Stochastic kinetic models: Dynamic independence, modularity and graphs

The dynamic properties and independence structure of stochastic kinetic models (SKMs) are analyzed. An SKM is a highly multivariate jump process used to model chemical reaction networks, particularly those in biochemical and cellular systems. We identify SKM subprocesses with the corresponding counting processes and propose a directed, cyclic graph (the kinetic independence graph or KIG) that encodes the local independence structure of their conditional intensities. Given a partition $[A,D,B]$ of the vertices, the graphical separation $A\perp B|D$ in the undirected KIG has an intuitive chemical interpretation and implies that $A$ is locally independent of $B$ given $A\cup D$. It is proved that this separation also results in global independence of the internal histories of $A$ and $B$ conditional on a history of the jumps in $D$ which, under conditions we derive, corresponds to the internal history of $D$. The results enable mathematical definition of a modularization of an SKM using its implied dynamics. Graphical decomposition methods are developed for the identification and efficient computation of nested modularizations. Application to an SKM of the red blood cell advances understanding of this biochemical system.Comment: Published in at http://dx.doi.org/10.1214/09-AOS779 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models

A continuous time econometric modelling framework for multivariate financial market event (or 'transactions') data is developed in which the model is specified via the vector conditional intensity. This has the advantage that the conditioning information set is updated continuously in time as new information arrives. Generalised Hawkes (g-Hawkes) models are introduced that are sufficiently flexible to incorporate `inhibitory' events and dependence between trading days. Novel omnibus specification tests for parametric models based on a multivariate random time change theorem are proposed. A computationally efficient thinning algorithm for simulation of g-Hawkes processes is also developed. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically. Furthermore, the model delivers a continuous record of instantaneous volatility that is conditional on the timing of trades and quote changes.Point process, conditional intensity, Hawkes process, specification test, random time change, transactions data, market microstructure.

Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models

A continuous time econometric modelling framework for multivariate financial market event (or `transactions') data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new information arrives. The class of generalised Hawkes models is introduced which allows the estimation of the dependence of the intensity on the events of previous trading days. Analytic likelihoods are available and it is shown how to construct diagnostic tests based on the transformation of non-Poisson processes into standard Poisson processes using random changes of time. A proof of the validity of the diagnostic testing procedures is given that imposes only a very weak condition on the point process model, thus establishing their widespread applicability. A continuous time, bivariate point process model of the timing of trades and mid-quote changes is presented for a New York Stock Exchange stock and the empirical findings are related to the theoretical and empirical market microstructure literature. The two-way interaction of trades and quote changes is found to be important empirically.Point and counting processes, multivariate, intensity, Hawkes process, diagnostics, goodness of fit, specification tests, change of time, transactions data, NYSE, market microstructure.

Stationarity and the term structure of interest rates: a characterisation of stationary and unit root yield curves

The nature of yield curve dynamics and the determinants of the integration order of yields are investigated using a benchmark economy in which the logarithmic expectations theory holds and the regularity condition of a limiting yield and limiting term premium is satisfied. By considering a zero-coupon yield curve with a complete term structure of maturities, a linear vector autoregressive process is constructed that provides an arbitrarily accurate moving average representation of the complete yield curve as its cross-sectional dimension (n) goes to infinity. We use this to prove the following novel results. First, any I(2) component vanishes owing to the almost sure (a.s.) convergence of the innovations to yields, vt(n), as n. Second, the yield curve is stationary if and only if nvt(n) converges a.s., or equivalently the innovations to log discount bond prices converge a.s.; otherwise yields are I(1). A necessary condition for either stationarity or the absence of arbitrage is that the limiting yield is constant over time. Since the time-varying component of term premia is small in various fixed-income markets, these results provide insight into the critical determinants of the stationarity properties of the term structure.Econometric models ; Interest rates

The dynamics of economics functions: modelling and forecasting the yield curve

The class of Functional Signal plus Noise (FSN) models is introduced that provides a new, general method for modelling and forecasting time series of economic functions. The underlying, continuous economic function (or "signal") is a natural cubic spline whose dynamic evolution is driven by a cointegrated vector autoregression for the ordinates (or "y-values") at the knots of the spline. The natural cubic spline provides flexible cross-sectional fit and results in a linear, state space model. This FSN model achieves dimension reduction, provides a coherent description of the observed yield curve and its dynamics as the cross-sectional dimension N becomes large, and can feasibly be estimated and used for forecasting when N is large. The integration and cointegration properties of the model are derived. The FSN models are then applied to forecasting 36-dimensional yield curves for US Treasury bonds at the one month ahead horizon. The method consistently outperforms the Diebold and Li (2006) and random walk forecasts on the basis of both mean square forecast error criteria and economically relevant loss functions derived from the realised profits of pairs trading algorithms. The analysis also highlights in a concrete setting the dangers of attempts to infer the relative economic value of model forecasts on the basis of their associated mean square forecast errors.Time-series analysis ; Forecasting ; Mathematical models ; Macroeconomics - Econometric models

Modelling the Dynamics of Cross-Sectional Price Functions: an Econometric Analysis of the Bid and Ask Curves of an Automated Exchange

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal function is used to approximate the price curve data. Estimation may then be performed using quasi-maximum likelihood methods based on the Kalman filter. The model is used to provide one of the first studies of the dynamics of the bid and ask curves of an electronic limit order book and enables the comprehensive measurement of the dynamic determinants of traders execution costs. It is found that the differences between the bid and ask curves and their intercepts (i.e. the immediate price impacts of market orders) are well described by covariance stationary processes. The in-sample, 1-step ahead point predictions for these curves perform well and motivate the development of parametric FSN models that take into account the monotonicity of the price curves and can be used to form predictive distributions.functional time series, bid and ask curves, liquidity, electronic limit order book, cubic spline, state space form, Kalman filter, quasi-maximum likelihood.

The fidelity of dynamic signaling by noisy biomolecular networks

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.MV acknowledges support under an MRC Biomedical Informatics Fellowship. PT acknowledges support by the Royal Commission for the Exhibition of 1851. RG acknowledges support from the Leverhulme Trust (RPG-2013-171). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript