Giampiero M. Gallo

Prof. Dr. Giampiero M. Gallo

NYU in Florence
+1
orcid

Research Focus & Metrics

Academic Career Length

40Years (1987-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2006 in Journal of Econometrics
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Journal Article
Closed access
RERobert F. Engle
Robert F. Engle
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo

1 December 2006 in Computational Statistics & Data Analysis
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Journal Article
Closed access
Christian BrownleesChristian Brownlees
Christian Brownlees
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo

27 July 2009 in Journal of Financial Econometrics
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Journal Article
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PDF file
Christian BrownleesChristian Brownlees
Christian Brownlees
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo
In this paper we address the issue of forecasting Value–at–Risk (VaR) using different volatility measures: realized volatility, bipower realized volatility, two-scales realized volatility, realized kernel, as well as the daily range. We propose a dynamic model with a flexible trend specification bonded with a penalized maximum likelihood estimation strategy: the P-spline multiplicative error model. Exploiting ultra-high-frequency data (UHFD) volatility measures, VaR predictive ability is considerably improved upon relative to a baseline GARCH but not so relative to the range; there are gains from modeling volatility trends and from using realized kernels that are robust to dependent microstructure noise.

1 February 2012 in Review of Economics and Statistics
Verified
Journal Article
Closed access
RERobert F. Engle
Robert F. Engle
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo
MVMargherita Velucchi
Margherita Velucchi

1 February 2008 in Computational Statistics & Data Analysis
Verified
Journal Article
Closed access
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo
EOEdoardo Otranto
Edoardo Otranto

1 July 2010 in Journal of Financial Econometrics
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Journal Article
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Christian BrownleesChristian Brownlees
Christian Brownlees
FCFabrizio Cipollini
Fabrizio Cipollini
Giampiero M. GalloGiampiero M. Gallo
Giampiero M. Gallo
The explosion of algorithmic trading has been one of the most pro-minent recent trends in the financial industry. Algorithmic trading consists of automated trading strategies that attempt to minimize transaction costs by optimally placing orders. The key ingredient of many of these strategies are intra-daily volume proportions forecasts. This work proposes a dynamic model for intra-daily volumes that captures salient features of the series such as time series dependence, intra-daily periodicity and volume asymmetry. Moreover, we introduce loss functions for the evaluation of proportion forecasts which retains both an operational and information theoretic interpretation. An empirical application on a set of widely traded index Exchange Traded Funds shows that the proposed methodology is able to significantly outperform common forecasting methods and delivers more precise predictions for Volume Weighted Average Price trading. (JEL: C22, C51, C53, G12)