9,125 research outputs found
Pricing Bermudan options under local L\'evy models with default
We consider a defaultable asset whose risk-neutral pricing dynamics are
described by an exponential L\'evy-type martingale. This class of models allows
for a local volatility, local default intensity and a locally dependent L\'evy
measure. We present a pricing method for Bermudan options based on an
analytical approximation of the characteristic function combined with the COS
method. Due to a special form of the obtained characteristic function the price
can be computed using a Fast Fourier Transform-based algorithm resulting in a
fast and accurate calculation. The Greeks can be computed at almost no
additional computational cost. Error bounds for the approximation of the
characteristic function as well as for the total option price are given
Adjacent Channel Interference in UMTS Networks
One of the purposes of receive filtering in a Universal Mobile Telecommunication System (UMTS) handset receiver is to attenuate out-of-channel interference to provide channel selectivity. A UMTS handset receiver using a receive filter adaptive on out-of-channel interference level can be more computationally efficient than a handset with a fixed receive filter provided that the hand-set operates in low out-of-channel interference conditions often enough. The UMTS Adjacent Channel Selectivity (ACS) test case requires the adaptive receive filter to provide a worst case ACS of 33 dB. An adaptive receive filter is more computationally efficient than a fixed receive filter when the required ACS is less than 23 dB, because the added complexity of measuring the out-of-channel interference is compensated for by the reduction in the required number of filter taps to achieve the ACS. Measurements of the out-of-channel interference show that currently the interference levels for which the maximum ACS of 33 dB is required are hardly ever reached in practice. For the currently measured interference levels an adaptive receive filter will be computationally more efficient than a fixed\ud
receive filter 97% of the time. However, the current out-of-channel interference measurements might be on the optimistic side, because the loads of the UMTS networks are low. When these loads increase in the future, the out-of-channel interference levels may increase and the advantage in computational efficiency of the adaptive receive filter will be reduced
Pricing options and computing implied volatilities using neural networks
This paper proposes a data-driven approach, by means of an Artificial Neural
Network (ANN), to value financial options and to calculate implied volatilities
with the aim of accelerating the corresponding numerical methods. With ANNs
being universal function approximators, this method trains an optimized ANN on
a data set generated by a sophisticated financial model, and runs the trained
ANN as an agent of the original solver in a fast and efficient way. We test
this approach on three different types of solvers, including the analytic
solution for the Black-Scholes equation, the COS method for the Heston
stochastic volatility model and Brent's iterative root-finding method for the
calculation of implied volatilities. The numerical results show that the ANN
solver can reduce the computing time significantly
Severing the Link between Farm Program Payments and Farm Production: Motivation, International Efforts, and Lessons
Agricultural and Food Policy, International Relations/Trade,
Circulating cell death products predict clinical outcome of colorectal cancer patients.
BackgroundTumor cell death generates products that can be measured in the circulation of cancer patients. CK18-Asp396 (M30 antigen) is a caspase-degraded product of cytokeratin 18 (CK18), produced by apoptotic epithelial cells, and is elevated in breast and lung cancer patients.MethodsWe determined the CK18-Asp396 and total CK18 levels in plasma of 49 colorectal cancer patients, before and after surgical resection of the tumor, by ELISA. Correlations with patient and tumor characteristics were determined by Kruskal-Wallis H and Mann-Whitney U tests. Disease-free survival was determined using Kaplan-Meier methodology with Log Rank tests, and univariate and multivariate Cox proportional hazard analysis.ResultsPlasma CK18-Asp396 and total CK18 levels in colorectal cancer patients were related to disease stage and tumor diameter, and were predictive of disease-free survival, independent of disease-stage, with hazard ratios (HR) of patients with high levels (> median) compared to those with low levels (< or = median) of 3.58 (95% CI: 1.17-11.02) and 3.58 (95% CI: 0.97-7.71), respectively. The CK18-Asp396/CK18 ratio, which decreased with tumor progression, was also predictive of disease-free survival, with a low ratio (< or = median) associated with worse disease-free survival: HR 2.78 (95% CI: 1.06-7.19). Remarkably, the plasma CK18-Asp396 and total CK18 levels after surgical removal of the tumor were also predictive of disease-free survival, with patients with high levels having a HR of 3.78 (95% CI: 0.77-18.50) and 4.12 (95% CI: 0.84-20.34), respectively, indicating that these parameters can be used also to monitor patients after surgery.ConclusionCK18-Asp396 and total CK18 levels in the circulation of colorectal cancer patients are predictive of tumor progression and prognosis and might be helpful for treatment selection and monitoring of these patients
A neural network-based framework for financial model calibration
A data-driven approach called CaNN (Calibration Neural Network) is proposed
to calibrate financial asset price models using an Artificial Neural Network
(ANN). Determining optimal values of the model parameters is formulated as
training hidden neurons within a machine learning framework, based on available
financial option prices. The framework consists of two parts: a forward pass in
which we train the weights of the ANN off-line, valuing options under many
different asset model parameter settings; and a backward pass, in which we
evaluate the trained ANN-solver on-line, aiming to find the weights of the
neurons in the input layer. The rapid on-line learning of implied volatility by
ANNs, in combination with the use of an adapted parallel global optimization
method, tackles the computation bottleneck and provides a fast and reliable
technique for calibrating model parameters while avoiding, as much as possible,
getting stuck in local minima. Numerical experiments confirm that this
machine-learning framework can be employed to calibrate parameters of
high-dimensional stochastic volatility models efficiently and accurately.Comment: 34 pages, 9 figures, 11 table
On local Fourier analysis of multigrid methods for PDEs with jumping and random coefficients
In this paper, we propose a novel non-standard Local Fourier Analysis (LFA)
variant for accurately predicting the multigrid convergence of problems with
random and jumping coefficients. This LFA method is based on a specific basis
of the Fourier space rather than the commonly used Fourier modes. To show the
utility of this analysis, we consider, as an example, a simple cell-centered
multigrid method for solving a steady-state single phase flow problem in a
random porous medium. We successfully demonstrate the prediction capability of
the proposed LFA using a number of challenging benchmark problems. The
information provided by this analysis helps us to estimate a-priori the time
needed for solving certain uncertainty quantification problems by means of a
multigrid multilevel Monte Carlo method
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