17 research outputs found
Homogeneous Volatility Bridge Estimators
We present a theory of homogeneous volatility bridge estimators for log-price
stochastic processes. The main tool of our theory is the parsimonious encoding
of the information contained in the open, high and low prices of incomplete
bridge, corresponding to given log-price stochastic process, and in its close
value, for a given time interval. The efficiency of the new proposed estimators
is favorably compared with that of the Garman-Klass and Parkinson estimators.Comment: 25 pages, 9 figure
Hierarchy of temporal responses of multivariate self-excited epidemic processes
Many natural and social systems are characterized by bursty dynamics, for which past events trigger future activity. These systems can be modelled by so-called self-excited Hawkes conditional Poisson processes. It is generally assumed that all events have similar triggering abilities. However, some systems exhibit heterogeneity and clusters with possibly different intra- and inter-triggering, which can be accounted for by generalization into the "multivariate” self-excited Hawkes conditional Poisson processes. We develop the general formalism of the multivariate moment generating function for the cumulative number of first-generation and of all generation events triggered by a given mother event (the "shock”) as a function of the current time t. This corresponds to studying the response function of the process. A variety of different systems have been analyzed. In particular, for systems in which triggering between events of different types proceeds through a one-dimension directed or symmetric chain of influence in type space, we report a novel hierarchy of intermediate asymptotic power law decays ∼ 1/t 1−(m+1)θ of the rate of triggered events as a function of the distance m of the events to the initial shock in the type space, where 0 < θ < 1 for the relevant long-memory processes characterizing many natural and social systems. The richness of the generated time dynamics comes from the cascades of intermediate events of possibly different kinds, unfolding via random changes of types genealog
Theory of Earthquake Recurrence Times
The statistics of recurrence times in broad areas have been reported to obey
universal scaling laws, both for single homogeneous regions (Corral, 2003) and
when averaged over multiple regions (Bak et al.,2002). These unified scaling
laws are characterized by intermediate power law asymptotics. On the other
hand, Molchan (2005) has presented a mathematical proof that, if such a
universal law exists, it is necessarily an exponential, in obvious
contradiction with the data. First, we generalize Molchan's argument to show
that an approximate unified law can be found which is compatible with the
empirical observations when incorporating the impact of the Omori law of
earthquake triggering. We then develop the full theory of the statistics of
inter-event times in the framework of the ETAS model of triggered seismicity
and show that the empirical observations can be fully explained. Our
theoretical expression fits well the empirical statistics over the whole range
of recurrence times, accounting for different regimes by using only the physics
of triggering quantified by Omori's law. The description of the statistics of
recurrence times over multiple regions requires an additional subtle
statistical derivation that maps the fractal geometry of earthquake epicenters
onto the distribution of the average seismic rates in multiple regions. This
yields a prediction in excellent agreement with the empirical data for
reasonable values of the fractal dimension , the average
clustering ratio , and the productivity exponent times the -value of the Gutenberg-Richter law.Comment: 30 pages + 13 figure
Hierarchy of Temporal Responses of Multivariate Self-Excited Epidemic Processes
We present the first exact analysis of some of the temporal properties of
multivariate self-excited Hawkes conditional Poisson processes, which
constitute powerful representations of a large variety of systems with bursty
events, for which past activity triggers future activity. The term
"multivariate" refers to the property that events come in different types, with
possibly different intra- and inter-triggering abilities. We develop the
general formalism of the multivariate generating moment function for the
cumulative number of first-generation and of all generation events triggered by
a given mother event (the "shock") as a function of the current time . This
corresponds to studying the response function of the process. A variety of
different systems have been analyzed. In particular, for systems in which
triggering between events of different types proceeds through a one-dimension
directed or symmetric chain of influence in type space, we report a novel
hierarchy of intermediate asymptotic power law decays of the rate of triggered events as a function of the
distance of the events to the initial shock in the type space, where for the relevant long-memory processes characterizing many natural
and social systems. The richness of the generated time dynamics comes from the
cascades of intermediate events of possibly different kinds, unfolding via a
kind of inter-breeding genealogy.Comment: 40 pages, 8 figure
Distribution of nearest distances between nodal points for the Berry function in two dimensions
According to Berry a wave-chaotic state may be viewed as a superposition of
monochromatic plane waves with random phases and amplitudes. Here we consider
the distribution of nodal points associated with this state. Using the property
that both the real and imaginary parts of the wave function are random Gaussian
fields we analyze the correlation function and densities of the nodal points.
Using two approaches (the Poisson and Bernoulli) we derive the distribution of
nearest neighbor separations. Furthermore the distribution functions for nodal
points with specific chirality are found. Comparison is made with results from
from numerical calculations for the Berry wave function.Comment: 11 pages, 7 figure
Distributions in the physical and engineering sciences
Continuing the authors’ multivolume project, this text considers the theory of distributions from an applied perspective, demonstrating how effective a combination of analytic and probabilistic methods can be for solving problems in the physical and engineering sciences. Volume 1 covered foundational topics such as distributional and fractional calculus, the integral transform, and wavelets, and Volume 2 explored linear and nonlinear dynamics in continuous media. With this volume, the scope is extended to the use of distributional tools in the theory of generalized stochastic processes and fields, and in anomalous fractional random dynamics. Chapters cover topics such as probability distributions; generalized stochastic processes, Brownian motion, and the white noise; stochastic differential equations and generalized random fields; Burgers turbulence and passive tracer transport in Burgers flows; and linear, nonlinear, and multiscale anomalous fractional dynamics in continuous media. The needs of the applied-sciences audience are addressed by a careful and rich selection of examples arising in real-life industrial and scientific labs and a thorough discussion of their physical significance. Numerous illustrations generate a better understanding of the core concepts discussed in the text, and a large number of exercises at the end of each chapter expand on these concepts. Distributions in the Physical and Engineering Sciences is intended to fill a gap in the typical undergraduate engineering/physical sciences curricula, and as such it will be a valuable resource for researchers and graduate students working in these areas. The only prerequisites are a three-four semester calculus sequence (including ordinary differential equations, Fourier series, complex variables, and linear algebra), and some probability theory, but basic definitions and facts are covered as needed. An appendix also provides background material concerning the Dirac-delta and other distributions.Distributions in the Physical and Engineering Sciences is a comprehensive exposition on analytic methods for solving science and engineering problems which is written from the unifying viewpoint of distribution theory and enriched with many modern topics which are important to practitioners and researchers. The goal of the book is to give the reader, specialist and non-specialist usable and modern mathematical tools in their research and analysis. This new text is intended for graduate students and researchers in applied mathematics, physical sciences and engineering. The careful explanations, accessible writing style, and many illustrations/examples also make it suitable for use as a self-study reference by anyone seeking greater understanding and proficiency in the problem solving methods presented. The book is ideal for a general scientific and engineering audience, yet it is mathematically precise.
Most Efficient Homogeneous Volatility Estimators
We present a new theory of homogeneous volatility (and variance) estimators for arbitrary stochastic processes. The main tool of our theory is the parsimonious encoding of all the information contained in the OHLC prices for a given time interval by the joint distributions of the high-minusopen, low-minus-open and close-minus-open values, whose analytical expression is derived exactly for Wiener processes with drift. The efficiency of the new proposed estimators is favorably compared with that of the Garman-Klass, Roger-Satchell and maximum likelihood estimators.Variance and volatility estimators, efficiency, homogeneous functions, Schwarz inequality, extremes of Wiener processes