Author: Szymon BorakPublisher:
ISBN:
Category :
Languages : en
Pages : 149
Book Description
Dynamic Semiparametric Factor Models
Author: Szymon BorakDynamic Semiparametric Factor Models in Risk Neutral Density Estimation
Author: Time Varying Adaptive Copulae and Dynamic Semiparametic Factor Models with Applications in Finance
Author: Enzo GiacominiA Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics
Author: Matthias R. FenglerPublisher:
ISBN:
Category :
Languages : en
Pages : 43
Book Description
A primary goal in modelling the implied volatility surface (IVS) for pricing and hedging aims at reducing complexity. For this purpose one fits the IVS each day and applies a principal component analysis using a functional norm. This approach, however, neglects the degenerated string structure of the implied volatility data and may result in a modelling bias. We propose a dynamic semiparametric factor model (DSFM), which approximates the IVS in a finite dimensional function space. The key feature is that we only fit in the local neighborhood of the design points. Our approach is a combination of methods from functional principal component analysis and backfitting techniques for additive models. The model is found to have an approximate 10% better performance than a sticky moneyness model. Finally, based on the DSFM, we devise a generalized vega-hedging strategy for exotic options that are priced in the local volatility framework. The generalized vega-hedging extends the usual approaches employed in the local volatility framework.
A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics
Author: Matthias R. FenglerConfidence Bands in Quantile Regression and Generalized Dynamic Semiparametric Factor Models
Author: Song SongDynamic Semiparametric Factor Models in Risk Neutral Density Estimation
Author: Enzo GiacominiPublisher:
ISBN:
Category :
Languages : en
Pages : 19
Book Description
Dimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most prominent dimension reduction technique - functional principal components analysis - however, does not model time dependences embedded in functional data. In this paper we use dynamic semiparametric factor models (DSFM) to reduce dimensionality and analyse the dynamic structure of unknown random functions by means of inference based on their lower dimensional representation. We apply DSFM to estimate the dynamic structure of risk neutral densities implied by prices of option on the DAX stock index.
Dynamic Factor Models
Author: Siem Jan KoopmanPublisher: Emerald Group Publishing
ISBN: 1785603523
Category : Business & Economics
Languages : en
Pages : 685
Book Description
This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Author: Likai Chen
Author: Piotr Majer