Volatility Components, Affine Restrictions, and Nonnormal Innovations

Christoffersen, Peter; Dorion, Christian; Jacobs, Kris; Wang, Yintian

doi:10.1198/jbes.2009.06122

Cited by 83 publications

(26 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This added flexibility performs well in tests, so it once again seems to suggest that there could be a change in the level and rate of mean reversion for the quantile process. In Section 2.3 we propose a two-component model in order to avoid the sudden shifts that are brought about by threshold crossing, and to achieve a smoother series that could potentially save transaction costs.To this end, we focus on extending the CAViaR models based on the approach of Engle and Lee (1999), which has been successfully applied to option pricing by Christoffersen et al (2008) and Christoffersen et al (2010).…”

Section: Another Threshold-based Nonlinear Effects Models For Comparisonmentioning

confidence: 99%

Value at Risk Models with Long Memory Features and Their Economic Performance

Oberoi

Mitrodima

2015

SSRN Journal

View full text Add to dashboard Cite

We study alternative dynamics for Value at Risk (VaR) that incorporate a slow moving component and information on recent aggregate returns in established quantile (auto) regression models. These models are compared on their economic performance, and also on metrics of first-order importance such as violation ratios. By better economic performance, we mean that changes in the VaR forecasts should have a lower variance to reduce transaction costs and should lead to lower exceedance sizes without raising the average level of the VaR. We find that, in combination with a targeted estimation strategy, our proposed models lead to improved performance in both statistical and economic terms.

show abstract

Section: Another Threshold-based Nonlinear Effects Models For Comparisonmentioning

confidence: 99%

Value at Risk Models with Long Memory Features and Their Economic Performance

Oberoi

Mitrodima

2015

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Its use in GARCH or EGARCH models considerably improves forecasts [7,22,28]. Another well-known family of functions is the generalized error distribution (GED).Christoffersen et al [12] show its nice fitting characteristics on daily stock returns for different GARCH-type models. Some studies also focus on the Generalized Hyperbolic (GH) distributions, a five-parameters family of density functions, introduced first by Barndorff-Nielsen [3].…”

Section: Introductionmentioning

confidence: 99%

“…Since the 2008 financial crisis, the literature faces a renewed interest in the choice of an adequate error distribution, able to capture the skewness and excess kurtosis of stochastic processes [see, among others 8,12,35,37]. In this article, we propose a robust methodology to select a distribution family in a classical multiplicative heteroscedastic model.…”

Section: Introductionmentioning

confidence: 99%

A robust statistical approach to select adequate error distributions for financial returns

Hambuckers

Heuchenne

2016

Journal of Applied Statistics

View full text Add to dashboard Cite

In this article, we propose a robust statistical approach to select an appropriate error distribution, in a classical multiplicative heteroscedastic model. In a first step, unlike to the traditional approach, we don't use any GARCH-type estimation of the conditional variance. Instead, we propose to use a recently developed nonparametric procedure [31]: the Local Adaptive Volatility Estimation (LAVE). The motivation for using this method is to avoid a possible model misspecification for the conditional variance. In a second step, we suggest a set of estimation and model selection procedures (Berk-Jones tests, kernel density-based selection, censored likelihood score, coverage probability) based on the so-obtained residuals. These methods enable to assess the global fit of a set of distributions as well as to focus on their behavior in the tails, giving us the capacity to map the strengths and weaknesses of the candidate distributions. A bootstrap procedure is provided to compute the rejection regions in this semiparametric context. Finally, we illustrate our methodology throughout a small simulation study and an application on three time series of daily returns (UBS stock returns, BOVESPA returns and EUR/USD exchange rates).

show abstract

“…In another parameter estimation method, the minimum of quadratic sum of the bias between option market price and theoretical price is used to set up an objective function. However, this method has no advantage against the GARCH option pricing model (refer to [9][10][11][12]). It is a valuable research direction to carry out GARCH option pricing analysis based on historical price information of options.…”

Section: Introductionmentioning

confidence: 99%

“…Much research progress has been made in this aspect. Siu et al (2004) [8] and Christoffersen et al (2010) [9] built a nonnormal distribution-based GARCH option pricing model. Elliott et al (2006) [10] introduced Markov-switching model into option pricing.…”

Section: Introductionmentioning

confidence: 99%

Lévy Process-Driven Asymmetric Heteroscedastic Option Pricing Model and Empirical Analysis

Zhang

Zheng

Zhang

et al. 2018

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

This paper describes the peak, fat tail, and skewness characteristics of asset price via a Lévy process. It applies asymmetric GARCH model to depict asset price's random volatility characteristics and builds a GARCH-Lévy option pricing model with random jump characteristics. It also uses circular maximum likelihood estimation technology to improve the stability of model parameter estimation. In order to test the model's pricing results, we use Hong Kong Hang Seng Index (HSI) price data and its option data to carry out empirical studies. Results prove that the pricing bias of EGARCH-Lévy model is lower than that of standard HestonNandi (HN) model in the financial industry. For short-term, middle-term, and long-term European-style options, the pricing error of EGARCH-Lévy model is the lowest.

show abstract

Volatility Components, Affine Restrictions, and Nonnormal Innovations

Cited by 83 publications

References 25 publications

Value at Risk Models with Long Memory Features and Their Economic Performance

Value at Risk Models with Long Memory Features and Their Economic Performance

A robust statistical approach to select adequate error distributions for financial returns

Lévy Process-Driven Asymmetric Heteroscedastic Option Pricing Model and Empirical Analysis

Contact Info

Product

Resources

About