An Agnostic and Practically Useful Estimator of the Stochastic Discount Factor

We propose an estimator for the stochastic discount factor (SDF) which is agnostic because it does not require macroeconomic proxies or preference assumptions. It depends only on observed asset returns. Nonetheless, it is immune to the form of the multivariate return distribution, including the distribution’s factor structure. Putting our estimator to work, we find that a unique positive SDF prices all U.S. asset classes and satisfies the Hansen/Jagannathan variance bound. In contrast, the Chinese and Indian equity markets do not share the same SDF and hence do not seem to be integrated

IPO Firm Executives, Compensation, and Selling

IPO firm executives are significant net sellers in the year immediately following the IPO year. Two significant variables affecting their sales are the number of stock options exercised during the year and the number of shares held at the end of the preceding year. Contrary to the findings of the previous studies, the number of stock options and the number of restricted stocks turn out to be insignificant. The evidence suggests that IPO executives sell mainly to realize a significant part of their undiversified wealth; however, they do not sell to explicitly hedge against stock option grants or to exploit potential overvaluation

Internationally correlated jumps

Stock returns are characterized by extreme observations, jumps that would not occur under the smooth variation of a Gaussian process. We find that jumps are prevalent in most countries. This has been little investigation of whether the jumps are internationally correlated. Their possible inter-correlation is important for investors because international diversification is less effective when jumps are frequent, unpredictable and strongly correlated. Public supervisors may also mind about widely correlated jumps, as they could bring down certain financial intermediaries. We investigate using daily returns on broad equity indexes from 82 countries and for several statistical measures of jumps. Various jump measures are not in complete agreement but a general pattern emerges. Jumps are internationally correlated but not as much as returns. Although the smooth variation in returns is driven strongly by systematic global factors, jumps are more idiosyncratic and most of them are found in Europe. Some pairs of correlated jumps occur simultaneously but not to the extent of correlated returns

Legitimacy signals and family IPO performances

The objective of this research is to examine the relationship between signals including governance and management practices and the performance of family firms IPOs. Using IPO data of 129 family firms and 129 comparable non-family firms from the Taiwan Stock Exchange, our findings highlighted the role of non-family insiders, or non-family affiliated directors in the IPOs of family firms. Our comparison between family and non-family IPOs shows hiring prestigious underwriters significantly improves the performance of family firm IPOs. Finally, we found the industries of IPO firms moderate the relationship between corporate governance characteristics and IPO performances, as non-family firms in technology industries are perceived to be more legitimate than their family counterparts. This paper makes three contributions to existing research. Firstly, we contribute to the legitimacy theory by suggesting an interaction effect between internal (organizational) and external (environmental) factors. Secondly, our analysis highlighted the roles of affiliated directors and industry in the performances of public family firms. Thirdly, this study contributes to the family business research by underscoring the differences between family and non-family firms in the IPO context

Resolving the Errors-in-Variables Bias in Risk Premium Estimation

The Fama-Macbeth (1973) rolling-B method is widely used for estimating risk premiums, but its inherent errors-in-variables bias remains an unresolved problem, particularly when using individual assets or macroeconomic factors. We propose a solution with a particular instrumental variable, B calculated from alternate observations. The resulting estimators are unbiased. In simulations, we compare this new approach with several existing methods. The new approach corrects the bias even when the sample period is limited. Moreover, our proposed standard errors are unbiased, and lead to correct rejection size in finite samples

A Protocol for Factor Identification

We propose a protocol for identifying genuine risk factors. The underlying premise is that a risk factor must be related to the covariance matrix of returns, must be priced in the cross-section of returns, and should yield a reward-to-risk ratio that is reasonable enough to be consistent with risk pricing. A market factor, a profitability factor, and traded versions of macroeconomic factors pass our protocol, but many characteristic-based factors do not. Several of the underlying characteristics, however, do command premiums in the cross-section

Market fragility and international market crashes

We extend the Pukthuanthong and Roll (2009) measure of integration to provide an estimate of systemic risk within international equity markets. Our measure indicates an increasing likelihood of market crashes. The conditional probability of market crashes increases substantially following increases of our risk measure. High levels of our risk measure indicate the probability of a global crash is greater than the probability of a local crash. That is, conditional on high levels of systemic risk, the probability of a sever

Changing Expected Returns Can Induce Spurious Serial Correlation

Changing expected returns can induce spurious autocorrelation in returns and possible bias in other financial models. We show why this happens with some simple examples and then investigate its prevalence in actual equity data. Assets may undergo changes in expected returns for a variety of reasons. Hence, autocorrelations computed from extended records can be subject to spurious bias. The bias might be difficult to measure because of noise but a re-sampling method (the bootstrap) discloses its rather ubiquitous presence in US equity data. Turning the phenomenon on its head, return serial correlation in an efficient market is evidence of changing expected returns. Estimated risk measures such as “beta” might also be subject to bias induced by non-stationary mean returns, but the direction of the bias is more ambiguous