Realized volatility forecasting and market microstructure noise

We extend the analytical results for reduced form realized volatility based forecasting in ABM (2004) to allow for market microstructure frictions in the observed high-frequency returns. Our results build on the eigenfunction representation of the general stochastic volatility class of models developed byMeddahi (2001). In addition to traditional realized volatility measures and the role of the underlying sampling frequencies, we also explore the forecasting performance of several alternative volatility measures designed to mitigate the impact of the microstructure noise. Our analysis is facilitated by a simple unified quadratic form representation for all these estimators. Our results suggest that the detrimental impact of the noise on forecast accuracy can be substantial. Moreover, the linear forecasts based on a simple-to-implement ‘average’ (or ‘subsampled’) estimator obtained by averaging standard sparsely sampled realized volatility measures generally perform on par with the best alternative robust measures.     

Small firms,bigger changes: health insurance coverage take-up rates in small firms after the ACA

Measuring the impact of the Affordable Care Act of 2010 (ACA) on employer-sponsored health insurance is essential in an era of constant changes to health policy. Using data from the Kaiser Family Foundation Employer Survey, we focus on firms with fewer than 50 employees in order to isolate the effect of the ACA on small firms. We utilize a differences‐in‐differences approach with a time fixed effect structure to provide analysis for a treatment group of small firms and a control group of large firms. After excluding firms with grandfathered plans, we find that the ACA provisions reduced health insurance coverage take-up rates in small firms by 1.96 to 2.67 percentage points (compared to large firms).     

ANALYTICAL EVALUATION OF VOLATILITY FORECASTS*

Estimation and forecasting for realistic continuous‐time stochastic volatility models is hampered by the lack of closed‐form expressions for the likelihood. In response, Andersen, Bollerslev, Diebold, and Labys (Econometrica, 71 (2003), 579–625) advocate forecasting integrated volatility via reduced‐form models for the realized volatility, constructed by summing high‐frequency squared returns. Building on the eigenfunction stochastic volatility models, we present analytical expressions for the forecast efficiency associated with this reduced‐form approach as a function of sampling frequency. For popular models like GARCH, multifactor affine, and lognormal diffusions, the reduced form procedures perform remarkably well relative to the optimal (infeasible) forecasts.     

Box–Cox transforms for realized volatility

The log transformation of realized volatility is often preferred to the raw version of realized volatility because of its superior finite sample properties. One of the possible explanations for this finding is the fact the skewness of the log transformed statistic is smaller than that of the raw statistic. Simulation evidence presented here shows that this is the case. It also shows that the log transform does not completely eliminate skewness in finite samples. This suggests that there may exist other nonlinear transformations that are more effective at reducing the finite sample skewness.     

Does Buyer Experience Pay Off? Evidence from eBay

This study explores the relationship between buyer experience and final price in Internet auctions for a homogenous product. We find that more experienced buyers are able to purchase a homogenous product at lower prices, on average, compared to relatively less experienced buyers. The findings are robust to how we account for instances when auctions fail to result in a sale or when they end via the “Buy it Now” option. This result supports findings from the experimental auction literature on auction performance and experience using real auction data.     

Bootstrapping realized multivariate volatility measures

We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as realized regression, covariance and correlation coefficients. We show that the finite sample performance of the bootstrap is superior to the existing first-order asymptotic theory. Nevertheless, and contrary to the existing results in the bootstrap literature for regression models subject to error heteroskedasticity, the Edgeworth expansion for the pairs bootstrap that we develop here shows that this method is not second-order accurate. We argue that this is due to the fact that the conditional mean parameters of realized regression models are heterogeneous under stochastic volatility.     

Examining emotions linked to live chat services: The role of e-service quality and impact on word of mouth

The purpose of this article is twofold: (1) to investigate the emotions experienced by banking customers when using live chat services and their impact on positive word-of-mouth intentions; and, (2) to identify which dimensions of e-service quality (accessibility, customer service and support, perceived security/privacy and design) impact these emotions. A survey was conducted with 682 members of a Web-based panel. Findings demonstrate that emotions, both positive and negative, significantly impact word of mouth. Results further reveal that customer service and support followed by design of live chat services mostly influence consumer emotions. On a practical level, the recommendations provided will improve consumer experience and encourage consumers to use live chat services. This is all the more important since such services are now part of the promotion and contribution of a sustainable mode of consumption that is accessible to all.