The author attempts to find a remedy for the bias of staleness in valuations of venture capital or buyout funds. The bias derives from the lack of a public market for such assets. As a result, the prices and valuations of venture capital or buyout investments change in a smoother manner over time compared with those of public market–driven benchmarks. Using standard risk measures against such data can produce overly conservative results.

The methodology the author presents carries two practical benefits. First, it reduces the bias in risk measurements. Second, it provides a readily available setup for preparing mark-to-market valuations of portfolios composed of such assets.

The author presents an approach for direct measurement of risk levels in alternative asset portfolios. In particular, the capital asset pricing model (CAPM) is enhanced through the additions of lagging market returns as well as autoregression corrections. As a result, the author estimates betas for venture capital and buyout funds at approximately 1.64 and 0.90, respectively, which is more than double the outcomes generated by the standard procedure.

The decomposition of a portfolio according to the size of the lag in the valuation of its individual components is a byproduct of the author’s procedure. It creates a systematic and statistically documented approach for marking returns to market. To mark to market the fraction of the portfolio that is n quarters old, one must multiply the market return from that time by the beta for the portfolio.

There are several groups of investment-related professionals who might benefit from these outcomes, including investors trading in such assets, particularly if they are obliged to mark to market (e.g., when managing pension plans of public companies). Another group to benefit are funds’ general partners. Thanks to increased predictability of the performance of funds under their management, they may be able to attract new investors more easily.

The author first calculates risk measures for stale portfolios using the standard CAPM. Then, she adds several lagging market returns and autoregressive correction changes. This procedure generates betas twice as high as those generated with the standard method. It is worth noting that autoregressive correction has stronger effects for venture capital fund portfolios than for buyouts.

The dataset used for this analysis is sourced from Cambridge Associates (CA), an advisory organization concentrating on alternative investments. CA venture returns are regressed against the Wilshire 5000 index minus the three-month T-bill rate. The data cover 1990–2011 (86 quarters of data).

There are several limitations to the methodology. First, it is viable only for portfolios that include funds that are a mix of vintage years as well as a mix of partnerships. It may not be appropriate for individual vintages or portfolios with limited diversification in terms of partnerships (or individual partnerships). This issue means the methodology is not holistic in its approach. Second, there are some limitations related to the dataset itself. These limitations include a potential upward bias because CA clients may not be interested in getting advice on the worst-performing funds and their risk tolerance is higher than, for instance, that of clients of Thomson Venture Economics.

From my perspective, the approach presented in the paper is very appealing. Despite its relative simplicity, it contains a quite powerful yet comprehensible mechanism that may benefit different participants of the alternative investment market. These participants include investors, fund managers, and companies assuming venture capital as their source of financing. The author also did an excellent job identifying weaknesses of the methodology presented, which, in my opinion, increases the chance of practical application of the model and provides room for further research and discussion.