Sunspot dynamics

The goal of this research was the understanding of the various oscillatory, transient, and quasi-steady motions in sunspots and the basic structure of a sunspot. The research involved both theoretical modeling (based on thermohydrodynamic theory) and observations of dynamical phenomena in sunspots. The principal topics of the research were sunspot seismology (the interaction of solar p-modes with a sunspot as a probe of the subsurface structure of a sunspot); three minute umbral oscillations and their relation to the structure of the umbral atmosphere; siphon flows in isolated magnetic flux tubes and their relation to the photospheric Evershed flow and to intense magnetic elements outside of sunspots; and more general theoretical work on magneto-atmospheric waves. Here, a summary of results is given

Sunspot dynamics

This report describes recent results of our theoretical and observational work on dynamical phenomena in sunspots. The overall goal of this research has been a better understanding of the various oscillatory, transient, and steady motions in a sunspot and their relation to the basic structure of the sunspot. The principal topics of the research reported here are the following: (1) sunspot seismology, i.e., the study of the interaction of solar p-modes with a sunspot as a probe of the subsurface structure of a sunspot; (2) local sources of acoustic waves in the solar photosphere; and (3) siphon flows in isolated magnetic flux tubes and their relation to the photospheric Evershed flow and to intense magnetic elements outside of sunspots

Experimental Progress in Computation by Self-Assembly of DNA Tilings

Approaches to DNA-based computing by self-assembly require the use of D. T A nanostructures, called tiles, that have efficient chemistries, expressive computational power: and convenient input and output (I/O) mechanisms. We have designed two new classes of DNA tiles: TAO and TAE, both of which contain three double-helices linked by strand exchange. Structural analysis of a TAO molecule has shown that the molecule assembles efficiently from its four component strands. Here we demonstrate a novel method for I/O whereby multiple tiles assemble around a single-stranded (input) scaffold strand. Computation by tiling theoretically results in the formation of structures that contain single-stranded (output) reported strands, which can then be isolated for subsequent steps of computation if necessary. We illustrate the advantages of TAO and TAE designs by detailing two examples of massively parallel arithmetic: construction of complete XOR and addition tables by linear assemblies of DNA tiles. The three helix structures provide flexibility for topological routing of strands in the computation: allowing the implementation of string tile models

Siphon flows in isolated magnetic flux tubes. 3: The equilibrium path of the flux tube arch

The arched equilibrium path of a thin magnetic flux tube in a plane-stratified, nonmagnetic atmosphere is calculated for cases in which the flux tube contains a steady siphon flow. The large scale mechanical equilibrium of the flux tube involves a balance among the magnetic buoyancy force, the net magnetic tension force due to the curvature of the flux tube axis, and the inertial (centrifugal) force due to the siphon flow along curved streamlines. The ends of the flux tube are assumed to be pinned down by some other external force. Both isothermal and adiabatic siphon flows are considered for flux tubes in an isothermal external atmosphere. For the isothermal case, in the absence of a siphon flow the equilibrium path reduces to the static arch calculated by Parker (1975, 1979). The presence of a siphon flow causes the flux tube arch to bend more sharply, so that magnetic tension can overcome the additional straightening effect of the inertial force, and reduces the maximum width of the arch. The curvature of the arch increases as the siphon flow speed increases. For a critical siphon flow, with supercritical flow in the downstream leg, the arch is asymmetric, with greater curvature in the downstream leg of the arch. Adiabatic flow have qualitatively similar effects, except that adiabatic cooling reduces the buoyancy of the flux tube and thus leads to significantly wider arches. In some cases the cooling is strong enough to create negative buoyancy along sections of the flux tube, requiring upward curvature of the flux tube path along these sections and sometimes leading to unusual equilibrium paths of periodic, sinusoidal form

New species of Parmeliaceae (lichenized Ascomycotina) from South America

The species Flavoparmelia quichuaensis Elix & T. H. Nash, Hypotrachyna divaricatica Elix & T. H. Nash, Hypotrachyna goiasii Elix & Nash, Hypotrachyna hypoalectorialica Elix & T. H. Nash and Relicina xanthoparmeliformis Elix & T. H. Nash are described as new to science

Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions?

Many finance questions require the predictive distribution of returns. We propose a bivariate model of returns and realized volatility (RV), and explore which features of that time-series model contribute to superior density forecasts over horizons of 1 to 60 days out of sample. This term structure of density forecasts is used to investigate the importance of: the intraday information embodied in the daily RV estimates; the functional form for log(RV ) dynamics; the timing of information availability; and the assumed distributions of both return and log(RV) innovations. We find that a joint model of returns and volatility that features two components for log(RV) provides a good fit to S&P 500 and IBM data, and is a significant improvement over an EGARCH model estimated from daily returnsRealized Volatility, multiperiod out-of-sample prediction, term structure of density forecasts, Stochastic Volatility

How useful are historical data for forecasting the long-run equity return distribution?

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different history of data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns

How useful are historical data for forecasting the long-run equity return distribution?

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probability-weighted average of submodels, each of which is estimated over a different historyof data. The paper illustrates the importance of uncertainty about structural breaks and the value of modeling higher-order moments of excess returns when forecasting the return distribution and its moments. The shape of the long-run distribution and the dynamics of the higher-order moments are quite different from those generated by forecasts which cannot capture structural breaks. The empirical results strongly reject ignoring structural change in favor of our forecasts which weight historical data to accommodate uncertainty about structural breaks. We also strongly reject the common practice of using a fixed-length moving window. These differences in long-run forecasts have implications for many financial decisions, particularly for risk management and long-run investment decisions.density forecasts, structural change, model risk, parameter uncertainty, Bayesian learning, market returns

Nonlinear Features of Realized FX Volatility

This paper investigates nonlinear features of FX volatility dynamics using estimates of daily volatility based on the sum of intraday squared returns. Measurement errors associated with using realized volatility to measure ex post latent volatility imply that standard time series models of the conditional variance become variants of an ARMAX model. We explore nonlinear departures from these linear specifications using a doubly stochastic process under duration-dependent mixing. This process can capture large abrupt changes in the level of volatility, time varying persistence, and time-varying variance of volatility. The results have implications for forecast precision, hedging, and pricing of derivatives. Dans cet article, nous étudions les caractéristiques nonlinéaires de la dynamique de la volatilité des taux de change à l'aide d'estimations de la volatilité quotidienne basées sur la somme du carré des rendements intraquotidiens. Les erreurs de mesure commises en utilisant la volatilité réalisée pour mesurer la volatilité latente ex post font en sorte que les modèles standards de séries chronologiques de la variance conditionnelle deviennent des variantes d'un modèle ARMAX. Nous explorons des alternatives nonlinéaires à ces spécifications linéaires en utilisant un processus doublement stochastique, avec mixage dépendant de la durée. Ce processus peut capter des changements importants et abrupts dans le niveau de la volatilité, de même qu'une persistence et une variance de la volatilité variant dans le temps. Nos résultats influent sur la précision des prévisions, la couverture et l'évaluation des produits dérivés.High-frequency data, realized volatility, semi-Marko, Données à haute fréquence, volatilité réalisée, demi-Markov

News Arrival, Jump Dynamics and Volatility Components for Individual Stock Returns

This paper models different components of the return distribution which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. This mixture captures occasional large changes in price, due to the impact of news innovations such as earnings surprises, as well as smoother changes in prices which can result from liquidity trading or strategic trading as information disseminates. Unlike typical SV-jump models, previous realizations of both jump and normal innovations can feedback asymmetrically into expected volatility. This is a new source of asymmetry (in addition to good versus bad news) that improves forecasts of volatility particularly after large moves such as the '87 crash. A heterogeneous Poisson process governs the likelihood of jumps and is summarized by a time varying conditional intensity parameter. The model is applied to returns from individual companies and three indices. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, contemporaneous and lagged leverage effects, the time-series dynamics of jump clustering, and the importance of modeling the dynamics of jumps around high volatility episodes. Cet article modélise les différentes composantes de la distribution des rendements qui sont supposés être régis par un processus latent de nouvelles. La variance conditionnelle des rendements est une combinaison de sauts et de composantes qui varient continûment. Ce mélange permet de capter les grands changements occasionnels de prix qui sont dus à l'impact des nouvelles, telles que des surprises dans les revenus d'une compagnie, aussi bien que des changements plus lisses des prix qui peuvent résulter de transactions de liquidité ou de transactions stratégiques au fur et à mesure que l'information est disséminée. À la différence des modèles classique de sauts SV, les réalisations précédentes des sauts et des innovations normales peuvent intervenir asymétriquement dans la volatilité espérée. Il s'agit d'une nouvelle source d'asymétrie qui améliore les prévisions de volatilité, en particulier après de grands mouvements tels que le crash de 87. Un processus de Poisson hétérogène régit la probabilité des sauts et est représenté par un paramètre d'intensité conditionnelle qui varie dans le temps. Le modèle est appliqué aux rendements de différentes compagnies et à trois indices. Nous montrons ainsi empiriquement l'impact et les effets de rétroaction des sauts par rapport aux innovations normales, les effets de leviers simultanés et décalés, la dynamique de série temporelle du groupement des sauts, et l'importance de modéliser la dynamique des sauts dans les périodes de volatilité élevée.volatility components, news impacts, conditional jump intensity, jump size, leverage effects, filter, composantes de volatilité, impact des nouvelles, intensité conditionnelle des sauts, taille des sauts, effets de levier, filtre