Jumps and betas: A new framework for disentangling and estimating systematic risks

We provide a new theoretical framework for disentangling and estimating the sensitivity towards systematic diffusive and jump risks in the context of factor models. Our estimates of the sensitivities towards systematic risks, or betas, are based on the notion of increasingly finer sampled returns over fixed time intervals. We show consistency and derive the asymptotic distributions of our estimators. In an empirical application of the new procedures involving high-frequency data for forty individual stocks, we find that the estimated monthly diffusive and jump betas with respect to an aggregate market portfolio differ substantially for some of the stocks in the sample.     

Estimating fixed-effect panel stochastic frontier models by model transformation

Traditional panel stochastic frontier models do not distinguish between unobserved individual heterogeneity and inefficiency. They thus force all time-invariant individual heterogeneity into the estimated inefficiency. Greene (2005) proposes a true fixed-effect stochastic frontier model which, in theory, may be biased by the incidental parameters problem. The problem usually cannot be dealt with by model transformations owing to the nonlinearity of the stochastic frontier model. In this paper, we propose a class of panel stochastic frontier models which create an exception. We show that first-difference and within-transformation can be analytically performed on this model to remove the fixed individual effects, and thus the estimator is immune to the incidental parameters problem. Consistency of the estimator is obtained by either N→∞$N\to \infty$ or T→∞$T\to \infty$, which is an attractive property for empirical researchers.     

Bootstrapping I(1) data

A functional law is given for an I(1) sample data version of the continuous-path block bootstrap of Paparoditis and Politis (2001a). The results provide an alternative demonstration that continuous-path block bootstrap unit root tests are consistent under the null.     

Knowledge spillovers in US patents: A dynamic patent intensity model with secret common innovation factors

During the past two decades, innovations protected by patents have played a key role in business strategies. This fact enhanced studies of the determinants of patents and the impact of patents on innovation and competitive advantage. Sustaining competitive advantages is as important as creating them. Patents help sustaining competitive advantages by increasing the production cost of competitors, by signaling a better quality of products and by serving as barriers to entry. If patents are rewards for innovation, more R&D should be reflected in more patent applications but this is not the end of the story. There is empirical evidence showing that patents through time are becoming easier to get and more valuable to the firm due to increasing damage awards from infringers. These facts question the constant and static nature of the relationship between R&D and patents. Furthermore, innovation creates important knowledge spillovers due to its imperfect appropriability. Our paper investigates these dynamic effects using US patent data from 1979 to 2000 with alternative model specifications for patent counts. We introduce a general dynamic count panel data model with dynamic observable and unobservable spillovers, which encompasses previous models, is able to control for the endogeneity of R&D and therefore can be consistently estimated by maximum likelihood. Apart from allowing for firm specific fixed and random effects, we introduce a common unobserved component, or secret stock of knowledge, that affects differently the propensity to patent of each firm across sectors due to their different absorptive capacity.     

Inconsistency of the MLE and inference based on weighted LS for LARCH models

This paper considers a class of finite-order autoregressive linear ARCH models. The model captures the leverage effect, allows the volatility to be arbitrarily close to zero and to reach its minimum for non-zero innovations, and is appropriate for long memory modeling when infinite orders are allowed. However, the (quasi-)maximum likelihood estimator is, in general, inconsistent. A self-weighted least-squares estimator is proposed and is shown to be asymptotically normal. A score test for conditional homoscedasticity and diagnostic portmanteau tests are developed. Their performance is illustrated via simulation experiments. It is also investigated whether stock market returns exhibit some of the characteristic features of the linear ARCH model.     

Dominating estimators for minimum-variance portfolios

In this paper, we derive two shrinkage estimators for minimum-variance portfolios that dominate the traditional estimator with respect to the out-of-sample variance of the portfolio return. The presented results hold for any number of assets d≥4$d\ge 4$ and number of observations n≥d+2$n\ge d+2$. The small-sample properties of the shrinkage estimators as well as their large-sample properties for fixed d$d$ but n→∞$n\to \infty$ and n,d→∞$n,d\to \infty$ but n/d→q≤∞$n/d\to q\le \infty$ are investigated. Furthermore, we present a small-sample test for the question of whether it is better to completely ignore time series information in favor of naive diversification.     

Threshold bipower variation and the impact of jumps on volatility forecasting

This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump.     

ON THE “PRO‐POORNESS” OF GROWTH IN A MULTIDIMENSIONAL CONTEXT

This paper represents a first attempt to bring together the issues of multidimensional poverty and growth “pro‐poorness” assessments. More specifically, we suggest the use of sequential dominance procedures to test the “pro‐poorness” of observed growth spells when poverty is measured on the basis of income and another discrete well‐being attribute. Sequential procedures are also used to obtain graphical tools that are consistent with the spirit of Ravallion and Chen's growth incidence curve and Son's poverty growth curve. Contrary to traditional unidimensional tests, our method makes it possible to take into account the importance of deprivation correlations at the individual level and thus may reverse results observed with the traditional tools used to check the “pro‐poorness” of growth. An illustration of our approach is given using Turkish data for the period 2003–05.     

Durable goods produced by state owned enterprises

A two-period durable-goods monopoly model is analyzed where the durable good is provided by a state owned enterprise (SOE). First, we suppose that the SOE is under pressure to provide employment, and therefore has an employment goal, as well as the traditional profit and consumer surplus objectives. Assuming that the SOE has difficulty committing to current buyers with respect to its profit and employment motives, we find that as the employment burden increases, the SOE tends to move further away from the efficient durability and provides a lower durability level than a pure profit maximizer. Additionally, we show that a durable-goods SOE without commitment power, will wish to partially privatize to help mitigate its commitment problem with buyers and increase social welfare. Both of these findings provide economic rationale for the partial privatization of SOEs in transitioning economies that have not been identified in the literature prior to this.