James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
A hybrid GNSS integrity design leveraging a priori signal noise characteristics. Journal of Navigation, 63(3), 513-526, (2010).
Abstract
The objective of this paper is to explore a hybrid Global Navigation Satellite System (GNSS) architecture that efficiently meets the stringent needs of safety of life systems. An architecture is proposed that allocates error bounding and alerting functionality between the space, ground and user segments based on refining the assumptions of the leading-order fault free error sources expected in the near future from developing GNSS technologies. By revisiting the first principles used to derive standard RAIM fault detection, a modified detection algorithm is developed to more accurately accommodate these new fault-free error distributions while supporting timely user alerts. The results of the analysis and simulation indicate that this optimized receiver algorithm and associated architecture can provide significant development and operational benefit for navigation users requiring high levels of integrity.
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
A Kalman Filter Control Technique in Mean-Variance Portfolio Management. Journal of Economics and Finance, 39 (Published Online October 2012), pp. 235-261, (2015).
Abstract
This article develops and tests a methodology for rebalancing the mean-variance optimized portfolio through the use of a Kalman filter. The approach combines information from a mean-variance (MV) optimization technique along with a three factor regression model that includes market capitalization, book to market ratio, and the market index. We demonstrate empirically using 46 years of daily returns from 17 industrial sectors that a Kalman filter model can be an effective approach under the conditions of minimum variance and low expected risk-to-return for both a constrained and unconstrained MV technique. Enhancements to returns are largely due to the ability to reduce turnover for the unconstrained case, and the ability to maintain portfolio exposure to cap and value weighted positions in the constrained case. Statistical significance is demonstrated to show return improvements of the Kalman filter model over a comparable MV technique, where the greatest statistical benefit at the 0.05 and 0.10 level is shown under the minimum variance objective. Additionally, the KF applied to the constrained optimization case always provides a Sharpe ratio higher than the Naïve portfolio, after transactions costs are taken into account.
Keywords: Kalman Filter, Three Factor Model, Optimization, Investments
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
Optimal Strategies for Traditional vs. Roth IRA/401(k) Consumption During Retirement. Decision Sciences, April, (2016) with Daniel N. Ostrov.
Abstract
We establish an algorithm that produces an optimal strategy for retirees to withdraw funds between their tax-deferred accounts (TDAs), like traditional IRA/401(k) accounts, and their Roth IRA/401(k) accounts, in the context of a financial model based on American tax law. This optimal strategy follows a geometrically simple, intuitive approach that can be used to maximize the size of a retiree’s bequest to an heir or, alternatively, to maximize a retiree’s portfolio longevity. We give examples where retirees following the approach currently implemented by major investment firms, like Fidelity and Vanguard, will reduce their bequests by approximately 10% or lose 18 months of portfolio longevity compared to our optimal approach. Further, our strategy and algorithm can be extended to many cases where the retiree has additional, known yearly sources of money, such as income from part-time work, taxable investment accounts, and Social Security.
Levan Efremidze, PhD, Assistant Professor of Finance
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
Darrol J. Stanley, DBA, Professor of Finance and Accounting
Using VIX Entropy Indicators for Style Rotation Timing. Journal of Investing, 23(3), 130-143, (2014).
Abstract
In this article, the authors examine the feasibility of market timing between large-capitalization value and growth portfolios with the use of entropy measures as compared with previously tested methods of market timing using stock market volatility (using the CBOE’s Volatility Index, VIX). Including transaction fees, style rotations using entropy measures appear to provide superior risk-adjusted returns and may offer a desirable alternative strategy for risk-averse investors seeking equity exposure.
James DiLellio, PhD, MBA, Associate Professor of Decision Sciences
What to do when Traditional Diversification Strategies Fail. Graziadio Business Report, 12(4), (2009).
Abstract
In 2008, market events showed that some of the protection provided by diversification is lost when correlation among asset classes changes rapidly. Now, the question is: Are traditional diversification concepts no longer applicable due to some systemic change? Or is there still a simple, repeatable approach to diversification that can lead to significant protection against loss of principle? Many factors could be contributing to recent volatile market behavior, for example, globalization, investor fear, government policies, and alternative investments. This article explores a methodology that attempts to address these factors.
