The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

Detecting self-organisational change in economic processes exhibiting logistic growth

This paper offers an econometric methodology for the detection of self-organisational change (defined in terms of the presence of time irreversibility, structural change and fundamental uncertainty) in economic processes that follow logistic diffusion growth paths in historical time. The approach we adopted is built upon recent developments in `moving window' spectral methods which are applied to the scaled residuals generated by estimated logistic diffusion models. We illustrate the use of such methods by examining the case of a financial instrument, namely, the Australian Building Society Deposit, which experienced logistic growth in its market share until bank deregulation was enacted in the 1980s. We show that there is clear evidence that self-organisational change is present over the historical period considered.     

Bargaining with asymmetric information in non-stationary markets

Summary. The Rubinstein and Wolinsky bargaining-in-markets framework is modified by the introduction of asymmetric information and non-stationarity. Non-stationarity is introduced in the form of an arbitrary stochastic Markov process which captures the dynamics of market entry and pairwise matching. A new technique is used for establishing existence and characterizing the unique outcome of a non-stationary market equilibrium. The impact of market supply and demand on bilateral bargaining outcomes and matching probabilities is explored. The results are useful for examining such questions as why coordination failures and macroeconomic output fluctuations are correlated with real and monetary shocks. Received: July 22, 1994; revised version: January 21, 1998     

非平稳条件下的市场可预测性问题研究

本文研究了非平稳对线性、非线性可预测性检验的影响,从非平稳角度解释了"非线性可预测性难题"。在此基础上对传统的Box-Pierce-Ljung线性检验法和广义谱密度非线性检验法进行了修正,并运用严格的理论证明和蒙特卡罗模拟证实了它们在大样本和有限样本情况下的非平稳稳健性。最后,运用修正的检验方法对我国第一只股指期货标的沪深300统一指数的可预测性进行了分析。本文结论对于我国金融市场实证研究具有重要的指导意义,也反映了我国新兴金融市场的独有特性以及成熟统计方法在我国的实用性。     

An empirical likelihood method for spatial regression

Properties of a “blockwise”empirical likelihood for spatial regression with non-stochastic regressors are investigated for spatial data on a lattice. The method enables nonparametric confidence regions for spatial trend parameters to be calibrated, even though non-random regressors introduce non-stationary forms of spatial dependence into the “blockwise” construction. Additionally, the regression results are valid in a general framework allowing for a variety of behavior in regressor variables as well as the underlying spatial error process. The same regression method also applies when the regressors are stochastic.     

Population size and the size of government

We examine the effect of population size on government size for a panel of 130 countries for the period between 1970 and 2014. We show that previous analyses of the nexus between population size and government size are incorrectly specified and fail to consider the influence of cross-sectional dependence, non-stationarity and cointegration. Using a panel time-series approach that adequately accounts for these issues, we find that population size has a positive long-run effect on government size. This finding suggests that effects of population size that increase government size (primarily due to the costs of heterogeneity, congestion, crime and conflict) dominate effects that reduce government size (primarily due to scale economies).     

DIRECT MULTI-STEP ESTIMATION AND FORECASTING

Abstract.  This paper surveys the literature on multi-step forecasting when the model or the estimation method focuses directly on the link between the forecast origin and the horizon of interest. Among diverse contributions, we show how the current consensual concepts have emerged. We present an exhaustive overview of the existing results, including a conclusive review of the circumstances favourable to direct multi-step forecasting, namely different forms of non-stationarity and appropriate model design. We also provide a unifying framework which allows us to analyse the sources of forecast errors and hence of accuracy improvements from direct over iterated multi-step forecasting.     

The international transmission channels of US supply and demand shocks: Evidence from a non-stationary dynamic factor model for the G7 countries

We employ a multi-country non-stationary dynamic factor model to assess spillover effects and transmission channels of US supply and demand shocks on a variety of macroeconomic variables in individual non-US G7 countries. We find that trade, financial and confidence channels all play a significant role in the international transmission of US shocks. However, the results point to substantial heterogeneities of shock transmission across the individual G7 economies. In particular, we find negative transmission effects for Italy and Japan as the only two G7 countries not well integrated into global value chains. Moreover, the exchange rate responses of Germany, France and Italy turn out to be far less pronounced in comparison to the other G7 economies which we relate to their membership of the euro area and their coordinated monetary policies prior to the establishment of the euro. Whereas we document a close comovement of stock market dynamics across the G7 countries, we find credit and real estate markets to be less synchronized. We do not find the effects and transmission channels to be fundamentally affected by the post-2008 economic environment.     

Properties of nonlinear transformations of fractionally integrated processes

This paper shows that the properties of nonlinear transformations of a fractionally integrated process strongly depend on whether the initial series is stationary or not. Transforming a stationary Gaussian I(d) process with d>0 leads to a long-memory process with the same or a smaller long-memory parameter depending on the Hermite rank of the transformation. Any nonlinear transformation of an antipersistent Gaussian I(d) process is I(0)). For non-stationary I(d) processes, every polynomial transformation is non-stationary and exhibits a stochastic trend in mean and in variance. In particular, the square of a non-stationary Gaussian I(d) process still has long memory with parameter d, whereas the square of a stationary Gaussian I(d) process shows less dependence than the initial process. Simulation results for other transformations are also discussed.