Market calibration under a long memory stochastic volatility model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F16%3A43930083" target="_blank" >RIV/49777513:23520/16:43930083 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/1350486X.2017.1279977" target="_blank" >http://dx.doi.org/10.1080/1350486X.2017.1279977</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/1350486X.2017.1279977" target="_blank" >10.1080/1350486X.2017.1279977</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Market calibration under a long memory stochastic volatility model

Popis výsledku v původním jazyce

In this article, we study a long memory stochastic volatility model (LSV), under which stock prices follow a jump-diffusion stochastic process and its stochastic volatility is driven by a continuous-time fractional process that attains a long memory. LSV model should take into account most of the observed market aspects and unlike many other approaches, the volatility clustering phenomenon is captured explicitly by the long memory parameter. Moreover, this property has been reported in realized volatility time-series across different asset classes and time periods. In the first part of the article, we derive an alternative formula for pricing European securities. The formula enables us to effectively price European options and to calibrate the model to a given option market. In the second part of the article, we provide an empirical review of the model calibration. For this purpose, a set of traded FTSE 100 index call options is used and the long memory volatility model is compared to a popular pricing approach - the Heston model. To test stability of calibrated parameters and to verify calibration results from previous data set, we utilize multiple data sets from NYSE option market on Apple Inc. stock.

Název v anglickém jazyce

Market calibration under a long memory stochastic volatility model

Popis výsledku anglicky

In this article, we study a long memory stochastic volatility model (LSV), under which stock prices follow a jump-diffusion stochastic process and its stochastic volatility is driven by a continuous-time fractional process that attains a long memory. LSV model should take into account most of the observed market aspects and unlike many other approaches, the volatility clustering phenomenon is captured explicitly by the long memory parameter. Moreover, this property has been reported in realized volatility time-series across different asset classes and time periods. In the first part of the article, we derive an alternative formula for pricing European securities. The formula enables us to effectively price European options and to calibrate the model to a given option market. In the second part of the article, we provide an empirical review of the model calibration. For this purpose, a set of traded FTSE 100 index call options is used and the long memory volatility model is compared to a popular pricing approach - the Heston model. To test stability of calibrated parameters and to verify calibration results from previous data set, we utilize multiple data sets from NYSE option market on Apple Inc. stock.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

<a href="/cs/project/GA14-11559S" target="_blank" >GA14-11559S: Analýza frakcionálních modelů stochastické volatility a jejich implementace v gridu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Applied Mathematical Finance

ISSN

1350-486X

e-ISSN

—

Svazek periodika

23

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

21

Strana od-do

"323?343"

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85010698375