Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Equity trading volume and volatility: Latent information arrivals and common long-run dependencies

This article examines the behavior of equity trading volume and volatility for the individual firms composing the Standard & Poor's 100 composite index. Using multivariate spectral methods, we find that fractionally integrated processes best describe the long-run temporal dependencies in both series. Consistent with a stylized mixture-of-distributions hypothesis model in which the aggregate "news"-arrival process possesses long-memory characteristics, the long-run hyperbolic decay rates appear to be common across each volume-volatility pair.

Estimating stochastic volatility diffusion using conditional moments of integrated volatility

We exploit the distributional information contained in high-frequency intraday data in constructing a simple conditional moment estimator for stochastic volatility diffusions. The estimator is based on the analytical solutions of the first two conditional moments for the latent integrated volatility, the realization of which is effectively approximated by the sum of the squared high-frequency increments of the process. Our simulation evidence indicates that the resulting GMM estimator is highly reliable and accurate. Our empirical implementation based on high-frequency five-minute foreign exchange returns suggests the presence of multiple latent stochastic volatility factors and possible jumps. © 2002 Elsevier Science B.V. All rights reserved.

Long-term equity anticipation securities and stock market volatility dynamics

Recent empirical findings suggest that the long-run dependence in U.S. stock market volatility is best described by a slowly mean-reverting fractionally integrated process. The present study complements this existing time-series-based evidence by comparing the risk-neutralized option pricing distributions from various ARCH-type formulations. Utilizing a panel data set consisting of newly created exchange traded long-term equity anticipation securities, or leaps, on the Standard and Poor's 500 stock market index with maturity times ranging up to three years, we find that the degree of mean reversion in the volatility process implicit in these prices is best described by a Fractionally Integrated EGARCH (FIEGARCH) model. © 1999 Elsevier Science S.A. All rights reserved.

Volume, volatility, and leverage: A dynamic analysis

This paper uses dynamic impulse response analysis to investigate the interrelationships among stock price volatility, trading volume, and the leverage effect. Dynamic impulse response analysis is a technique for analyzing the multi-step-ahead characteristics of a nonparametric estimate of the one-step conditional density of a strictly stationary process. The technique is the generalization to a nonlinear process of Sims-style impulse response analysis for linear models. In this paper, we refine the technique and apply it to a long panel of daily observations on the price and trading volume of four stocks actively traded on the NYSE: Boeing, Coca-Cola, IBM, and MMM.

Bid-ask spreads and volatility in the foreign exchange market. An empirical analysis

Consistent with the implications from a simple asymmetric information model for the bid-ask spread, we present empirical evidence that the size of the bid-ask spread in the foreign exchange market is positively related to the underlying exchange rate uncertainty. The estimation results are based on an ordered probit analysis that captures the discreteness in the spread distribution, with the uncertainty of the spot exchange rate being quantified through a GARCH type model. The data sets consists of more than 300,000 continuously recorded Deutschemark/dollar quotes over the period from April 1989 to June 1989. © 1994.

Simultaneous two-wavelength transmission quantitative phase microscopy with a color camera.

We present a quantitative phase microscopy method that uses a Bayer mosaic color camera to simultaneously acquire off-axis interferograms in transmission mode at two distinct wavelengths. Wrapped phase information is processed using a two-wavelength algorithm to extend the range of the optical path delay measurements that can be detected using a single temporal acquisition. We experimentally demonstrate this technique by acquiring the phase profiles of optically clear microstructures without 2pi ambiguities. In addition, the phase noise contribution arising from spectral channel crosstalk on the color camera is quantified.

Modeling the evolution of regulatory elements by simultaneous detection and alignment with phylogenetic pair HMMs.

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation.

Simultaneous transcranial magnetic stimulation and single-neuron recording in alert non-human primates.

Transcranial magnetic stimulation (TMS) is a widely used, noninvasive method for stimulating nervous tissue, yet its mechanisms of effect are poorly understood. Here we report new methods for studying the influence of TMS on single neurons in the brain of alert non-human primates. We designed a TMS coil that focuses its effect near the tip of a recording electrode and recording electronics that enable direct acquisition of neuronal signals at the site of peak stimulus strength minimally perturbed by stimulation artifact in awake monkeys (Macaca mulatta). We recorded action potentials within ∼1 ms after 0.4-ms TMS pulses and observed changes in activity that differed significantly for active stimulation as compared with sham stimulation. This methodology is compatible with standard equipment in primate laboratories, allowing easy implementation. Application of these tools will facilitate the refinement of next generation TMS devices, experiments and treatment protocols.

Integrating simultaneous prosocial and antisocial behavior into theories of collective action.

Trust and cooperation constitute cornerstones of common-pool resource theory, showing that "prosocial" strategies among resource users can overcome collective action problems and lead to sustainable resource governance. Yet, antisocial behavior and especially the coexistence of prosocial and antisocial behaviors have received less attention. We broaden the analysis to include the effects of both "prosocial" and "antisocial" interactions. We do so in the context of marine protected areas (MPAs), the most prominent form of biodiversity conservation intervention worldwide. Our multimethod approach relied on lab-in-the-field economic experiments (n = 127) in two MPA and two non-MPA communities in Baja California, Mexico. In addition, we deployed a standardized fishers' survey (n = 544) to verify the external validity of our findings and expert informant interviews (n = 77) to develop potential explanatory mechanisms. In MPA sites, prosocial and antisocial behavior is significantly higher, and the presence of antisocial behavior does not seem to have a negative effect on prosocial behavior. We suggest that market integration, economic diversification, and strengthened group identity in MPAs are the main potential mechanisms for the simultaneity of prosocial and antisocial behavior we observed. This study constitutes a first step in better understanding the interaction between prosociality and antisociality as related to natural resources governance and conservation science, integrating literatures from social psychology, evolutionary anthropology, behavioral economics, and ecology.