Risk‐Adjusting the Returns to Venture Capital

Abstract

This appendix presents a number of supporting results, extensions, and robustness checks to supplement the analysis in the main paper. Section A provides a detailed derivation of the results for the log-normal model referred to in the introduction of the main paper. Section B discusses the relationship between our method and tests with linearized versions of SDFs that are common in the literature. Section C conducts Monte Carlo experiments to study the size of the tests we use to evaluate VC fund and start-up company payoffs. Section D looks at how the estimation of SDF parameters affects the size and power of tests. Section E provides additional detail on the data sets. Section F presents robustness checks where we consider further subsample results, including a sample of liquidated funds, and we explore the sensitivity of our findings to the assumptions about the unobserved acquisition returns of start-up company investments. In Section G, we compare our approach to earlier methods in the literature that are based on a log-normal model of returns. A Expected Returns in the Jointly Log-Normal CaseWe show results for expected returns for the special case of jointly log-normal market returns and asset returns R i,t+1 for the general SDF specification M t+1 = exp(a − br m,t+1 ).

Estimating Private Equity Returns from Limited Partner Cash Flows

“…The primary contribution of this paper is the introduction of a methodology based on Bayesian Markov Chain Monte Carlo (MCMC) to estimate a time series of PE returns using cash flows accruing to limited partners and factor returns from public capital markets. The identification strategy of this methodology is similar to that of Cochrane (2005), Korteweg and Sorensen (2010), Driessen, Lin, and Phalippou (2012), Franzoni, Nowak, and Phalippou (2012), and Korteweg and Nagel (2016). Our contribution with respect to prior research is that, in addition to estimating factor loadings and αs, we are able to construct a quarterly time series of returns that is useful for understanding the intertemporal behavior of the asset class.…”

“…Recent work byKorteweg and Nagel (2016) explores the relation between PMEs and discount rates. The problem of correlated forward-looking discount rates and cash flows is also considered byBrennan (1997) andAng and Liu (2004).…”

Real Estate Returns by Strategy: Have Value‐Added and Opportunistic Funds Pulled Their Weight?

“…Of such models, a popular single‐factor model is the capital asset pricing model (CAPM) ( e.g ., see Sharpe, ) and, among the multifactor models, there are three‐factor models ( e.g ., see Fama and French, ) as well as models that include a fourth factor: “momentum” ( e.g ., see Carhart, ) or liquidity ( e.g ., Pástor and Stambaugh, ). Moreover, because the evaluation of venture capital‐like payoffs is particularly challenging ( e.g ., infrequent and skewed payoffs covering varying time horizons), some form of a stochastic discount factor may be utilized; for example, see Korteweg and Nagel ().…”

Small Business Equity Returns: Empirical Evidence from the Business Credit Card Securitization Market

“…This confidence interval for the small business equity beta is comparable to the range of market betas for publicly traded NYSE, Amex, or NASDAQ firms (see, e.g., Fama and French (1997)). In contrast, Korteweg and Nagel (2016) report that estimates of venture capital market betas in the literature are consistently above one (see their footnote 4). Furthermore, Korteweg (2019) provides an extensive list of private equity and venture capital market beta estimates reported in that literature.…”