A Tabu Search heuristic procedure in Markov chain bootstrapping

Markov chain theory is proving to be a powerful approach to bootstrap finite states processes, especially where time dependence is non linear. In this work we extend such approach to bootstrap discrete time continuous-valued processes. To this purpose we solve a minimization problem to partition the state space of a continuous-valued process into a finite number of intervals or unions of intervals (i.e. its states) and identify the time lags which provide “memory” to the process. A distance is used as objective function to stimulate the clustering of the states having similar transition probabilities. The problem of the exploding number of alternative partitions in the solution space (which grows with the number of states and the order of the Markov chain) is addressed through a Tabu Search algorithm. The method is applied to bootstrap the series of the German and Spanish electricity prices. The analysis of the results confirms the good consistency properties of the method we propose.     

Bootstrapping Autoregressive and Moving Average Parameter Estimates of Infinite Order Vector Autoregressive Processes

We consider anr-dimensional multivariate time series {y_t, tZ} which is generated by an infinite order vector autoregressive process. We show that a bootstrap procedure which works by generating time series replicates via an estimated finitek-order vector autoregressive process (k→∞ at an appropriate rate with the sample size) gives asymptotically valid approximations to the joint distribution of the growing set of estimated autoregressive coefficients and to the corresponding set of estimated moving average coefficients (impuls responses).     

A robust forecasting system,based on the combination of two simple moving averages

For series with negligible growth and seasonality, simple moving averages are frequently used to estimate the current level of a process, and the resultant value projected as a forecast for future observations. This paper shows that a linear combination of two simple moving averages (SMA) can provide an improved estimate of the underlying level of the process. The proposition is demonstrated by simulation, and good combinations are listed. The theory underlying the improvement is developed. The general rules are then illustrated through an application in an inventory situation.     

Robust to noise and outliers estimator of correlation dimension

The estimation of correlation dimension of continuous and discreet deterministic chaotic processes corrupted by an additive noise and outliers observations is investigated. In this paper we propose a new estimator of correlation dimension based on similarity between the evolution of Gaussian kernel correlation sum (Gkcs) and that of modified Boltzmann sigmoidal function (mBsf), this estimator is given by the maximum value of the first derivative of logarithmic transform of Gkcs against logarithmic transform of bandwidth, so the proposed estimator is independent of the choice of regression region like other regression estimators of correlation dimension. Simulation study indicates the robustness of proposed estimator to the presence of different types of noise such us independent Gaussian noise, non independent Gaussian noise and uniform noise for high noise level, moreover, this estimator is also robust to presence of 60% of outliers observations. Application of this new estimator with determination of their confidence interval using the moving block bootstrap method to adjusted closed price of S&P500 index daily time series revels the stochastic behavior of such financial time series.     

A Turing Test of Bootstrap Scenarios

Scenario generation can be an important part of the training of human decision makers. A good scenario generation method should be able to generate large numbers of realistic scenarios. When the scenarios take the form of univariate stationary time series, the moving blocks bootstrap has the potential to be a good automatic scenario generator. However, one must determine the proper bootstrap block length. We have developed a method of setting the block length based on the distribution of a statistic computed from zero crossing counts. To test whether this way of setting the block length results in realistic scenarios, we performed two Turing tests. These visualization experiments confirmed that, when a bootstrap is optimally tuned, it is difficult for sophisticated subjects to identify a bootstrap sample plotted among several real samples. The main visual defects in moving blocks bootstrap samples are sudden jumps at block boundaries and repeating patterns.     

Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach

The study of the rodent fluctuations of the North was initiated in its modern form with Elton’s pioneering work. Many scientific studies have been designed to collect yearly rodent abundance data, but the resulting time series are generally subject to at least two “problems”: being short and non-linear. We explore the use of the continuous threshold autoregressive (TAR) models for analyzing such data. In the simplest case, the continuous TAR models are additive autoregressive models, being piecewise linear in one lag, and linear in all other lags. The location of the slope change is called the threshold parameter. The continuous TAR models for rodent abundance data can be derived from a general prey-predator model under some simplifying assumptions. The lag in which the threshold is located sheds important insights on the structure of the prey-predator system. We propose to assess the uncertainty on the location of the threshold via a new bootstrap called the nearest block bootstrap (NBB) which combines the methods of moving block bootstrap and the nearest neighbor bootstrap. The NBB assumes an underlying finite-order time-homogeneous Markov process. Essentially, the NBB bootstraps blocks of random block sizes, with each block being drawn from a non-parametric estimate of the future distribution given the realized past bootstrap series. We illustrate the methods by simulations and on a particular rodent abundance time series from Kilpisjärvi, Northern Finland.     

基于GARCH模型的石油价格变动模拟

石油是一种特殊的商品,是国家重要的战略物资,世界各国都十分重视其价格变动问题,因为油价变化会影响到各国经济发展,甚至国家安全。因此,本文采用GARCH模型,通过基于Gibbs抽样的MCMC方法分析了国际市场石油价格的分布特征,对石油价格波动的异方差特性进行描述和模拟,实证分析结果说明从石油价格波动序列峰度系数和平方价格波动序列自相关函数的描述来看,基于t分布的模型模拟效果优于基于正态分布的模型,这一结论反映了石油价格波动序列的分布特性。     

Lévy driven moving averages and semimartingales

The aim of the present paper is to study the semimartingale property of continuous time moving averages driven by Lévy processes. We provide necessary and sufficient conditions on the kernel for the moving average to be a semimartingale in the natural filtration of the Lévy process, and when this is the case we also provide a useful representation. Assuming that the driving Lévy process is of unbounded variation, we show that the moving average is a semimartingale if and only if the kernel is absolutely continuous with a density satisfying an integrability condition.     

Distributed Price Adjustment Based on Convex Analysis

In this paper, we suggest a distributed process of price adjustment toward a partial market equilibrium. As the main contribution, our algorithm of price adjustment is computationally efficient and decentralized. Its convergence properties are crucially based on convex analysis. The proposed price adjustment corresponds to a subgradient scheme for minimizing a special nonsmooth convex function. This function is the total excessive revenue of the market’s participants and its minimizers are equilibrium prices. As the main result, the algorithm of price adjustment is shown to converge to equilibrium prices. Additionally, the market clears on average during the price adjustment process, i.e., by historical averages of supply and demand. Moreover, a global rate of convergence is obtained. We endow our algorithm with decentralized prices by introducing the trade design with price initiative of producers. The latter suggests that producers settle and update their individual prices, and consumers buy at the lowest purchase price.     

Resampling time series using missing values techniques

Several techniques for resampling dependent data have already been proposed. In this paper we use missing values techniques to modify the moving blocks jackknife and bootstrap. More specifically, we consider the blocks of deleted observations in the blockwise jackknife as missing data which are recovered by missing values estimates incorporating the observation dependence structure. Thus, we estimate the variance of a statistic as a weighted sample variance of the statistic evaluated in a “complete” series. Consistency of the variance and the distribution estimators of the sample mean are established. Also, we apply the missing values approach to the blockwise bootstrap by including some missing observations among two consecutive blocks and we demonstrate the consistency of the variance and the distribution estimators of the sample mean. Finally, we present the results of an extensive Monte Carlo study to evaluate the performance of these methods for finite sample sizes, showing that our proposal provides variance estimates for several time series statistics with smaller mean squared error than previous procedures.     

Robustness of the R / S Statistic for Fractional Stable Noises

The R / S statistic is used to detect long-range dependence in a time series and to estimate its intensity. One of its virtues is robustness against different distributions. We show here that the R / S statistic continues to be robust if the time series is a moving average with long-range dependence with innovations that are in the domain of attraction of an infinite variance stable process.     

On option pricing under a completely random measure via a generalized Esscher transform

In this paper, we develop an option valuation model when the price dynamics of the underlying risky asset is governed by the exponential of a pure jump process specified by a shifted kernel-biased completely random measure. The class of kernel-biased completely random measures is a rich class of jump-type processes introduced in [James, L.F., 2005. Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages. Ann. Statist. 33, 1771–1799; James, L.F., 2006. Poisson calculus for spatial neutral to the right processes. Ann. Statist. 34, 416–440] and it provides a great deal of flexibility to incorporate both finite and infinite jump activities. It includes a general class of processes, namely, the generalized Gamma process, which in its turn includes the stable process, the Gamma process and the inverse Gaussian process as particular cases. The kernel-biased representation is a nice representation form and can describe different types of finite and infinite jump activities by choosing different mixing kernel functions. We employ a dynamic version of the Esscher transform, which resembles an exponential change of measures or a disintegration formula based on the Laplace functional used by James, to determine an equivalent martingale measure in the incomplete market. Closed-form option pricing formulae are obtained in some parametric cases, which provide practitioners with a convenient way to evaluate option prices.     

Modelling NASDAQ Series by Sparse Multifractional Brownian Motion

The objective of this paper is to compare the performance of different estimators of Hurst index for multifractional Brownian motion (mBm), namely, Generalized Quadratic Variation (GQV) Estimator, Wavelet Estimator and Linear Regression GQV Estimator. Both estimators are used in the real financial dataset Nasdaq time series from 1971 to the 3rd quarter of 2009. Firstly, we review definitions, properties and statistical studies of fractional Brownian motion (fBm) and mBm. Secondly, a numerical artifact is observed: when we estimate the time varying Hurst index H(t) for an mBm, sampling fluctuation gives the impression that H(t) is itself a stochastic process, even when H(t) is constant. To avoid this artifact, we introduce sparse modelling for mBm and apply it to Nasdaq time series.     

Parameter change test for location‐scale time series models with heteroscedasticity based on bootstrap

This study considers the bootstrap cumulative sum (CUSUM) test for a parameter change in location‐scale time series models with heteroscedasticity. The CUSUM test has been popular for detecting an abrupt change in time series models because it performs well in many applications. However, it has severe size distortions in many situations. As a remedy, we consider the bootstrap CUSUM test, particularly focusing on the CUSUM test based on score vectors, and demonstrate the weak consistency of the bootstrap test for its justification. A simulation study and data analysis are conducted for illustration.     

Gaussian moving averages,semimartingales and option pricing

We provide a characterization of the Gaussian processes with stationary increments that can be represented as a moving average with respect to a two-sided Brownian motion. For such a process we give a necessary and sufficient condition to be a semimartingale with respect to the filtration generated by the two-sided Brownian motion. Furthermore, we show that this condition implies that the process is either of finite variation or a multiple of a Brownian motion with respect to an equivalent probability measure. As an application we discuss the problem of option pricing in financial models driven by Gaussian moving averages with stationary increments. In particular, we derive option prices in a regularized fractional version of the Black–Scholes model.     

How does innovation''''s tail risk determine marginal tail risk of a stationary financial time series?

We discuss the relationship between the marginal tail risk probability and theinnovation's tail risk probability for some stationary financial time series models. We firstgive the main results on the tail behavior of a class of infinite weighted sums of randomvariables with heavy-tailed probabilities. And then, the main results are applied to threeimportant types of time series models; infinite order moving averages, the simple bilineartime series and the solutions of stochastic difference equations. The explicit formulasare given to describe how the marginal tail probabilities come from the innovation's tailprobabilities for these time series. Our results can be applied to the tail estimation of timeseries and are useful for risk analysis in finance.     

Entry and exit decisions with linear costs under uncertainty

From the viewpoint of stochastic programming, we rigorously analyse entry and exit decisions of a project which were proposed by Dixit [A. Dixit, Entry and exit decisions under uncertainty, J. Polit. Econ. 97 (1989), pp. 620–638]. In this article, instead of assuming that the costs are constant in classical research, we assume that they are linear with respect to the price of the commodity produced by the project. Under this assumption, we obtain a condition which guarantees that investing in the project is worthless; besides, the project may be terminated when the commodity price is greater than a certain value. In contrast, there are no such results provided that the costs are constant. Moreover, we provide an explicit solution of entry and exit decisions if the project is worthy to be invested in.     

Non-stationarity in Sugar Prices

Time series analysis of unstable series such as raw material prices contains several methodological pitfalls. These are illustrated by a survey of the Box-Jenkins method of analysis applied to 12 years of daily sugar prices.Two problems in particular have received insufficient attention in previous analyses. First, the model variance fluctuates substantially over time. The logarithmic transformation of prices is recommended as significantly reducing the fluctuations. Second, it is essential to study the constant term in the Box-Jenkins model, which is shown to differ significantly from zero. This proves the existence of price trends. The commonly accepted random walk model for commodity prices is thus incorrect, at least for sugar.However, these price trends vary over time and this possibility is not covered by the Box-Jenkins models. Consequently, a new model for price movements is proposed, characterized by frequent changes in the daily price trend yet consistent with the results of the autocorrelation analysis.     

Parameter estimation of an agent‐based stock price model

The influence of the behavior and strategies of traders on stock price formation has attracted much interest. It is assumed that there is a positive correlation between the total net demand and the price change. A buy order is expected to increase the price, whereas a sell order is assumed to decrease it. We perform data analysis based on a recently proposed stochastic model for stock prices. The model involves long‐range dependence, self‐similarity, and no arbitrage principle, as observed in real data. The arrival times of orders, their quantity, and their duration are created by a Poisson random measure. The aggregation of the effect of all orders based on these parameters yields the log‐price process. By scaling the parameters, a fractional Brownian motion or a stable Levy process can be obtained in the limit. In this paper, our aim is twofold; first, to devise statistical methodology to estimate the model parameters with an application on high‐frequency price data, and second, to validate the model by simulations with the estimated parameters. We find that the statistical properties of agent level behavior are reflected on the stock price, and can affect the entire process. Moreover, the price model is suitable for prediction through simulations when the parameters are estimated from real data. The methods developed in the present paper can be applied to frequently traded stocks in general. Copyright © 2013 John Wiley & Sons, Ltd.