Volatility Forecast Comparison using Imperfect Volatility Proxies

The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We derive necessary and sufficient conditions on functional form of the loss function for the ranking of competing volatility forecasts to be robust to the presence of noise in the volatility proxy, and derive some interesting special cases of this class of “robust” loss functions. We motivate the theory with analytical results on the distortions caused by some widely-used loss functions, when used with standard volatility proxies such as squared returns, the intra-daily range or realised volatility. The methods are illustrated with an application to the volatility of returns on IBM over the period 1993 to 2003.forecast evaluation; forecast comparison; loss functions; realised Variance; range

Testable implications of forecast optimality

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. This paper considers properties of optimal forecasts under general loss functions and establishes new testable implications of forecast optimality. These hold when the forecaster’s loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric estimation of the forecaster’s loss function, and obtain a test of forecast optimality via a test of over-identifying restrictions

Testable Implications of Forecast Optimality

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. This paper considers properties of optimal forecasts under general loss functions and establishes new testable implications of forecast optimality. These hold when the forecaster's loss function is unknown but testable restrictions can be imposed on the data generating process, trading off conditions on the data generating process against conditions on the loss function. Finally, we propose flexible parametric estimation of the forecaster's loss function, and obtain a test of forecast optimality via a test of over-identifying restrictions.forecast evaluation, loss function, rationality tests

Properties of Optimal Forecasts

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with increasing variance as the forecast horizon grows. Using analytical results, we show in this paper that all the standard properties of optimal forecasts can be invalid under asymmetric loss and nonlinear data generating processes and thus may be very misleading as a benchmark for an optimal forecast. Our theoretical results suggest that many of the conclusions in the empirical literature concerning suboptimality of forecasts could be premature. We extend the properties that an optimal forecast should have to a more general setting than previously considered in the literature. We also present results on forecast error properties that may be tested when the forecaster's loss function is unknown, and introduce a change of measure, following which the optimum forecast errors for general loss functions have the same properties as optimum errors under MSE lossforecast evaluation, loss function, rationality, efficient markets

Photoemission Spectroscopy Studies of New Topological Insulator Materials

Title from PDF of title page, viewed on August 10, 2015Dissertation advisor: Anthony CarusoVitaIncludes bibliographic references (pages 141-147)Thesis (Ph.D.)--Department of Physics and Astronomy and Department of Chemistry. University of Missouri--Kansas City, 2015As the size of a solid shrinks, the ratio of surface area to bulk volume grows and surface effects become more important. In a world where technologies advance with the shrinking size of electronic devices, one phase of matter has emerged which is fit for the near future of surface-dominated performance. Moreover, it has brought a new set of ideas to solid-state physics and chemistry, especially the understanding that the discipline of topology can be applied to classify the electron band structures. The topological insulator phase yields an exotic metal surface state in which the orientation of the electron’s spin is locked perpendicular to its momentum. This property suppresses backscattering (making it possible to pass spin-polarized currents through the material without loss), offers a crucial ingredient for innovative approaches to quantum computation, and provides the basis for observing unique magnetoelectric effects. However, the surface states of materials in the topological insulator phase can wildly differ, so it is of interest to systematically characterize new materials to understand how the structure in position-space is related to the spin-resolved structure of electrons in energy- and momentum-space. We will discuss this relationship as it is probed through spin- and angle-resolved photoemission spectroscopy experiments on three topological (Bi₂)m(Bi₂Se₃)n superlattices: (a) Bi₂Se₃ (m = 0, n = 1), (b) Bi₄Se₃ (m = 1, n = 1), and (c) BiSe (m = 1, n = 2). Our studies have not only proven the topological nature of these materials, but also demonstrate how bulk band structure and polar chemical bonding control the surface metal’s concentration, dispersion, and spin-orbital character. Case (a) is considered to provide an ideal model of the topological surface metal. Case (b) provides the three important findings: (1) the chemical identity of the surface-termination controls the orbital composition and energy distribution of the surface states, (2) there are two topological states in sequential bulk band gaps, (3) of these, one of topological state undergoes a hybridization effect that yields a momentum-dependent gap in the band structure as large as 85 meV. Case (c) has a practical significance in that the surface metal has a potentially record-breaking carrier density of ~10¹³cm⁻² (estimated from the Fermi surface area), more than an order of magnitude higher than in Bi₂Se₃. This occurs as a result of charge transfer from the Bi₂ layers to the Bi₂Se₃ layers.Introduction -- Electron spectroscopies applied to bismuth cahlcogenides -- Topological semimetal composed of bismuth-bilayers and bismuth selenide layers stacked in a 1:1 ratio -- Topological insulator composed of bismuth-bilayers and bismuth selenide layers stacked in a 1:2 ration -- Summary and outloo

Company news affects the way in which a stock’s returns co-move with those of other firms

The degree of co-movement signals the stock’s systematic risk, write Michela Verardo and Andrew Patto

Generalized Autoregressive Score Trees and Forests

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal et. al, 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods significantly outperform the baseline GAS model. In our applications to stock return volatility and density prediction, the optimal GAS tree model reveals a leverage effect and a variance risk premium effect. Our study of stock-bond dependence finds evidence of a flight-to-quality effect in the optimal GAS forest forecasts, while our analysis of high-frequency trade durations uncovers a volume-volatility effect

Head and neck injury risks in heavy metal: head bangers stuck between rock and a hard bass

Objective To investigate the risks of mild traumatic brain injury and neck injury associated with head banging, a popular dance form accompanying heavy metal music