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Marat Molyboga

Researcher at Illinois Institute of Technology

Publications -  34
Citations -  131

Marat Molyboga is an academic researcher from Illinois Institute of Technology. The author has contributed to research in topics: Portfolio & Asset allocation. The author has an hindex of 6, co-authored 32 publications receiving 111 citations. Previous affiliations of Marat Molyboga include EDHEC Business School.

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The Revealed Preference of Sophisticated Investors

TL;DR: In this paper, the authors show that hedge fund investors' revealed preferences are also best modeled by the Capital Asset Pricing Model (CAPM) and that using the CAPM is rational, as they show that CAPM alpha correlates with managerial skill and predicts performance better than other multi-factor models.
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A Modified Hierarchical Risk Parity Framework for Portfolio Management

TL;DR: A modified hierarchical risk parity (MHRP) approach that extends the HRP approach by incorporating three intuitive elements commonly used by practitioners, which improves diversification across portfolio constituents both within and across clusters by relying on an equal volatility, rather than an inverse variance, allocation approach.
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Risk premia in Chinese commodity markets

TL;DR: In this article, the authors investigated risk premia in Chinese commodity markets by decomposing the returns of commodity futures into spot and term premia following Szymanowska et al. They found that a three-factor model that includes an equally-weighted market factor, carry and time-series momentum explains spot premia.
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A Simulation-Based Methodology for Evaluating Hedge Fund Investments

TL;DR: In this article, a large scale simulation framework for evaluating hedge funds' investments subject to the realistic constraints of institutional investors is presented. But the authors do not consider the effect of hedge fund investment strategies on individual investors' performance.
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Assessing hedge fund performance with institutional constraints: evidence from CTA funds

TL;DR: In this paper, a set of performance persistence tests based on a large-scale simulation framework and stochastic dominance methodology is proposed to investigate momentum in the performance of hedge funds of the managed futures industry.