Overnight Borrowing, Interest Rates and Extreme Value Theory

Cataloged from PDF version of article.We examine the dynamics of extreme values of overnight borrowing rates in an inter-bank money market before a financial crisis during which overnight borrowing rates rocketed up to (simple annual) 4000 percent. It is shown that the generalized Pareto distribution fits well to the extreme values of the interest rate distribution. We also provide predictions of extreme overnight borrowing rates using pre-crisis data. The examination of tails (extreme values) provides answers to such issues as to what are the extreme movements to be expected in financial markets; is there a possibility for even larger movements and, are there theoretical processes that can model the type of fat-tails in the observed data? The answers to such questions are essential for proper management of financial exposures and laying ground for regulations. (c) 2005 Elsevier B.V. All rights reserved

Extreme value theory and Value-at-Risk: Relative performance im emerging markets

Cataloged from PDF version of article.In this paper, we investigate the relative performance of Value-at-Risk (VaR) models with the daily stock market returns of nine different emerging markets. In addition to well-known modeling approaches, such as variance-covariance method and historical simulation, we study the extreme value theory (EVT) to generate VaR estimates and provide the tail forecasts of daily returns at the 0.999 percentile along with 95% confidence intervals for stress testing purposes. The results indicate that EVT-based VaR estimates are more accurate at higher quantiles. According to estimated Generalized Pareto Distribution parameters, certain moments of the return distributions do not exist in some countries. In addition, the daily return distributions have different moment properties at their right and left tails. Therefore, risk and reward are not equally likely in these economies. (C) 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Multiscale Systematic Risk

Cataloged from PDF version of article.In this paper we propose a new approach to estimating systematic risk (the beta of an asset). The proposed method is based on a wavelet multiscaling approach that decomposes a given time series on a scale-by-scale basis. The empirical results from different economies show that the relationship between the return of a portfolio and its beta becomes stronger as the wavelet scale increases. Therefore, the predictions of the CAPM model should be investigated considering the multiscale nature of risk and return. (C) 2004 Elsevier Ltd. All rights reserved

A multiscale view on inverse statistics and gain/loss asymmetry in financial time series

Researchers have studied the first passage time of financial time series and observed that the smallest time interval needed for a stock index to move a given distance is typically shorter for negative than for positive price movements. The same is not observed for the index constituents, the individual stocks. We use the discrete wavelet transform to illustrate that this is a long rather than short time scale phenomenon -- if enough low frequency content of the price process is removed, the asymmetry disappears. We also propose a new model, which explain the asymmetry by prolonged, correlated down movements of individual stocks

Analysing Lyapunov spectra of chaotic dynamical systems

It is shown that the asymptotic spectra of finite-time Lyapunov exponents of a variety of fully chaotic dynamical systems can be understood in terms of a statistical analysis. Using random matrix theory we derive numerical and in particular analytical results which provide insights into the overall behaviour of the Lyapunov exponents particularly for strange attractors. The corresponding distributions for the unstable periodic orbits are investigated for comparison.Comment: 4 pages, 4 figure

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Hybrid of swarm intelligent algorithms in medical applications

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both th

Components of the Profitability of Technical Currency Trading

This paper investigates the sources of the profitability of 1024 moving average and momentum models when trading in the German mark (euro)/U.S. dollar market based on daily data. The main results are as follows. First, each of these models would have been profitable over the entire sample period. Second, this profitability is exclusively due to the exploitation of persistent exchange rate trends. Third, these results do not change substantially when trading is examined within subperiods. Fourth, the 25 best performing models in each in-sample period examined were profitable also out of sample in most cases. Fifth, the profitability of technical trading the German mark (euro)/U. S. dollar exchange rate has been significantly lower since the late 1980s as compared to the first 15 years of the floating rate period

Chlamydiatrachomatis and placental inflammation in early preterm delivery

Chlamydiatrachomatis may infect the placenta and subsequently lead to preterm delivery. Our aim was to evaluate the relationship between the presence of Chlamydiatrachomatis and signs of placental inflammation in women who delivered at 32 weeks gestation or less. Setting: placental histology and clinical data were prospectively obtained from 304 women and newborns at the Erasmus MC-Sophia, Rotterdam, the Netherlands. C.trachomatis testing of placentas was done retrospectively using PCR. C.trachomatis was detected in 76 (25%) placentas. Histological evidence of placental inflammation was present in 123 (40%) placentas: in 41/76 (54%) placentas with C.trachomatis versus 82/228 (36%) placentas without C.trachomatis infection (OR 2.1, 95% CI 1.2–3.5). C.trachomatis infection correlated with the progression (P = 0.009) and intensity (P = 0.007) of materno-fetal placental inflammation. C.trachomatis DNA was frequently detected in the placenta of women with early preterm delivery, and was associated with histopathological signs of placental inflammation

Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias

In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the "superior predictive ability" (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf (2005, Econometrica). We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for these markets, yet such evidence weakens after the ETFs are introduced. © 2009.preprin