767 research outputs found
On worst-case investment with applications in finance and insurance mathematics
We review recent results on the new concept of worst-case portfolio optimization, i.e. we consider the determination of portfolio processes which yield the highest worst-case expected utility bound if the stock price may have uncertain (down) jumps. The optimal portfolios are derived as solutions of non-linear differential equations which itself are consequences of a Bellman principle for worst-case bounds. They are by construction non-constant ones and thus differ from the usual constant optimal portfolios in the classical examples of the Merton problem. A particular application of such strategies is to model crash possibilities where both the number and the height of the crash is uncertain but bounded. We further solve optimal investment problems in the presence of an additional risk process which is the typical situation of an insurer
Testing the Gaussian Copula Hypothesis for Financial Assets Dependences
Using one of the key property of copulas that they remain invariant under an
arbitrary monotonous change of variable, we investigate the null hypothesis
that the dependence between financial assets can be modeled by the Gaussian
copula. We find that most pairs of currencies and pairs of major stocks are
compatible with the Gaussian copula hypothesis, while this hypothesis can be
rejected for the dependence between pairs of commodities (metals).
Notwithstanding the apparent qualification of the Gaussian copula hypothesis
for most of the currencies and the stocks, a non-Gaussian copula, such as the
Student's copula, cannot be rejected if it has sufficiently many ``degrees of
freedom''. As a consequence, it may be very dangerous to embrace blindly the
Gaussian copula hypothesis, especially when the correlation coefficient between
the pair of asset is too high as the tail dependence neglected by the Gaussian
copula can be as large as 0.6, i.e., three out five extreme events which occur
in unison are missed.Comment: Latex document of 43 pages including 14 eps figure
Extreme statistics for time series: Distribution of the maximum relative to the initial value
The extreme statistics of time signals is studied when the maximum is
measured from the initial value. In the case of independent, identically
distributed (iid) variables, we classify the limiting distribution of the
maximum according to the properties of the parent distribution from which the
variables are drawn. Then we turn to correlated periodic Gaussian signals with
a 1/f^alpha power spectrum and study the distribution of the maximum relative
height with respect to the initial height (MRH_I). The exact MRH_I distribution
is derived for alpha=0 (iid variables), alpha=2 (random walk), alpha=4 (random
acceleration), and alpha=infinity (single sinusoidal mode). For other,
intermediate values of alpha, the distribution is determined from simulations.
We find that the MRH_I distribution is markedly different from the previously
studied distribution of the maximum height relative to the average height for
all alpha. The two main distinguishing features of the MRH_I distribution are
the much larger weight for small relative heights and the divergence at zero
height for alpha>3. We also demonstrate that the boundary conditions affect the
shape of the distribution by presenting exact results for some non-periodic
boundary conditions. Finally, we show that, for signals arising from
time-translationally invariant distributions, the density of near extreme
states is the same as the MRH_I distribution. This is used in developing a
scaling theory for the threshold singularities of the two distributions.Comment: 29 pages, 4 figure
Measuring degree-degree association in networks
The Pearson correlation coefficient is commonly used for quantifying the
global level of degree-degree association in complex networks. Here, we use a
probabilistic representation of the underlying network structure for assessing
the applicability of different association measures to heavy-tailed degree
distributions. Theoretical arguments together with our numerical study indicate
that Pearson's coefficient often depends on the size of networks with equal
association structure, impeding a systematic comparison of real-world networks.
In contrast, Kendall-Gibbons' is a considerably more robust measure
of the degree-degree association
Aggregation of log-linear risks
In this paper we work in the framework of a k-dimensional vector of log-linear risks. Under weak conditions on the marginal tails and the dependence structure of a vector of positive risks, we derive the asymptotic tail behaviour of the aggregated risk and present an application concerning log-normal risks with stochastic volatility
Density of near-extreme events
We provide a quantitative analysis of the phenomenon of crowding of
near-extreme events by computing exactly the density of states (DOS) near the
maximum of a set of independent and identically distributed random variables.
We show that the mean DOS converges to three different limiting forms depending
on whether the tail of the distribution of the random variables decays slower
than, faster than, or as a pure exponential function. We argue that some of
these results would remain valid even for certain {\em correlated} cases and
verify it for power-law correlated stationary Gaussian sequences. Satisfactory
agreement is found between the near-maximum crowding in the summer temperature
reconstruction data of western Siberia and the theoretical prediction.Comment: 4 pages, 3 figures, revtex4. Minor corrections, references updated.
This is slightly extended version of the Published one (Phys. Rev. Lett.
Performance Limitations of Flat Histogram Methods and Optimality of Wang-Landau Sampling
We determine the optimal scaling of local-update flat-histogram methods with
system size by using a perfect flat-histogram scheme based on the exact density
of states of 2D Ising models.The typical tunneling time needed to sample the
entire bandwidth does not scale with the number of spins N as the minimal N^2
of an unbiased random walk in energy space. While the scaling is power law for
the ferromagnetic and fully frustrated Ising model, for the +/- J
nearest-neighbor spin glass the distribution of tunneling times is governed by
a fat-tailed Frechet extremal value distribution that obeys exponential
scaling. We find that the Wang-Landau algorithm shows the same scaling as the
perfect scheme and is thus optimal.Comment: 5 pages, 6 figure
Markov Processes, Hurst Exponents, and Nonlinear Diffusion Equations with application to finance
We show by explicit closed form calculations that a Hurst exponent H that is
not 1/2 does not necessarily imply long time correlations like those found in
fractional Brownian motion. We construct a large set of scaling solutions of
Fokker-Planck partial differential equations where H is not 1/2. Thus Markov
processes, which by construction have no long time correlations, can have H not
equal to 1/2. If a Markov process scales with Hurst exponent H then it simply
means that the process has nonstationary increments. For the scaling solutions,
we show how to reduce the calculation of the probability density to a single
integration once the diffusion coefficient D(x,t) is specified. As an example,
we generate a class of student-t-like densities from the class of quadratic
diffusion coefficients. Notably, the Tsallis density is one member of that
large class. The Tsallis density is usually thought to result from a nonlinear
diffusion equation, but instead we explicitly show that it follows from a
Markov process generated by a linear Fokker-Planck equation, and therefore from
a corresponding Langevin equation. Having a Tsallis density with H not equal to
1/2 therefore does not imply dynamics with correlated signals, e.g., like those
of fractional Brownian motion. A short review of the requirements for
fractional Brownian motion is given for clarity, and we explain why the usual
simple argument that H unequal to 1/2 implies correlations fails for Markov
processes with scaling solutions. Finally, we discuss the question of scaling
of the full Green function g(x,t;x',t') of the Fokker-Planck pde.Comment: to appear in Physica
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