Spending rules for endowment funds

Endowment fund managers face an asset allocation problem with several particularities: they are more interested in spending for current and future beneficiaries than growing value, although the trade-off between these two alternatives needs to be understood; they have to consider longest-term investment, typically an infinite horizon. We do address these allocation constraints in a dynamic framework where minimum subsistence levels (introducing the idea that a minimum spending amount needs to be made at every time period) are introduced in the objective function. We derive explicit formulas for the optimal spending stream, endowment value, spending rate and portfolio strategy in a simple Black/Scholes type economy. We analyze the effects of parameter changes on asset allocation decisions and provide simulations on bearish, median and bullish paths. Copyright Springer Science + Business Media, LLC 2006

Methodes probabilistes d'evaluation et modeles a variables d'etat : une synthese

Available at INIST (FR), Document Supply Service, under shelf-number : DO 1649 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

How Risk Managers Should Fix TEV and VaR Constraints in Asset Management

Many investors assign part of their funds to asset managers of mutual funds who are given the task of beating a benchmark. Asset managers usually face a constraint on maximum Tracking Error Volatility (TEV), imposed by the risk management office to keep the risk of the portfolio close to that of the selected benchmark. However, many admissible portfolios still have problems in mean-variance terms, for example because of a too high variance. To overcome this problem Jorion (2003) also fixes a constraint on variance, while Alexander and Baptista (2008) fix a constraint on Value-at-Risk (VaR). Moreover, a minimum TEV should be imposed to force the asset manager to perform a real active strategy, as proposed in Riccetti (2012). In this paper, I determine an optimal value for the set of bounds composed by minimum TEV, maximum TEV and maximum VaR. In particular, concerning maximum VaR, I develop a strategy which imposes to asset managers a set of portfolios that contains as much as possible "efficient constrained TEV" portfolios and, at the same time, as less as possible non-efficient ones. With this aim, I show that a bound on maximum VaR is usually better than a bound on maximum variance