64,378 research outputs found
Counterparty Credit Limits: An Effective Tool for Mitigating Counterparty Risk?
A counterparty credit limit (CCL) is a limit imposed by a financial
institution to cap its maximum possible exposure to a specified counterparty.
Although CCLs are designed to help institutions mitigate counterparty risk by
selective diversification of their exposures, their implementation restricts
the liquidity that institutions can access in an otherwise centralized pool. We
address the question of how this mechanism impacts trade prices and volatility,
both empirically and via a new model of trading with CCLs. We find empirically
that CCLs cause little impact on trade. However, our model highlights that in
extreme situations, CCLs could serve to destabilize prices and thereby
influence systemic risk
European exchange trading funds trading with locally weighted support vector regression
In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series
Forecasting foreign exchange rates with adaptive neural networks using radial basis functions and particle swarm optimization
The motivation for this paper is to introduce a hybrid Neural Network architecture of Particle
Swarm Optimization and Adaptive Radial Basis Function (ARBF-PSO), a time varying leverage
trading strategy based on Glosten, Jagannathan and Runkle (GJR) volatility forecasts and a
Neural Network fitness function for financial forecasting purposes. This is done by
benchmarking the ARBF-PSO results with those of three different Neural Networks
architectures, a Nearest Neighbors algorithm (k-NN), an autoregressive moving average model
(ARMA), a moving average convergence/divergence model (MACD) plus a naïve strategy.
More specifically, the trading and statistical performance of all models is investigated in a
forecast simulation of the EUR/USD, EUR/GBP and EUR/JPY ECB exchange rate fixing time
series over the period January 1999 to March 2011 using the last two years for out-of-sample
testing
Predicting trend reversals using market instantaneous state
Collective behaviours taking place in financial markets reveal strongly
correlated states especially during a crisis period. A natural hypothesis is
that trend reversals are also driven by mutual influences between the different
stock exchanges. Using a maximum entropy approach, we find coordinated
behaviour during trend reversals dominated by the pairwise component. In
particular, these events are predicted with high significant accuracy by the
ensemble's instantaneous state.Comment: 18 pages, 15 figure
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