Predicting the equity market with option-implied variables

We comprehensively analyze the predictive power of several option-implied variables for monthly S&P 500 excess returns and realized variance. The correlation risk premium (CRP) and the variance risk premium (VRP) emerge as strong predictors of both excess returns and realized variance. This is true both in- and out-of-sample. Our results also reveal that statistical evidence of predictability does not necessarily lead to economic gains. However, a timing strategy based on the CRP leads to utility gains of more than 5.03% per annum. Forecast combinations provide stable forecasts for both excess returns and realized variance, and add economic value.     

Lump-Sum Payments and Retail Services: A Relational Contracting Perspective*

How should manufacturers motivate their retailers to provide customer services? The vertical restraints literature tells us that retail competition distorts service incentives in the short run. We consider how repeated interaction mitigates this problem, and particularly how a manufacturer can provide service incentives with discretionary lump-sum payments. We find that these payments may allow the manufacturer to sustain optimal service levels even if retailers are very impatient. We also show that banning reverse lump-sum payments may deprive consumers of the chance to enjoy high-quality services, and thereby reduce their welfare.     

Time and causality: A Monte Carlo assessment of the timing-of-events approach

We present new Monte Carlo evidence regarding the feasibility of separating causality from selection within non-experimental duration data, by means of the non-parametric maximum likelihood estimator (NPMLE). Key findings are: (i) the NPMLE is extremely reliable, and it accurately separates the causal effects of treatment and duration dependence from sorting effects, almost regardless of the true unobserved heterogeneity distribution; (ii) the NPMLE is normally distributed, and standard errors can be computed directly from the optimally selected model; and (iii) unjustified restrictions on the heterogeneity distribution, e.g., in terms of a pre-specified number of support points, may cause substantial bias.     

Financial data science: the birth of a new financial research paradigm complementing econometrics?

Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an individual coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.     

The term structure of systematic and idiosyncratic risk

We study the term structure of variance (total risk), systematic, and idiosyncratic risk. Consistent with the expectations hypothesis, we find that, for the entire market, the slope of the term structure of variance is mainly informative about the path of future variance. Thus, there is little indication of a time-varying term premium. Turning the focus to individual stocks, we cannot reject the expectations hypothesis for systematic variance, but we strongly reject it for idiosyncratic variance. Our results are robust to jumps and potential statistical biases.     

Job search incentives and job match quality

We examine the impacts of time-limited unemployment insurance (UI) and active labor market programs (ALMP) on the duration and outcome of job search in Norway. We use a comprehensive simultaneous equations model accounting for i) the duration of unemployment spells; ii) their outcomes, iii) subsequent employment stability; and iv) the earnings level associated with the first job. We find that time invested in job search pays off in form of higher earnings once a job match is formed. ALMP raises the probability of eventually finding a job as well as expected earnings, but at the cost of lengthening job search.