Optimal Allocation Across Alternative Investment Strategies In A Dynamic Framework
Introduction:
The success of Alternative Investments is attributable to both their higher level of expected return per unit of risk than traditional investments, as well as to the portfolio diversification benefits they provide. The benefits of such lightly constrained strategies can further be put to profit by enhancing the investment process in two ways; firstly by employing robust portfolio optimization techniques while including multiple risk criteria (Value at Risk, Extreme Value Theory…) so as to account for the non-linear features of both Alternative Investments returns and investors utility functions, secondly by integrating the views of Alternative Investment managers' to the portfolio construction process. The following document introduces a structure that allows Alternative Investment managers to account for both, investors' and strategies' asymmetric nature. Additionally, this document outlines an approach that allows for the proper apportioning of managers' views to predetermined neutral benchmark weights, and a framework to derive those views in a sound, consistent and tractable manner.
For more information, please see http://www.qmsadv.com/ or send your inquiries to info@qmsadv.com
Tuesday, July 7, 2009
Hedge Funds’ Dynamic Strategic & Tactical Asset Allocation and Dynamic Risk Exposure in a Regime Switching Framework
Introduction:
Hedge funds engage in dynamic trading strategies, use leverage opportunistically, take positions in non-linear instruments and take concentrated bets. These techniques result in non-linear payoffs at the single Hedge Fund and at the Fund of Hedge Fund level. The following approach is therefore to assess Hedge Fund strategies' risks, to forecast Hedge Fund style returns and to construct optimal strategic and tactical hedge fund allocations that reflect the option-like and nonlinear features of those strategies.
Description:
Hedge funds usually exhibit non-normal payoffs for multiple reasons such as the use of derivatives, structured products, and dynamic trading strategies. Further, hedge funds take state-contingent opportunistic bets that account for a significant part of their returns and risks. Despite ever growing research on the topic, most studies on hedge funds' performance so far focused on classical linear factor models, non-parametric models or linear factor models with option like factors.
The proposed framework departs from those approaches and utilizes factor models based on the regime switching theory, where non-linearity in the exposure is captured by factor loadings that are state dependent. The regime switching approach first identifies the current and future likely states of the markets - a stochastic process based on numerous risk factors with forecasting power that identifies and translates the current and future states of the markets in quintiles after which a state dependent factor loading process is able to capture hedge funds’ exposure to the market risk factor in these different states or market conditions.
Empirical results show that switching regime factor models can explain a larger proportion of the variation in returns of hedge funds, as opposed to classical linear factor models, non-parametric models and linear factor models with option like factors. The justification for this greater explanatory power can be linked to both the dynamic nature of hedge funds' strategies and to the tendency of market factors' to synchronize or become more dependent in periods of stress.
For more information, please see http://www.qmsadv.com/ or send your inquiries to info@qmsadv.com
Hedge funds engage in dynamic trading strategies, use leverage opportunistically, take positions in non-linear instruments and take concentrated bets. These techniques result in non-linear payoffs at the single Hedge Fund and at the Fund of Hedge Fund level. The following approach is therefore to assess Hedge Fund strategies' risks, to forecast Hedge Fund style returns and to construct optimal strategic and tactical hedge fund allocations that reflect the option-like and nonlinear features of those strategies.
Description:
Hedge funds usually exhibit non-normal payoffs for multiple reasons such as the use of derivatives, structured products, and dynamic trading strategies. Further, hedge funds take state-contingent opportunistic bets that account for a significant part of their returns and risks. Despite ever growing research on the topic, most studies on hedge funds' performance so far focused on classical linear factor models, non-parametric models or linear factor models with option like factors.
The proposed framework departs from those approaches and utilizes factor models based on the regime switching theory, where non-linearity in the exposure is captured by factor loadings that are state dependent. The regime switching approach first identifies the current and future likely states of the markets - a stochastic process based on numerous risk factors with forecasting power that identifies and translates the current and future states of the markets in quintiles after which a state dependent factor loading process is able to capture hedge funds’ exposure to the market risk factor in these different states or market conditions.
Empirical results show that switching regime factor models can explain a larger proportion of the variation in returns of hedge funds, as opposed to classical linear factor models, non-parametric models and linear factor models with option like factors. The justification for this greater explanatory power can be linked to both the dynamic nature of hedge funds' strategies and to the tendency of market factors' to synchronize or become more dependent in periods of stress.
For more information, please see http://www.qmsadv.com/ or send your inquiries to info@qmsadv.com
Efficient Portfolio Management: A Dynamic Investment Framework
RATIONALE FOR A STRATEGIC ASSET ALLOCATION
A Strategic Asset Allocation (SAA) refers to an ex-ante optimal weighting of asset classes in an investor’s portfolio that aims to achieve the long-term investment objectives as set forth in the investor’s policy statement. It is based on long-term return, risk and correlation expectations for the asset classes considered for the policy portfolio. The fundamental principle of Strategic Asset Allocation is to capture the advantages that come from getting exposure to a variety of asset classes with dissimilar return patterns, effectively diversifying risks across assets without sacrificing return expectations. Diversifying across multiple asset classes can help achieve a portfolio weighted average of volatility that is less than the volatility of each of the individual portfolio components, resulting in a higher portfolio risk-adjusted return.
The goal of a Strategic Asset Allocation is therefore to maximize the risk-adjusted total return of an investor’s strategic portfolio, based on an investor’s guidelines on tracking error risk at both the asset class level and at the overall portfolio level.
A Strategic Asset Allocation (SAA) refers to an ex-ante optimal weighting of asset classes in an investor’s portfolio that aims to achieve the long-term investment objectives as set forth in the investor’s policy statement. It is based on long-term return, risk and correlation expectations for the asset classes considered for the policy portfolio. The fundamental principle of Strategic Asset Allocation is to capture the advantages that come from getting exposure to a variety of asset classes with dissimilar return patterns, effectively diversifying risks across assets without sacrificing return expectations. Diversifying across multiple asset classes can help achieve a portfolio weighted average of volatility that is less than the volatility of each of the individual portfolio components, resulting in a higher portfolio risk-adjusted return.
The goal of a Strategic Asset Allocation is therefore to maximize the risk-adjusted total return of an investor’s strategic portfolio, based on an investor’s guidelines on tracking error risk at both the asset class level and at the overall portfolio level.
I/ THE DERIVATION OF ASSET CLASS ASSUMPTIONS
Deriving Robust Long-Term Asset Class Expectations
II/ FINANCIAL DECISION MAKING PROCESS
1/ Strategic Asset Allocation
2/ Risk Budgeting
3/ Tactical Asset Allocation
III/ PORTFOLIO IMPLEMENTATION
Monitoring and Rebalancing
For more information, please see http://www.qmsadv.com/ or send your inquiries to info@qmsadv.com
Saturday, June 6, 2009
Enhanced Hedge-Fund Index Solution
ALPHA GENERATION VIA TACTICAL HEDGE FUND STYLE TILTS
To maximize the risk-adjusted total return of a diversified portfolio of hedge fund sub-indices by actively overweighting and underweighting the components of the strategic portfolio
To maximize the risk-adjusted total return of a diversified portfolio of hedge fund sub-indices by actively overweighting and underweighting the components of the strategic portfolio
KEY PRODUCT DIFFERENTIATING FACTORS
- Innovative Solution
- Transparent Quantitative Process
- Optimal Portfolio Construction
- Market and economic intuition
THREE STEPS CONSTRUCTION PROCESS
1/ Strategic Allocation
2/ Tactical views and alpha generation
3/ Disciplined and robust portfolio construction framework
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