743 research outputs found
Fourier spectra of measures associated with algorithmically random Brownian motion
In this paper we study the behaviour at infinity of the Fourier transform of
Radon measures supported by the images of fractal sets under an algorithmically
random Brownian motion. We show that, under some computability conditions on
these sets, the Fourier transform of the associated measures have, relative to
the Hausdorff dimensions of these sets, optimal asymptotic decay at infinity.
The argument relies heavily on a direct characterisation, due to Asarin and
Pokrovskii, of algorithmically random Brownian motion in terms of the prefix
free Kolmogorov complexity of finite binary sequences. The study also
necessitates a closer look at the potential theory over fractals from a
computable point of view.Comment: 24 page
The rapid points of a complex oscillation
By considering a counting-type argument on Brownian sample paths, we prove a
result similar to that of Orey and Taylor on the exact Hausdorff dimension of
the rapid points of Brownian motion. Because of the nature of the proof we can
then apply the concepts to so-called complex oscillations (or 'algorithmically
random Brownian motion'), showing that their rapid points have the same
dimension.Comment: 11 page
Distribution of the time at which the deviation of a Brownian motion is maximum before its first-passage time
We calculate analytically the probability density of the time
at which a continuous-time Brownian motion (with and without drift) attains its
maximum before passing through the origin for the first time. We also compute
the joint probability density of the maximum and . In the
driftless case, we find that has power-law tails: for large and for small . In
presence of a drift towards the origin, decays exponentially for large
. The results from numerical simulations are in excellent agreement with
our analytical predictions.Comment: 13 pages, 5 figures. Published in Journal of Statistical Mechanics:
Theory and Experiment (J. Stat. Mech. (2007) P10008,
doi:10.1088/1742-5468/2007/10/P10008
Stochastic Analysis of Gaussian Processes via Fredholm Representation
We show that every separable Gaussian process with integrable variance
function admits a Fredholm representation with respect to a Brownian motion. We
extend the Fredholm representation to a transfer principle and develop
stochastic analysis by using it. We show the convenience of the Fredholm
representation by giving applications to equivalence in law, bridges, series
expansions, stochastic differential equations and maximum likelihood
estimations
An approximation scheme for quasi-stationary distributions of killed diffusions
In this paper we study the asymptotic behavior of the normalized weighted
empirical occupation measures of a diffusion process on a compact manifold
which is killed at a smooth rate and then regenerated at a random location,
distributed according to the weighted empirical occupation measure. We show
that the weighted occupation measures almost surely comprise an asymptotic
pseudo-trajectory for a certain deterministic measure-valued semiflow, after
suitably rescaling the time, and that with probability one they converge to the
quasi-stationary distribution of the killed diffusion. These results provide
theoretical justification for a scalable quasi-stationary Monte Carlo method
for sampling from Bayesian posterior distributions.Comment: v2: revised version, 29 pages, 1 figur
Algorithmic Complexity of Financial Motions
We survey the main applications of algorithmic (Kolmogorov) complexity to the problem of price dynamics in financial markets. We stress the differences between these works and put forward a general algorithmic framework in order to highlight its potential for financial data analysis. This framework is “general" in the sense that it is not constructed on the common assumption that price variations are predominantly stochastic in nature.algorithmic information theory; Kolmogorov complexity; financial returns; market efficiency; compression algorithms; information theory; randomness; price movements; algorithmic probability
Making dynamic modelling effective in economics
Mathematics has been extremely effective in physics, but not in economics beyond finance. To establish economics as science we should follow the Galilean method and try to deduce mathematical models of markets from empirical data, as has been done for financial markets. Financial markets are nonstationary. This means that 'value' is subjective. Nonstationarity also means that the form of the noise in a market cannot be postulated a priroi, but must be deduced from the empirical data. I discuss the essence of complexity in a market as unexpected events, and end with a biological speculation about market growth.Economics; fniancial markets; stochastic process; Markov process; complex systems
- …