A Kalman filter control technique in mean-variance portfolio management

DiLellio, James

doi:10.1007/s12197-012-9244-9

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…Many research papers reported that the co-movement of returns of certain financial assets are partially explained by a small number of factors [21], [14], [9]. This knowledge has been leveraged in a number of market-aware asset allocation strategies; [8], for example, developed and tested a methodology based on Kalman filtering that combines information from a MV optimization technique along with a three factor regression model. A factor-based asset return model is also considered in [25], which tackles portfolio optimization by a receding horizon control approach.…”

Section: A Motivation and Related Workmentioning

confidence: 99%

“…This feature contrasts our approach to alternative techniques that explicitly model the system. This is the case of procedures based on the Kalman filter, see, e.g., [8]. Note that finding the system's parameters, for instance via Expectation Maximization (EM) [23], inevitably leads to non-convex problem formulations.…”

Section: Dynamic Nature Of Portfolio Managementmentioning

confidence: 99%

“…In this case we considered a portfolio of 13 ETFs that track indices in different sectors 8 . While there seems to be mixed evidence on the predictability of returns, some papers have found that macroeconomic variables might be relevant within certain asset classes .…”

Section: Real Market Data 1) Portfolio and Inputsmentioning

confidence: 99%

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DynOpt: Incorporating dynamics into mean-variance portfolio optimization

Signoretto

Suykens

2013

2013 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Mean-variance (MV) portfolio theory leads to relatively simple and elegant numerical problems. Nonetheless, the approach has been criticized for treating the market parameters as if they were constant over time. We propose a novel convex optimization problem that extends an existing MV formulation with chance constraint(s) by accounting for the portfolio dynamics. The core idea is to consider a multiperiod scenario where portfolio weights are implicitly regarded as the output of a statespace dynamical system driven by external inputs. The approach leverages a result on realization theory and uses the nuclear norm to penalize complex dynamical behaviors. The proposed ideas are illustrated by two case studies.

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Section: A Motivation and Related Workmentioning

confidence: 99%

Section: Dynamic Nature Of Portfolio Managementmentioning

confidence: 99%

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DynOpt: Incorporating dynamics into mean-variance portfolio optimization

Signoretto

Suykens

2013

2013 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Introduction to special issue commemorating the 50th anniversary of the Kalman Filter and 40th anniversary of Box and Jenkins

Mills

Tsay

Young

2010

Journal of Forecasting

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This special issue of the Journal of Forecasting jointly celebrates the 40th anniversary of the publication of George Box and Gwilym Jenkins' highly infl uential book Time Series Analysis: Forecasting and Control, which introduced a robust and easily implementable strategy for modelling time series, and the 50th anniversary of the appearance of Rudolf Kalman's article 'A new approach to linear fi ltering and prediction problems' in the Journal of Basic Engineering, which has had an extraordinary impact in many diverse fi elds, has led to major advances in recursive estimation, and has introduced the term Kalman fi lter into the lexicon of time series analysis and forecasting. The huge number of papers published in the Journal of Forecasting that reference these two publications bears testament to their seminal status and long-lasting infl uence, making them a natural choice to base a special issue around. Copyright © 2010 John Wiley & Sons, Ltd. TIME SERIES ANALYSIS: FORECASTING AND CONTROLAfter 40 years, many still regard this book as the bible of time series analysis. It not only popularizes time series applications, but also leads to many new developments in time series research. In an era without powerful computers and statistical software, the book provided a practical iterative procedure for modelling time series data. The concept of identifi cation-estimation-checking continues to be as important and relevant as ever. As a matter of fact, the importance of diagnostic checking in modelling has become even more critical in this data-rich environment for all statistical analyses.The airline seasonal time series model remains widely applicable for most business and economic data. Indeed, our MBA students are equally impressed by the simplicity and applicability of the model in forecasting quarterly earnings of companies across many industrial sectors. The transfer function

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