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Kartik Sivaramakrishnan
Axioma
5Publications
1H-index
10Citations
Publications 5
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#1Kartik Sivaramakrishnan (Axioma)H-Index: 1
Last.Dieter Vandenbussche (Axioma)H-Index: 7
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The traditional Markowitz MVO approach is based on a single-period model. Single period models do not utilise any data or decisions beyond the rebalancing time horizon with the result that their policies are myopic in nature. For long-term investors, multi-period optimisation offers the opportunity to make wait-and-see policy decisions by including approximate forecasts and long-term policy decisions beyond the rebalancing time horizon. We consider portfolio optimisation with a composite alpha s...
Multi-asset class (MAC) portfolios can be composed of investments in equities, fixed income, commodities, foreign exchange, credit, derivatives, and alternatives such as real estate and private equity. The return for such nonlinear portfolios is asymmetric with significant tail risk. The traditional Markowitz mean–variance optimization (MVO) framework, which linearizes all the assets in the portfolio and uses the standard deviation of return as a measure of risk, does not always accurately measu...
#1Sebastián Ceria (Axioma)H-Index: 3
#2Kartik Sivaramakrishnan (Axioma)H-Index: 1
Last.Robert A. Stubbs (Axioma)H-Index: 8
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The three important ingredients in a mean-variance optimization (MVO) model are the alpha vector representing expected returns, the risk model that is used to measure the variance of the portfolio, and a set of constraints representing the portfolio managers’ mandates and choices. In the traditional quantitative investment process, these three inputs are usually developed independently of each other, without much regard to the interaction between them. As a result, the optimal portfolio generate...
The three ingredients in a mean–variance optimization model are the expected returns, the risk model, and constraints representing the portfolio manager’s mandates. Misalignment between the alpha vector and the risk model occurs when the alpha vector is not completely spanned by the factors in the risk model. It results in the optimizer taking large exposures on factors that have systematic risk but are missing from the risk model. With constraints, misalignment arises between the implied alpha ...
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