Driving Factors During the 2007-2009 Credit Crisis – February 2013 Adobe PDF icon

Dr Krishna Nehra & Laurent Favre – AlternativeSoft AG

This article focuses on investigating the temporal evolution of four asset classes – equity (S&P 500 Index), bond (JPM emerging markets bond Index), hedge fund (HFRI fund weighted composite index non-investable) and commodity (S&P GSCI commodity Index) using principal component analysis (PCA). PCA transforms a number of correlated time-series into a smaller number of uncorrelated time-series. It is shown that before the 2007-2009 credit crisis, the market was driven by commodities only, whereas, during the credit crisis (2007-2009) and in the years after (2010-2012), equities and commodities drove the market.

 

Asset Classes’ Expected Performance Based on Macro-Economic Views – Jan 2013 Adobe PDF icon

Dr Krishna Nehra & Laurent Favre – AlternativeSoft AG

The table provided in this article displays the relationship between economic factors and equity, fixed income, commodity or hedge fund indices monthly returns. Assuming current trends will continue in 2013, then MSCI Emerging Markets and DAX are the best equity indices, Emerging Markets USD bonds is the best fixed income investment and Gold is the best commodity investment.

 

Black-Litterman Based Portfolio Optimization – Sept 2012 Adobe PDF icon

Dr Krishna Nehra & Laurent Favre – AlternativeSoft AG

This article presents an example of portfolios optimized by AlternativeSoft’s BL model based optimization with the following objectives:
- Minimize portfolio volatility
- Produce a well-diversified portfolio
- Allow the investors to define their views (absolute as well as relative) on the funds’ (mutual funds and hedge funds) expected returns,
- Tilt the portfolio from an equally weighted portfolio towards the funds with good future expected returns if the investors are con dent about the future returns of these funds.

 

How to Price Hedge Funds: From Two to Four-Moment CAPM – July 2005 Adobe PDF icon

Angelo Ranaldo – University of St. Gallen; University of St. Gallen – SoF: School of Finance & Laurent Favre – AlternativeSoft AG

The authors conclude that in the CAPM model, it is hard to explain the superior paste performance of hedge funds. They argue that the Markowitz mean-variance criterion underpinning the traditional CAPM may fail to capture systematic features characterizing hedge fund performance. Thus, they extend the two-moment market model to a higher-moment model to accommodate coskewness and cokurtosis. The higher-moment approach is more appropriate for capturing the non-linear relation between hedge fund and market returns and accounting for the specific risk-return payoffs of each hedge fund investment strategy. The key result is that the sole use of the two-moment pricing model may be misleading and may wrongly indicate insufficient compensation for the investment risk.

 

Hedge Funds Allocation: Case Study of a Swiss Institutional Investor – June 2000 Adobe PDF icon

Laurent Favre – AlternativeSoft AG & José-Antonio Galeano – Lombard Odier & Cie

Asset allocation advisers usually use the mean-variance framework to show the benefits of investing in hedge funds. They prove that this is not optimal and develop a method based on a modified Value-at-Risk model for non-normally distributed assets. They take the example of a Swiss pension fund investing part of its wealth in hedge funds. Using a shortfall risk approach they are able to show that investing in a diversified Hedge Funds portfolio is beneficial for lowering modified Value-at-Risk. Then, we analyze several hedge fund strategies using local regression analysis. We obtain the payoffs of these strategies and compare them with the payoff of a standard Swiss pension fund portfolio. Finally, we compute for a representative hedge fund the price of the option like feature of the incentive fee and the premium paid to the investor by the fund for taking liquidity risk. We find that the return risk adjusted benefits of investing in hedge funds are reduced between 19% and 41% if we take into account the liquidity premium and the survivorship bias.

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