Universitätspublikationen
Refine
Year of publication
Document Type
- Working Paper (16)
- Part of Periodical (4)
- Article (1)
- Conference Proceeding (1)
- Contribution to a Periodical (1)
- Report (1)
Has Fulltext
- yes (24)
Is part of the Bibliography
- no (24)
Keywords
- General Equilibrium (3)
- Asset pricing (2)
- Börsenzulassung (2)
- Call-Option (2)
- Deutschland (2)
- Exotic options (2)
- Flotation Costs (2)
- Going Public (2)
- Macro Finance (2)
- Neuer Markt (2)
It has been documented that vertical customer-supplier links between industries are the basis for strong cross-sectional stock return predictability (Menzly and Ozbas (2010)). We show that robust predictability also arises from horizontal links between industries, i.e., from the fact that industries are competitors or offer products, which are substitutes for each other. These horizontally linked industries exhibit positively correlated fundamentals. The signal derived from this type of connectedness is the basis for significant alpha in sorted portfolio strategies, and informed investors take the related information into account when they form their portfolios. We thus provide evidence of return predictability based on a new type of economic links between industries not captured in previous studies.
We introduce Implied Volatility Duration (IVD) as a new measure for the timing of uncertainty resolution, with a high IVD corresponding to late resolution. Portfolio sorts on a large cross-section of stocks indicate that investors demand on average about seven percent return per year as a compensation for a late resolution of uncertainty. In a general equilibrium model, we show that `late' stocks can only have higher expected returns than `early' stocks if the investor exhibits a preference for early resolution of uncertainty. Our empirical analysis thus provides a purely market-based assessment of the timing preferences of the marginal investor.
Predictability and the cross-section of expected returns: a challenge for asset pricing models
(2020)
Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria models have to satisfy to produce expected return patterns in line with the data and discuss various examples.
On 23 July 2014, the U.S. Securities and Exchange Commission (SEC) passed the “Money Market Reform: Amendments to Form PF ,” designed to prevent investor runs on money market mutual funds such as those experienced in institutional prime funds following the bankruptcy of Lehman Brothers. The present article evaluates the reform choices in the U.S. and draws conclusions for the proposed EU regulation of money market funds.
When estimating misspecified linear factor models for the cross-section of expected returns using GMM, the explanatory power of these models can be spuriously high when the estimated factor means are allowed to deviate substantially from the sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. The mathematically correct global minimum of the GMM objective function can be obtained at a parameter vector that is far from the true parameters of the data-generating process. This property is not restricted to small samples, but rather holds in population. It is a feature of the GMM estimation design and applies to both strong and weak factors, as well as to all types of test assets.