fit the equity return distribution and option prices, and generates a low outofsample prediction for credit spreads. Adding jumps and jump risk premia improves the fitofthe model in terms of equity and option characteristics considerably and brings predicted credit spread levels much closer to observed levels.
A Portfolio Perspective on Option Pricing Anomalies
, 2003
"... We empirically study the economic beneÞts of giving investors access to index options in the context of the standard asset allocation problem. We solve the portfolio problem with a ßexible empirical methodology that does not rely on speciÞc assumptions about the process of the underlying equity inde ..."
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Cited by 21 (2 self)
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We empirically study the economic beneÞts of giving investors access to index options in the context of the standard asset allocation problem. We solve the portfolio problem with a ßexible empirical methodology that does not rely on speciÞc assumptions about the process of the underlying equity index, and that can handle market frictions and a variety of preference speciÞcations. Using data on S&P 500 index options (19872001) we consider returns on OTM put options and ATM straddles. CRRA investors Þnd it always optimal to short put options and straddles, regardless of their risk aversion. The economic and statistical signiÞcance of the option positions is large and robust to corrections for transaction costs, margin requirements, and Peso problems. These results may be interpreted as evidence for a substantial negative volatility risk premium and a positive jump risk premium, in line with the recent option pricing literature. Many nonexpected utility investors (loss aversion, cumulative prospect theory and disappointment aversion) also optimally hold short positions in puts and straddles. Only for cumulative prospect theory, where loss aversion is combined with distorted probability assessments, can we obtain positive portfolio weights for put options and straddles.
Individual stockoption prices and credit spreads’, Working Paper
, 2006
"... This paper introduces measures of volatility and jump risk that are based on individual stock options to explain credit spreads on corporate bonds. Implied volatilities of individual options are shown to contain important information for credit spreads and improve on both implied volatilities of ind ..."
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Cited by 15 (2 self)
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This paper introduces measures of volatility and jump risk that are based on individual stock options to explain credit spreads on corporate bonds. Implied volatilities of individual options are shown to contain important information for credit spreads and improve on both implied volatilities of index options and on historical volatilities when explaining the crosssectional and timeseries variation in a panel of corporate bond spreads. Both the level of individual implied volatilities and the impliedvolatility skew matter for credit spreads. The empirical estimates are in line with the coefficients predicted by a theoretical structural firm value model. Importantly, detailed principal component analysis shows that our newly constructed determinants of credit spreads reverse the finding in the literature that structural models leave a large part of the variation in credit spreads unexplained. Furthermore, our results indicate that optionmarket liquidity has a spillover effect on the shortmaturity corporate bond market, and we show that individual option prices contain information on the likelihood of rating migrations.
Volatility Comovement: A Multifrequency Approach
, 2004
"... We implement a multifrequency volatility decomposition of three exchange rates and show that components with similar durations are strongly correlated across series. This motivates a bivariate extension of the MarkovSwitching Multifractal (MSM) introduced in Calvet and Fisher (2001, 2004). Bivariat ..."
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Cited by 15 (2 self)
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We implement a multifrequency volatility decomposition of three exchange rates and show that components with similar durations are strongly correlated across series. This motivates a bivariate extension of the MarkovSwitching Multifractal (MSM) introduced in Calvet and Fisher (2001, 2004). Bivariate MSM is a stochastic volatility model with a closedform likelihood. Estimation can proceed by ML for state spaces of moderate size, and by simulated likelihood via a particle filter in highdimensional cases. We estimate the model and confirm its main assumptions in likelihood ratio tests. Bivariate MSM compares favorably to a standard multivariate GARCH both in and outofsample. We extend the model to multivariate settings with a potentially large number of assets by proposing a parsimonious multifrequency factor structure.
OptionImplied Correlations and the Price of Correlation Risk, Working paper
, 2012
"... Motivated by extensive evidence that stockreturn correlations are stochastic, we analyze whether the risk of correlation changes (affecting diversification benefits) may be priced. We propose a direct and intuitive test by comparing optionimplied correlations between stock returns (obtained by com ..."
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Cited by 9 (0 self)
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Motivated by extensive evidence that stockreturn correlations are stochastic, we analyze whether the risk of correlation changes (affecting diversification benefits) may be priced. We propose a direct and intuitive test by comparing optionimplied correlations between stock returns (obtained by combining index option prices with prices of options on all index components) with realized correlations. Our parsimonious model shows that the substantial gap between average implied (38% for S&P500 and 44% for DJ30) and realized correlations (31% and 34%, respectively) is direct evidence of a large negative correlation risk premium. Empirical implementation of our model also indicates that the index variance risk premium can be attributed to the high price of correlation risk. Finally, we provide evidence that optionimplied correlations have remarkable predictive power for future stock market returns.
A Martingale Approach for Testing Diffusion Models Based on Infinitesimal Operator
, 2009
"... I develop an omnibus specification test for diffusion models based on the infinitesimal operator instead of the already extensively used transition density. The infinitesimal operatorbased identification of the diffusion process is equivalent to a "martingale hypothesis" for the new processes transf ..."
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I develop an omnibus specification test for diffusion models based on the infinitesimal operator instead of the already extensively used transition density. The infinitesimal operatorbased identification of the diffusion process is equivalent to a "martingale hypothesis" for the new processes transformed from the original diffusion process. The transformation is via the celebrated "martingale problems". My test procedure is to check the "martingale hypothesis" via a multivariate generalized spectral derivative based approach which enjoys many good properties. The infinitesimal operator of the diffusion process enjoys the nice property of being a closedform expression of drift and diffusion terms. This makes my test procedure capable of checking both univariate and multivariate diffusion models and particularly powerful and convenient for the multivariate case. In contrast checking the multivariate diffusion models is very difficult by transition densitybased methods because transition density does not have a closedform in general. Moreover, different transformed martingale processes contain different separate information about the drift and diffusion terms and their interactions. This motivates us to suggest a separate inferencebased test procedure to explore the sources when rejection of a parametric form happens. Finally, simulation studies are presented and possible future researches using the infinitesimal operatorbased martingale characterization are discussed.
BIS Working Papers No 191 Explaining the Level of Credit Spreads: OptionImplied Jump Risk Premia in a Firm Value Model
, 2005
"... for International Settlements, and from time to time by other economists, and are published by the Bank. The views expressed in them are those of their authors and not necessarily the views of the BIS. Copies of publications are available from: ..."
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for International Settlements, and from time to time by other economists, and are published by the Bank. The views expressed in them are those of their authors and not necessarily the views of the BIS. Copies of publications are available from:
DiffusionType Models
, 2004
"... The theory of estimating functions for diffusiontype models is reviewed with a view to applications in finance. Several types of estimating functions are presented, including some explicit estimating functions. Special attention is given to martingale estimating functions. Also predictionbased est ..."
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The theory of estimating functions for diffusiontype models is reviewed with a view to applications in finance. Several types of estimating functions are presented, including some explicit estimating functions. Special attention is given to martingale estimating functions. Also predictionbased estimating functions are considered. Large sample asymptotics is discussed in detail. The theory of optimal estimating functions is presented and applied to diffusion models. The classical theory as well as the new theory of small ∆optimality are considered. The focus is on diffusion models and stochastic volatility models, but a simple diffusion with jumps is treated too. Several examples are given. 1 1