GRMHD prediction of coronal variability in accreting black holes

On the basis of data from an energy-conserving 3D general relativistic MHD simulation, we predict the statistical character of variability in the coronal luminosity from accreting black holes. When the inner boundary of the corona is defined to be the electron scattering photosphere, its location depends only on the mass accretion rate in Eddington units (\dot{M}). Nearly independent of viewing angle and \dot{M}, the power spectrum over the range of frequencies from approximately the orbital frequency at the innermost stable circular orbit (ISCO) to ~100 times lower is well approximated by a power-law with index -2, crudely consistent with the observed power spectra of hard X-ray fluctuations in AGN and the hard states of Galactic binary black holes. The underlying physical driver for variability in the light curve is variations in the accretion rate caused by the chaotic character of MHD turbulence, but the power spectrum of the coronal light output is significantly steeper. Part of this contrast is due to the fact that the mass accretion rate can be significantly modulated by radial epicyclic motions that do not result in dissipation, and therefore do not drive luminosity fluctuations. The other part of this contrast is due to the inward decrease of the characteristic inflow time, which leads to decreasing radial coherence length with increasing fluctuation frequency.Comment: Accepted for publication in ApJ, 35 pages, 11 figures (8 color and 3 greyscale), AASTEX. High-resolution versions can be found at the following links: [PS] http://www.pha.jhu.edu/~scn/papers/grmhd_var.ps [PDF] http://www.pha.jhu.edu/~scn/papers/grmhd_var.pd

Risk and Utility in Portfolio Optimization

Modern portfolio theory(MPT) addresses the problem of determining the optimum allocation of investment resources among a set of candidate assets. In the original mean-variance approach of Markowitz, volatility is taken as a proxy for risk, conflating uncertainty with risk. There have been many subsequent attempts to alleviate that weakness which, typically, combine utility and risk. We present here a modification of MPT based on the inclusion of separate risk and utility criteria. We define risk as the probability of failure to meet a pre-established investment goal. We define utility as the expectation of a utility function with positive and decreasing marginal value as a function of yield. The emphasis throughout is on long investment horizons for which risk-free assets do not exist. Analytic results are presented for a Gaussian probability distribution. Risk-utility relations are explored via empirical stock-price data, and an illustrative portfolio is optimized using the empirical data.Comment: 10 pages, 1 figure, presented at 2002 Conference on Econophysics in Bali Indonesi

Portfolio Optimization Using SPEA2 with Resampling

Proceeding of: Intelligent Data Engineering and Automated Learning – IDEAL 2011: 12th International Conference, Norwich, UK, September 7-9, 2011The subject of financial portfolio optimization under real-world constraints is a difficult problem that can be tackled using multiobjective evolutionary algorithms. One of the most problematic issues is the dependence of the results on the estimates for a set of parameters, that is, the robustness of solutions. These estimates are often inaccurate and this may result on solutions that, in theory, offered an appropriate risk/return balance and, in practice, resulted being very poor. In this paper we suggest that using a resampling mechanism may filter out the most unstable. We test this idea on real data using SPEA2 as optimization algorithm and the results show that the use of resampling increases significantly the reliability of the resulting portfolios.The authors acknowledge financial support granted by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR) and Comunidad de Madrid (CCG10- UC3M/TIC-5029).Publicad

Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

Chandra survey in the AKARI North Ecliptic Pole Deep Field. I. X-ray data, point-like source catalog, sensitivity maps, and number counts

We present data products from the 300 ks Chandra survey in the AKARI North Ecliptic Pole (NEP) deep field. This field has a unique set of 9-band infrared photometry covering 2-24 micron from the AKARI Infrared Camera, including mid-infrared (MIR) bands not covered by Spitzer. The survey is one of the deepest ever achieved at ~15 micron, and is by far the widest among those with similar depths in the MIR. This makes this field unique for the MIR-selection of AGN at z~1. We design a source detection procedure, which performs joint Maximum Likelihood PSF fits on all of our 15 mosaicked Chandra pointings covering an area of 0.34 square degree. The procedure has been highly optimized and tested by simulations. We provide a point source catalog with photometry and Bayesian-based 90 per cent confidence upper limits in the 0.5-7, 0.5-2, 2-7, 2-4, and 4-7 keV bands. The catalog contains 457 X-ray sources and the spurious fraction is estimated to be ~1.7 per cent. Sensitivity and 90 per cent confidence upper flux limits maps in all bands are provided as well. We search for optical MIR counterparts in the central 0.25 square degree, where deep Subaru Suprime-Cam multiband images exist. Among the 377 X-ray sources detected there, ~80 per cent have optical counterparts and ~60 per cent also have AKARI mid-IR counterparts. We cross-match our X-ray sources with MIR-selected AGN from Hanami et al. (2012). Around 30 per cent of all AGN that have MID-IR SEDs purely explainable by AGN activity are strong Compton-thick AGN candidates.Comment: 23 pages, 20 figures; catalogs, sensitivity maps, and upper limit flux maps are available from the VizieR Servic

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure

Portfolio Optimization and the Random Magnet Problem

Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movement of assets are are mutually correlated and therefore knowledge of cross--correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is analogous to the problem of finding the magnetization of a random magnet. The interactions for this ``random magnet problem'' are given by the cross-correlation matrix {\bf \sf C} of stock returns. We find that random matrix theory allows us to make an estimate for {\bf \sf C} which outperforms the standard estimate in terms of constructing an investment which carries a minimum level of risk.Comment: 12 pages, 4 figures, revte

Collective behavior of stock price movements in an emerging market

To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, e.g., those belonging to the same business sector, are much weaker. This lack of distinct sector identity in emerging markets is explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE reveals that, the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they are far fewer in number compared to, e.g., NYSE. We show this to be due to the relative weakness of intra-sector interactions between stocks, compared to the market mode, by modeling stock price dynamics with a two-factor model. Our results suggest that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.Comment: 10 pages, 10 figure

Study of stability and control moment gyro wobble damping of flexible, spinning space stations

An executive summary and an analysis of the results are discussed. A user's guide for the digital computer program that simulates the flexible, spinning space station is presented. Control analysis activities and derivation of dynamic equations of motion and the modal analysis are also cited

Multiscaled Cross-Correlation Dynamics in Financial Time-Series

The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of timescale information in the context of risk management