Statistical Models for High Frequency Security Prices

This article studies two extensions of the compound Poisson process with iid Gaussian innovations which are able to characterize important features of high frequency security prices. The first model explicitly accounts for the presence of the bid/ask spread encountered in price-driven markets. This model can be viewed as a mixture of the compound Poisson process model by Press and the bid/ask bounce model by Roll. The second model generalizes the compound Poisson process to allow for an arbitrary dependence structure in its innovations so as to account for more complicated types of market microstructure. Based on the characteristic function, we analyze the static and dynamic properties of the price process in detail. Comparison with actual high frequency data suggests that the proposed models are sufficiently flexible to capture a number of salient features of financial return data including a skewed and fat tailed marginal distribution, serial correlation at high frequency, time variation in market activity both at high and low frequency. The current framework also allows for a detailed investigation of the ``market-microstructure-induced bias'' in the realized variance measure and we find that, for realistic parameter values, this bias can be substantial. We analyze the impact of the sampling frequency on the bias and find that for non-constant trade intensity, ``business'' time sampling maximizes the bias but achieves the lowest overall MSECompound Poisson Process; High Frequency Data; Market Microstructure; Characteristic Function; OU Process; Realized Variance Bias; Optimal Sampling

Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news.Web of Science421049

Statistical models for over-dispersion in the frequency of peaks over threshold data for a flow series.

In a peaks over threshold analysis of a series of river flows, a sufficiently high threshold is used to extract the peaks of independent flood events. This paper reviews existing, and proposes new, statistical models for both the annual counts of such events and the process of event peak times. The most common existing model for the process of event times is a homogeneous Poisson process. This model is motivated by asymptotic theory. However, empirical evidence suggests that it is not the most appropriate model, since it implies that the mean and variance of the annual counts are the same, whereas the counts appear to be overdispersed, i.e., have a larger variance than mean. This paper describes how the homogeneous Poisson process can be extended to incorporate time variation in the rate at which events occur and so help to account for overdispersion in annual counts through the use of regression and mixed models. The implications of these new models on the implied probability distribution of the annual maxima are also discussed. The models are illustrated using a historical flow series from the River Thames at Kingston

The Spin Distribution of Fast Spinning Neutron Stars in Low Mass X-Ray Binaries: Evidence for Two Sub-Populations

We study the current sample of rapidly rotating neutron stars in both accreting and non-accreting binaries in order to determine whether the spin distribution of accreting neutron stars in low-mass X-ray binaries can be reconciled with current accretion torque models. We perform a statistical analysis of the spin distributions and show that there is evidence for two sub-populations among low-mass X-ray binaries, one at relatively low spin frequency, with an average of ~300 Hz and a broad spread, and a peaked population at higher frequency with average spin frequency of ~575 Hz. We show that the two sub-populations are separated by a cut-point at a frequency of ~540 Hz. We also show that the spin frequency of radio millisecond pulsars does not follow a log-normal distribution and shows no evidence for the existence of distinct sub-populations. We discuss the uncertainties of different accretion models and speculate that either the accreting neutron star cut-point marks the onset of gravitational waves as an efficient mechanism to remove angular momentum or some of the neutron stars in the fast sub-population do not evolve into radio millisecond pulsars.Comment: Submitted to Ap

Does Indexation Bias the Estimated Frequency of Price Adjustment?

We assess the implications of price indexation for estimated frequency of price adjustment in sticky price models of business cycles. These models predominantly assume that non-reoptimized prices are indexed to lagged or average inflation. The assumption of price indexation adds tractability although it is not likely reflective of the price practices of firms at the micro level. Under indexation firms have less incentive to adjust their prices, which implies downward bias in the estimated frequency of price changes. To evaluate the bias, we generate data with Calvo-type models without indexation. The artificial data are then used to estimate the frequency of price changes with indexation. Considering different assumptions about the degree of price rigidity and the level of trend inflation in the data-generating model, we find that the estimated indexation bias can be substantial, ranging up to 12 quarters in some cases.Inflation and prices; Economic models; Econometric and statistical methods

One in a Billion: MSSM-like D-Brane Statistics

Continuing our recent work hep-th/0411173, we study the statistics of four-dimensional, supersymmetric intersecting D-brane models in a toroidal orientifold background. We have performed a vast computer survey of solutions to the stringy consistency conditions and present their statistical implications with special emphasis on the frequency of Standard Model features. Among the topics we discuss are the implications of the K-theory constraints, statistical correlations among physical quantities and an investigation of the various statistical suppression factors arising once certain Standard Model features are required. We estimate the frequency of an MSSM like gauge group with three generations to be one in a billion.Comment: 36 pages, 12 figures; v2: typos corrected, one ref. added; v3: minor changes, version to appear in JHE