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Angelo Barbieri

Angelo Barbieri

Head of the Valuation Research Group

Angelo Barbieri is Managing Director and Head of the Valuation Research group. He oversees all modelling aspects related to valuation and risk assessment of derivatives structures supported by MSCI risk and FEA products. Previously, he was the Head of Financial Engineering at Financial Engineering Associates (FEA); FEA was acquired by Barra in late 2002. Angelo holds a Ph.D. in Physics from the University of California at Santa Barbara.

Research and Insights

Articles by Angelo Barbieri

    Model Insight - Barra Equity Volatility Futures Model EVX1 - June 2011

    Research Report | Jun 15, 2011 | Peter Shepard , Alexei Gladkevich , Angelo Barbieri , Lisa Goldberg

    In this paper, we present a daily factor model that forecasts daily volatility of variance for VIX Futures Contracts.

    Model Insight - Barra Commodity Model COM2 - March 2011

    Research Report | Mar 31, 2011 | Tsvetan Stoyanov , Angelo Barbieri

    This Model Insight explains the Barra Commodity Model (COM2), which covers a broader set of commodities, attributes risk to an expanded set of factors, and provides more accurate risk estimates along the commodity term structures.

    Modeling Value at Risk with Factors

    Research Report | Oct 1, 2009 | John Fox , Kelly Chang , Angelo Barbieri , Vladislav Dubikovsky

    Factor models are standards in investment management. For decades, Barra factor models have provided valuable risk forecasts and inputs for the portfolio construction process. Most uses of factor models have targeted longer horizons of months or years. However, we demonstrate in this paper that factor models can also provide accurate risk forecasts for shorter horizons of one to ten days. Furthermore, factor models have the advantage of explaining risk sources and providing consistency in...

    Central Limits and Financial Risk

    Research Report | Sep 1, 2009 | Vladislav Dubikovsky , Alexei Gladkevich , Angelo Barbieri , Lisa Goldberg

    Systematic model bias has been implicated in the global recession that began in 2007, and this bias can be traced back to assumptions about the normality of data. Nonetheless, the normal distribution continues to play a foundational role in quantitative finance. One reason for this is that the normal often emerges, without prompting, as the distribution of sums or averages of large collections of random variables. Precise statements of this miracle are known as Central Limit Theorems, and...