Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

Kumiega, Andrew; Neururer, Thaddeus; Vliet, Ben Van

doi:10.1016/j.qref.2011.03.002

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…After May 2010, the volatility of IC1 had calmed down as indicated by a decline in its level to around 1.0, showing that the concern shifted from the risk in periphery to the 24 We apply EGARCH assuming the leverage effect, same with the preceding studies such as Kumiega et al (2011). Note that the direction of the leverage in the CDS case is supposed to be opposite to the stock case; that is, CDS volatility is expected to extend more after the CDS spread increases than before it decreases.…”

Section: Volatility Of the Icsmentioning

confidence: 99%

“…In this paper, our objective is to identify the factors driving the changes in CDS spreads, separating the decoupling factor and the co-movement factor. Following Kumiega et al (2011) andGarcía-Ferrer et al (2012), in this paper we employ independent component analysis (ICA) for that purpose. Although similar to factor analysis (FA) and PCA in that it is a linear model, ICA differs from these two traditional models in that its objective is to find a linear representation for non-Gaussian variables so that the components identified are statistically independent from the other components identified.…”

Section: Introductionmentioning

confidence: 99%

“…The ICA-based factor model has several advantages in analysis. As shown by Kumiega et al (2011), the dynamic volatility and correlation can be calculated more easily by the ICA-GARCH model than by the other methods. Also, the factors obtained can be utilized for analysis detecting the effect of policy measures by the authorities such as the ECB.…”

Section: Introductionmentioning

confidence: 99%

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Factor decomposition of the Eurozone sovereign CDS spreads

Fabozzi

Giacometti

Tsuchida

2016

Journal of International Money and Finance

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Section: Volatility Of the Icsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Factor decomposition of the Eurozone sovereign CDS spreads

Fabozzi

Giacometti

Tsuchida

2016

Journal of International Money and Finance

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“…Recently, a number of authors have used ICA to study multivariate time series (e.g., see Chen et al [2008], Chen et al [2010], and Kumiega et al [2011]). 8 The combination of ICA and GARCH 9 methods is generally referred to as ICA-GARCH.…”

Section: Connection To Independent Component Analysismentioning

confidence: 99%

Implied ICA: Factor Extraction and MultiassetDerivative Pricing

Kumiega

Neururer

Vliet

2012

JOD

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In this article, we present a multifactor stochastic volatility framework for pricing European options based on independent component analysis. We fit this model to empirical data on exchange-traded options in order to create a consistent pricing and hedging mechanism for multiasset derivatives. Our model then prices exchange-traded fund options. E quity, foreign exchange (FX), and energy trading desks at financial firms trade a wide array of vanilla and exotic options, both on individual underlying assets and baskets of assets, such as equity indexes. Given that individual assets are components of baskets and indexes, a big challenge these firms face is to price and risk-manage their portfolios of option positions in a consistent manner. Many firms use unique pricing models for each type of derivative, making it difficult to aggregate the different risk sensitivities into a single value. Although all options-including shortdated options, LEAPS 1 , combination options, and multiasset options, such as best-of-twos, rainbows, and basket options-have Greeks 2 and implied volatilities, combining these risk measures across a portfolio in a standardized way is not easily done.Pricing and hedging multiasset options are complex tasks. The number of parameters required may be overwhelming. This is true even when employing simple models that assume geometric Brownian motion and constant correlations between assets. However, proper fitting to a model that prices options on both individual assets and baskets requires the addition of more complex structures, such as stochastic volatility, jumps, or nonconstant pair-wise correlations. In addition, many multiasset derivatives are unique or thinly traded, so no historical data exist for the development of pricing or hedging heuristics.In this article, we develop a model that addresses the complexities of multiasset option pricing. We use empirical data on listed equity options to develop our model and then test the model against exchange-traded fund (ETF) option data, a proxy for basket options. The lessons learned from our ETF example can be applied to more complex derivatives and their portfolios. At the outset, we presuppose four goals under which we develop our framework: 1. The model should incorporate stochastic volatility. For the single-asset case, the Hull-White [1987] and Heston [1993] stochastic volatility models demonstrate pricing improvement relative to the Black-Scholes [1973] base case. Any multivariate model should incorporate stochastic volatility for each asset. 2. The model should allow for stochastic changes in correlation both between assets and between an asset and its variance. Stochastic corre-

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“…The works that we considered more relevant in the context of our research have used ICA for extracting the following: the underlying factors explaining the stock returns in Japan [2], Hong Kong [4], Italy [9], the USA [24] and during the crisis period [25]; the relevant factors driving the movements from implied volatility surfaces of index options [1]; the factors driving the movements of a term structure on interest rates in Germany [35]; the factors driving spot rate curve movements in the USA [3]; the factors moving the returns for real estate investment trusts in the USA [30], and for estimating the factor model of returns for the USA Thrift Saving Plan Funds [37], and the factors for pricing multiasset derivatives [26].…”

Section: Introductionmentioning

confidence: 99%

Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange

Cortés¹,

Porras²,

Monte³

2018

CyS

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Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.

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Independent component analysis for realized volatility: Analysis of the stock market crash of 2008

Cited by 11 publications

References 21 publications

Factor decomposition of the Eurozone sovereign CDS spreads

Factor decomposition of the Eurozone sovereign CDS spreads

Implied ICA: Factor Extraction and MultiassetDerivative Pricing

Extraction of the Underlying Structure of Systematic Risk from Non-Gaussian Multivariate Financial Time Series Using Independent Component Analysis: Evidence from the Mexican Stock Exchange

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