Dennis Bams

European Mutual Fund Performance

This paper presents an overview of the European mutual fund industry and investigates mutual fund performance using a survivorship bias controlled sample of 506 funds from the five most important mutual fund countries. The latter is done using the Carhart (1997) 4‐factor asset‐pricing model. In addition we investigate whether European fund managers exhibit ‘hot hands’, persistence in performance. Finally the influence of fund characteristics on risk‐adjusted performance is considered. Our overall results suggest that European mutual funds, and especially small cap funds are able to add value, as indicated by their positive after cost alphas. If we add back management fees, four out of five countries exhibit significant out‐performance at an aggregate level. Finally, we detect strong persistence in mean returns for funds investing in the UK. Our results deviate from most US studies that argue mutual funds under‐perform the market by the amount of expenses they charge.

The Performance of Local versus Foreign Mutual Fund Managers

In this paper we examine the performance of US equity funds (locals) versus UK equity funds (foreigners) also investing in the US equity market. Based on informational disadvantages one would expect the UK funds to under-perform the US funds, especially in the research-intensive small company market. After controlling for tax treatment, fund objectives, investment style and time-variation in betas, we do not find evidence for this. In the small company segment we even find a slight out-performance for UK funds compared to US funds. Finally we observe a home bias in the UK portfolios, which is partly attributable to UK funds investing in cross-listed stocks in the USA.

Loss Functions in Option Valuation: A Framework for Selection

In this paper, we investigate the importance of different loss functions when estimating and evaluating option pricing models. Our analysis shows that it is important to take into account parameter uncertainty, because this leads to uncertainty in the predicted option price. We illustrate the effect on the out-of-sample pricing errors in an application of the ad hoc Black-Scholes model to DAX index options. We confirm the empirical results of Christoffersen and Jacobs (Christoffersen, P., K. Jacobs. 2004. The importance of the loss function in option valuation. J. Financial Econom.72 291--318) and find strong evidence for their conjecture that the squared pricing error criterion may serve as a general-purpose loss function in option valuation applications. At the same time, we provide a first yardstick to evaluate the adequacy of the loss function. This is accomplished through a data-driven method to deliver not just a point estimate of the root mean squared pricing error, but a distribution.

Risk Premia in the Term Structure of Interest Rates: A Panel Data Approach

This paper proposes a panel data approach to modeling the risk premium in the term structure of interest rates. Specifically, we develop a fixed maturity/random time effects model that implies a time-invariant one-factor model. Our approach allows us to disentangle risk premia and unexpected excess returns, which is not possible in the standard time series approach. In addition, small sample bias is alleviated and statistical efficiency improved. Our results allow for interesting inferences about maturity-specific effects in the term structure. First, the expectations hypothesis is soundly rejected for our full data panel of U.S. Treasury securities. Second, a considerable degree of mean reversion is present in the risk premia. Third, our findings shed new light on the magnitude of the slope coefficient in regressions of the yield onto the forward curve.

Credit Risk Characteristics of US Small Business Portfolios

This paper generalizes the existing asymptotic single-factor model to address issues related to industry heterogeneity, default clustering and parameter uncertainty of capital requirement in US retail loan portfolios. We argue that the Basel II capital requirement overstates the riskiness of small businesses even with prudential adjustments. Moreover, our estimates show that both location and spread of loss distribution bare uncertainty. Their shifts over the course of the recent crisis have important risk management implications. The results are based on a unique representative dataset of US small businesses from 2005 to 2011 and give fundamental insights into the US economy.

Pension Fund Asset Allocation in Low Interest Rate Environment

We analyze changes in portfolios of pension funds since the start of current low interest rate environment. We find that they have on average decreased the allocation to equity and increased the allocation to fixed income, which is inconsistent with the literature on strategic asset allocation. Next, more generally, we empirically investigate the relationship between variables that predict asset returns and portfolio allocation in levels as well as changes. We find that dividend-price ratio shows the strongest relationship among other variables. However, we find a negative relationship as opposed to a positive one predicted by the literature. Overall, the results suggest that pension funds are unable to incorporate predictive information in their strategic asset allocation, but they take active decisions by under or over-weighting their portfolio relative to the stated strategic portfolio to benefit from time-varying investment opportunities.

Optimal Risk Sharing in a Collective Defined Contribution Pension System

We analyze a collective defined contribution pension fund which aims at intergenerational risk sharing among different age cohorts using a return smoothing mechanism. Using a utility based framework, we find that approximately one third of unexpected return shocks should be directly passed on to all the cohorts in the year the shock occurs by means of the smoothing mechanism. We demonstrate that risk sharing implemented in this way is welfare improving compared to a plan with no risk sharing and more sustainable compared to defined benefit pension fund plans. Additionally, we show that the asset allocation of such a pension fund automatically corresponds to the life-cycle portfolio choice theory.

Asset Allocation Dynamics of Pension Funds

How does portfolio of long-term investors like pension funds change relative to the stated strategic portfolio? We investigate their portfolio dynamics using an international database that spans over 20 years and focus on portfolio rebalancing. We find that a significant proportion of the change in the weight of equity is related to passive change in portfolio due to realized equity returns. Moreover, pension funds follow asymmetric rebalancing, they rebalance poorly when stock market is doing well but rebalance strongly when stock market is doing poorly. Actual change in equity portfolio only partially reflects strategic changes. We also study cross-sectional differences in rebalancing. The results indicate that US and defined benefit pension funds rebalance less. Moreover, external managers and active managers can be identified as the major source of poor rebalancing. Lastly, between asset classes, pension fund are more passive in alternative investments.

From Time Varying Risk-Aversion to Anomalies in Market Moments’ Risk Premia

Previous studies on the cross-sectional market moments’ risk premia find significantly negative risk premia for the market volatility and the market skewness risks and a positive premium for the market kurtosis risk. However, a significantly negative price of risk for the market skewness and a positive price of risk for the market kurtosis move against the ICAPM intuition. We refer to these counter-intuitive prices of risk as the anomalies in the market moments’ risk premia, and show that these anomalies only exist in low risk-aversion periods. Once investors’ risk-aversion rise, these anomalies vanish. Moreover, we find that Baker and Wurgler’s (2006) index of investor sentiment is strongly negatively affected by the past realizations in investor risk-aversion. Consequently, our empirical results can also be replicated by analyzing periods of high and low sentiment, separately.