Walter Distaso

Platform competition and broadband uptake: Theory and empirical evidence from the European union

Abstract Broadband access provides users with high speed, always-on connectivity to the Internet. Due to its superiority, broadband is seen as the way for consumers and firms to exploit the great potentials of new applications. This has generated a policy debate on how to stimulate adoption of broadband technology. One of the most disputed issues is about competition policies: these may be intended to promote competition in the Digital Subscriber Line (DSL) segment of the market (intra-platform competition), or to stimulate entry into the market for alternative platforms such as cable access or fiber optics (inter-platform competition). Using a model of oligopoly competition between differentiated products, our paper explicitly studies the effects of inter- and intra-platform competition on the diffusion of broadband access. The implications of the model are then tested using data from 14 European countries. The econometric evidence confirms the results of the theoretical model and indicates that while inter-platform competition drives broadband adoption, competition in the market for DSL services does not play a significant role. The results also confirm that lower unbundling prices stimulate broadband uptake.

Assessing Market Microstructure Effects via Realized Volatility Measures with an Application to the Dow Jones Industrial Average Stocks

Transaction prices of financial assets are contaminated by market microstructure effects. This is particularly relevant when estimating volatility using high frequency data. In this article, we assess statistically the effect of microstructure noise on volatility estimators, and test the hypothesis that its variance is independent of the sampling frequency. We provide evidence based on the Dow Jones Industrial Average stocks. We find that noise has a statistically significant effect on volatility estimators at frequencies of 2–3 min or higher. The independently and identically distributed specification with constant variance seems to be a plausible model for microstructure noise, except for ultra high frequencies.

Predictive Inference for Integrated Volatility

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.

Asymptotic Normality for Weighted Sums of Linear Processes

We establish asymptotic normality of weighted sums of stationary linear processes with general triangular array weights and when the innovations in the linear process are martingale differences. The results are obtained under minimal conditions on the weights and as long as the process of conditional variances of innovations is covariance stationary with lag k auto-covariances tending to zero, as k tends to infinity. We also obtain weak convergence of weighted partial sum processes. The results are applicable to linear processes that have short or long memory or exhibit seasonal long memory behavior. In particular they are applicable to GARCH and ARCH(∞) models. They are also useful in deriving asymptotic normality of kernel estimators of a nonparametric regression function when errors may have long memory.

Corruption and Referee Bias in Football: The Case of Calciopoli

Based on the Calciopoli scandal, which uncovered widespread corruption in Italian football, this paper quantifies the effect of referee bias on the performance of football teams. The impartiality of referees is often distorted by external factors which exert some emotional pressure in order to influence their decisions. On the other hand, corrupt referees consciously and deliberately try to distort the results of the sport contest, in order to favor the corrupting teams. Building on the implications of a model where performance in a sport contest depends on both effort and bribing, our results highlight the different effects of these two forms of bias, and help to shed light on several aspects of the corruption scandal.

Semiparametric Estimation and Inference for Trending I(D) and Related Processes

This paper deals with estimation and hypothesis testing in models allowing for trending processes that are possibly nonstationary, nonlinear, and non-Gaussian. Using semi-parametric estimators, we obtain asymptotic confidence intervals for the trend and memory parameters, and we develop joint hypothesis testing for these. The confidence intervals are applicable for a wide class of processes, exhibit good coverage accuracy, and are easy to implement.

Tailing Tail Risk in the Hedge Fund Industry

This paper aims to assess dynamic tail risk exposure in the hedge fund sector using daily data. We use a copula function to model both lower and upper tail dependence between hedge-fund and broad-market returns as a function of market uncertainty. We proxy the latter by means of a single index that combines the options-implied market volatility, the volatility risk premium, and the swap and term spreads. We find substantial time-variation in both lower- and upper-tail dependence, even for hedge fund styles that exhibit little unconditional tail dependence. In particular, dependence between hedge fund and equity market returns decreases in both tails significantly with market uncertainty. There are only a few styles that feature neither unconditional nor conditional tail dependence, e.g., convertible arbitrage and equity market neutral. We also fail to observe any tail dependence with bond and currency markets, though we find strong evidence that the lower-tail risk exposure of macro hedge funds to commodity markets increases with liquidity risk. Finally, further analysis shows mixed evidence on how much hedge funds contribute to systemic risk.

Multivariate Mixture-of-Normals Hypothesis in Exchange Rates

This paper proposes methods for testing the multivariate mixture of normals hypothesis. It uses multivariate measures of integrated variance to standardize (in a matrix sense) daily returns. Because replacing the unobserved integrated covariance by its estimator introduces a finite-sample distortion, a correction is implemented to alleviate this problem. In the empirical application to foreign exchange rates, the mixture-of-normals hypothesis is soundly rejected. The rejection appears to be due to the deviations of the marginals from standard normality rather than the dependence structure being inconsistent with a normal copula. To shed more light on the joint distribution of the Euro, Dollar and Yen exchange rates, a five-equation parametric model is estimated for the bivariate standardized returns, realized volatilities and realized correlation, allowing for time-varying volatility of the realized measures as well as non-normal innovations. The model fits data well and uncovers nonlinear dependencies between return and volatility innovations.