Event URL: https://newyork.qwafafew.org/event/feb26
Bourbon Street Bar & Grill,
346 W 46th St., between 8th/9th Ave., NY
5:30 – 6:10 Registration/Networking
6:10 – 6:15 Greetings from Mike Carty, Chapter President, Treasurer, Program Chair and Organizer of all QWAFAFEW-NYC Events
6:15 – 6:55 “Accounting for Heterogeneity of Security Behavior with “Bottom Up” Asset Allocation ” – Dan diBartolomeo, President and Founder, Northfield Information Services
6:55 – 7:10 Refreshment/Networking Break
7:10 – 7:55 “What Really is that Beta in Your ETF?” – Stephen Mathai-Davis, CFA, CQF, Chief Investment Officer, and Managing Member, Quantamize
7:55 – 8:15 Adjournment
To learn more about QWAFAFEW: visit http://qwafafew.org/
Synopses and Bios:
Accounting for Heterogeneity of Security Behavior with “Bottom Up” Asset Allocation
In this presentation we will show how estimating asset class volatility and correlations from the “bottom up” (i.e. from current security compositions) provides material advantages relative to traditional time series methods when applied to collective assets. The dominant basic construct for allocating to the various assets within a portfolio is the classic Markowitz Modern Portfolio Theory (1952). This process requires formulating expected returns and covariances among the assets defined. An important nuance that most investors overlook is the distinction between allocation among singular assets (i.e. individual securities, currencies, etc.) and collective assets such as the set of singular assets included in a financial market index, which is then used as a representation of an asset class. While many techniques have been developed for forecast the parameters of the allocation problem most are applied uniformly to both singular and collective assets.
This omission materially increases estimation error for common asset (and ETF) allocation problems where collective asset are the primary vehicles. For example, consider estimating the ex-ante volatility of the S&P 500 as of December 31, 2018 (after a sharp drop of around 11% during December). Time series approaches such as ARCH and GARCH would suggest that ex-ante volatility estimates should increase in the wake of a volatile period, but the heterogeneity of the securities within the index plays a material role. In a sharp decline, high volatility (high beta) securities fall more and low volatility (low beta) securities fall less in value. Accordingly, the riskier securities are now a smaller portion and low risk securities are now a larger portion of this capitalization weighted index. This shift of weights within the index composition will mute the expected increase in volatility. A sharp increase in market valuation would have the opposite effect, increasing the weight of riskier securities thereby expanding any expectation of increased volatility.
In relatively concentrated indices (smaller markets or high average correlation) this effect can be sufficiently strong to actually decrease expected volatility after a large market decline, contrary to most investors intuition. The same mechanisms impact the expected correlations across asset classes represented by market indices (see our September 2002 newsletter). The same heterogeneity effects can also be important in asset allocation of taxable portfolios as described diBartolomeo (2003, 2008) and Markowitz and Blay (2016).
“What Really is that Beta in your ETF?”
Stephen Mathai-Davis, CIO & Managing Director – Quantamize
Dan is President and Founder of Northfield Information Services, Inc. Based in Boston since 1986, Northfield develops quantitative models of financial markets. The firm’s clients include nearly three hundred financial institutions in twenty countries. He spent nearly ten years as a Visiting Professor at the CARISMA research center of Brunel University in London. In addition, he serves on the Board of Directors of the Chicago Quantitative Alliance and the advisory board of the International Association for Quantitative Finance. He is also an active member of the Financial Management Association, and “QWAFAFEW”. He has been admitted as an expert witness in US federal courts and state courts for litigation matters regarding investment management practices and derivatives.
Mr. diBartolomeo is a director of the American Computer Foundation, and formerly served on the industry liaison committee of the Department of Statistics and Actuarial Sciences at New Jersey Institute of Technology. He continues his more than twenty years of service as a judge in the Moscowitz Prize competition, given by the University of California at Berkeley for excellence in academic research on socially responsible investing.
Dan has a long list of nearly forty publications including books, book chapters and research papers in professional journals such as Financial Analyst Journal, Quantitative Finance and Journal of Investing. Mr. diBartolomeo has also written extensively for the CFA Research Foundation. In January of 2018, he became co-editor of the Journal of Asset Management.
Stephen Mathai-Davis, CFA, CQF is the Chief Investment Officer and Managing Member of Quantamize. He is a CFA® charter holder and a member of the CFA Institute, CQF Institute, the New York Society of Security Analysts, and the Society of Quantitative Analysts.
Stephen’s investment experience and approach involves developing specialized multi-factor quantitative analytics and multi-asset options-volatility strategies. Stephen has significant global investing experience and is widely traveled in the global markets of Latin America, India, South-East Asia, South Asia, Russia Western Europe and Japan.
Stephen was previously a top performing analyst at PineBridge Investments (formerly AIG investments) with research responsibilities covering global consumer/media, energy, infrastructure, utilities, and telecommunications.
Prior to joining PineBridge Investments, Stephen served as a trader for the hedge fund, Sunridge Capital Advisors, a financials-focused investment management firm. He began his career at Sandler O’Neill & Partners as an insurance and equity trader.