We propose an easy to implement yield curve extrapolation method to determine long-term interest rates suitable for regulatory valuation. We empirically evaluate this approach for the German nominal bond market, by estimating the model on bonds with maturities up to 20 years and assessing the out-of-sample performance for bonds with maturities beyond 20 years. Even though observed long-term yields are somewhat lower than the predicted yields, the method performs quite well empirically given its simplicity. We perform a case study on pension fund liability valuation and show that our proposed method would have a substantial impact on liability values.

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small.

We estimate a model for the term structure of discounted risk-adjusted dividend growth using prices of dividend futures for the Eurostoxx 50. A 2-factor model capturing short-term mean reversion within a year and a medium-term component reverting at the business-cycle horizon gives an excellent fit of these prices. Hence, investors update the valuation of dividends beyond the business cycle only to a limited degree. The 2-factor model, estimated on dividend futures data only, explains a large part of observed daily stock market returns. We also show that the 2 latent factors are related to various economic and financial variables.

We develop a new methodology to estimate abnormal performance and risk exposure of nontraded assets from cash flows. Our methodology extends the standard internal rate of return approach to a dynamic setting. The small-sample properties are validated using a simulation study. We apply the method to a sample of 958 private equity funds. For venture capital funds, we find a high market beta and underperformance before and after fees. For buyout funds, we find a relatively low market beta and no evidence for outperformance. We find that self-reported net asset values significantly overstate fund values for mature and inactive funds.

We use a unique database on ownership stakes of equity mutual fund directors to analyze whether the directors’ incentive structure is related to fund performance. Ownership of both independent and nonindependent directors plays an economically and statistically significant role. Funds in which directors have low ownership, or “skin in the game,” significantly underperform. We posit two economic mechanisms to explain this relation. First, lack of ownership could indicate a director’s lack of alignment with fund shareholder interests. Second, directors may have superior private information on future performance. We find evidence in support of the first and against the second mechanism.

We empirically compare a wide range of term structure models used in the pricing and, in particular, hedging of caps and swaptions. We analyze the influence of the number of factors on the hedging and pricing results, and investigate the type of data—interest rate or derivative price—in combination with the estimation technique that should be used to obtain the best hedging and pricing results. We use data on interest rates, and cap and swaption prices from 1995–1999. The empirical results show that, if the number of hedge instruments is equal to the number of factors, multi-factor models outperform one-factor models in hedging caps and swaptions. However, if one uses a large set of hedge instruments, one-factor models perform as well as multi-factor models. We find that models with two or three factors imply better out-of-sample predictions of cap and swaption prices than one-factor models. Estimation on the basis of current derivative prices leads to more accurate out-of-sample prediction of cap and swaption prices than estimation on the basis of interest rate data.