Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiscale local change point detection with applications to
  value-at-risk   This paper offers a new approach to modeling and forecasting of nonstationary
time series with applications to volatility modeling for financial data. The
approach is based on the assumption of local homogeneity: for every time point,
there exists a historical interval of homogeneity, in which the volatility
parameter can be well approximated by a constant. The proposed procedure
recovers this interval from the data using the local change point (LCP)
analysis. Afterward, the estimate of the volatility can be simply obtained by
local averaging. The approach carefully addresses the question of choosing the
tuning parameters of the procedure using the so-called ``propagation''
condition. The main result claims a new ``oracle'' inequality in terms of the
modeling bias which measures the quality of the local constant approximation.
This result yields the optimal rate of estimation for smooth and piecewise
constant volatility functions. Then, the new procedure is applied to some data
sets and a comparison with a standard GARCH model is also provided. Finally, we
discuss applications of the new method to the Value at Risk problem. The
numerical results demonstrate a very reasonable performance of the new method.

18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg