A Model for Multivariate Non-Negative Valued Processes in Financial Econometrics

Cipollini, Fabrizio; Engle, Robert F.; Gallo, Giampiero M.

doi:10.2139/ssrn.1333869

“…The idea of using a measurement equation to tie the realized measure to the latent volatility goes back to Takahashi et al (2009), who used it in the context of stochastic volatility models. Additional MEM specifications have been explored and developed in Cipollini et al (2009) and Brownless and Gallo (2010).…”

Section: Introductionmentioning

confidence: 99%

Exponential GARCH Modeling with Realized Measures of Volatility

Hansen

¹

,

Huang

²

2015

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We introduce the Realized Exponential GARCH model that can utilize multiple realized volatility measures for the modeling of a return series. The model specifies the dynamic properties of both returns and realized measures, and is characterized by a flexible modeling of the dependence between returns and volatility. We apply the model to DJIA stocks and an exchange traded fund that tracks the S&P 500 index and find that specifications with multiple realized measures dominate those that rely on a single realized measure. The empirical analysis suggests some convenient simplifications and highlights the advantages of the new specification.

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“…In such a specification, the approach proposed in Cipollini et al (2007) to estimate the system with univariate model estimation adopting Gamma marginals, and then using copula functions to retrieve the contemporaneous correlation among innovations would not be applicable. The non-diagonality of β 1 is shown in the empirical applications to be supported by the data.…”

Section: Discussionmentioning

confidence: 99%

“…Examples are: volatility forecasting using different measures; volatility spillovers for studying contagion among markets; order execution dynamics in orderdriven markets; joint dynamics of duration, volume and volatility for the same asset (see Cipollini et al (2007) and references therein for details about these examples). This motivates the following multivariate extension of the MEM.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semiparametric Vector Mem

Cipollini

¹

,

Engle

²

,

Gallo

³

2012

J of Applied Econometrics

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In financial time series analysis we encounter several instances of non-negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model).Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non-negative valued innovation process.A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks. * This paper develops some ideas introduced in Cipollini, Engle and Gallo (2006) where estimation was based in the framework of estimating functions. Without implicating, we acknowledge comments by Nour Meddahi and Kevin Sheppard which led us to present the Estimating Functions approach in a more familiar GMM notation. We acknowledge financial support from the Italian MIUR under grant PRIN 2006131140 004.

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“…Multivariate distributions defined on the non-negative orthant are often not sufficiently flexible. Furthermore, sometimes they are defined via the characteristics function without an explicit pdf, thus complicating considerably parameter estimation (see, as an example, the discussion on the multivariate gamma in Cipollini et al (2007) and the work of Ahoniemi and Lanne (2007)). These are the main reasons that motivate recurring to copulas.…”

Section: Introductionmentioning

confidence: 99%

Semiparametric Vector MEM

Cipollini

¹

,

Engle

²

,

Gallo

³

2008

Self Cite

View full text Add to dashboard Cite

In financial time series analysis we encounter several instances of non-negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model).Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non-negative valued innovation process.A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks. * This paper develops some ideas introduced in Cipollini, Engle and Gallo (2006) where estimation was based in the framework of estimating functions. Without implicating, we acknowledge comments by Nour Meddahi and Kevin Sheppard which led us to present the Estimating Functions approach in a more familiar GMM notation. We acknowledge financial support from the Italian MIUR under grant PRIN 2006131140 004.

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A Model for Multivariate Non-Negative Valued Processes in Financial Econometrics

Cited by 27 publications

References 21 publications

Exponential GARCH Modeling with Realized Measures of Volatility

Exponential GARCH Modeling with Realized Measures of Volatility

Semiparametric Vector Mem

Semiparametric Vector MEM

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