31,895 research outputs found
The dynamics of the leverage cycle
We present a simple agent-based model of a financial system composed of
leveraged investors such as banks that invest in stocks and manage their risk
using a Value-at-Risk constraint, based on historical observations of asset
prices. The Value-at-Risk constraint implies that when perceived risk is low,
leverage is high and vice versa, a phenomenon that has been dubbed pro-cyclical
leverage. We show that this leads to endogenous irregular oscillations, in
which gradual increases in stock prices and leverage are followed by drastic
market collapses, i.e. a leverage cycle. This phenomenon is studied using
simplified models that give a deeper understanding of the dynamics and the
nature of the feedback loops and instabilities underlying the leverage cycle.
We introduce a flexible leverage regulation policy in which it is possible to
continuously tune from pro-cyclical to countercyclical leverage. When the
policy is sufficiently countercyclical and bank risk is sufficiently low the
endogenous oscillation disappears and prices go to a fixed point. While there
is always a leverage ceiling above which the dynamics are unstable,
countercyclical leverage can be used to raise the ceiling. We also study the
impact on leverage cycles of direct, temporal control of the bank's riskiness
via the bank's required Value-at-Risk quantile. Under such a rule the regulator
relaxes the Value-at-Risk quantile following a negative stock price shock and
tightens it following a positive shock. While such a policy rule can reduce the
amplitude of leverage cycles, its effectiveness is highly dependent on the
choice of parameters. Finally, we investigate fixed limits on leverage and show
how they can control the leverage cycle.Comment: 35 pages, 9 figure
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Purpose. Radiation therapy is a local treatment aimed at cells in and around
a tumor. The goal of this study is to develop an algorithmic solution for
predicting the position of a target in 3D in real time, aiming for the short
fixed calibration time for each patient at the beginning of the procedure.
Accurate predictions of lung tumor motion are expected to improve the precision
of radiation treatment by controlling the position of a couch or a beam in
order to compensate for respiratory motion during radiation treatment.
Methods. For developing the algorithmic solution, data mining techniques are
used. A model form from the family of exponential smoothing is assumed, and the
model parameters are fitted by minimizing the absolute disposition error, and
the fluctuations of the prediction signal (jitter). The predictive performance
is evaluated retrospectively on clinical datasets capturing different behavior
(being quiet, talking, laughing), and validated in real-time on a prototype
system with respiratory motion imitation.
Results. An algorithmic solution for respiratory motion prediction (called
ExSmi) is designed. ExSmi achieves good accuracy of prediction (error
mm/s) with acceptable jitter values (5-7 mm/s), as tested on out-of-sample
data. The datasets, the code for algorithms and the experiments are openly
available for research purposes on a dedicated website.
Conclusions. The developed algorithmic solution performs well to be
prototyped and deployed in applications of radiotherapy
The CMA Evolution Strategy: A Tutorial
This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands
for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized,
method for real-parameter (continuous domain) optimization of non-linear,
non-convex functions. We try to motivate and derive the algorithm from
intuitive concepts and from requirements of non-linear, non-convex search in
continuous domain.Comment: ArXiv e-prints, arXiv:1604.xxxx
A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes
Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016
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