Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

On the Difficulty of Measuring Forecasting Skill in Financial Markets

The use of correlation between forecasts and actual returns is commonplace in the literature, often used as a measurement of investors' skill. A prominent application of this is the concept of the information coefficient (IC). Not only can the IC be used as a tool to rate analysts and fund managers but it also represents an important parameter in the asset allocation and portfolio construction process. Nevertheless, a theoretical understanding of it has typically been limited to the partial equilibrium context where the investing activities of each agent have no effect on other market participants. In this paper we show that this can be an undesirable oversimplification and we demonstrate plausible circumstances in which conventional empirical measurements of IC can be highly misleading. We suggest that improved understanding of IC in a general equilibrium setting can lead to refined portfolio decision making ex ante and more informative analysis of performance ex post. Copyright © 2015 John Wiley & Sons, Ltd.     

Cross‐Section Stock Return and Implied Covariance between Jump and Diffusive Volatility

I examine the information content of option‐implied covariance between jumps and diffusive risk in the cross‐sectional variation in future returns. This paper documents that the difference between realized volatility and implied covariance (RV‐ICov) can predict future returns. The results show a significant and negative association of expected return and realized volatility–implied covariance spread in both the portfolio level analysis and cross‐sectional regression study. A trading strategy of buying a portfolio with the lowest RV‐ICov quintile portfolio and selling with the highest one generates positive and significant returns. This RV‐Cov anomaly is robust to controlling for size, book‐to‐market value, liquidity and systematic risk proportion. Copyright © 2015 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

A new species of Schizopera (Copepoda: Harpacticoida) from Japan,its phylogeny based on the mtCOI gene and comments on the genus Schizoperopsis

The predominantly marine genus Schizopera Sars, 1905 has only two significant inland water species-flocks, one in the ancient African Lake Tanganyika and the other in subterranean waters of Western Australia. Discovery of Schizopera abei sp. nov. from several interstitial locations in the vicinity of the ancient Lake Biwa has wider implications for the study of morphological homoplasies in the genus, as well as for the study of freshwater invasions in harpacticoid copepods. The new Schizopera species belongs to a small group of congeners with a two-segmented endopod of the fourth leg, which used to be recognised as a separate genus, Schizoperopsis Apostolov, 1982. Our reconstructed phylogenies based on the mtCOI partial sequences suggest that this character probably evolved convergently in at least some Schizopera, thus rendering the genus Schizoperopsis polyphyletic. However, almost all basal nodes in our cladograms are weakly supported, which shows limitations of a single-gene approach for reconstructing phylogenetic relationships. The new species is the first member of its genus from Japanese inland waters, and it has no close relatives among extent congeners anywhere in the world. We speculate that its ancestor may have invaded Lake Biwa, and subsequently its surrounding subterranean waters, from brackish areas around central Japan, presumably during a period of high sea water level through its major outflow river. This discovery may provide further support for the hypothesis about the role of ancient lakes as biodiversity pumps for subterranean habitats.

http://zoobank.org/urn:lsid:zoobank.org:pub:1F71F7AD-B7C8-4AD3-BE44-5E1BEE4E2AA8