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141.
We study the steady state of the Abelian sandpile models with stochastic toppling rules. The particle addition operators commute with each other, but in general these operators need not be diagonalizable. We use their Abelian algebra to determine their eigenvalues, and the Jordan block structure. These are then used to determine the probability of different configurations in the steady state. We illustrate this procedure by explicitly determining the numerically exact steady state for a one dimensional example, for systems of size ≤12, and also study the density profile in the steady state. 相似文献
142.
《Mendeleev Communications》2020,30(1):114-116
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143.
We study the origin of TB (tuberculosis) epidemic and complex distributions of various populations of TB infection within the stochastic framework. The stochastic nature of this disease infection could be linked to the stochastic behaviour at genome level which is exhibited in SNP (single nucleotide polymorphism) distributions of experimentally identified hotspot driver genes. Our results show the emergence of random clusters, and well-defined discrete domains of the respective species populations in the model driven by demographic stochasticity and intrinsic complex species interaction. The multifractal analysis of the time series of the species populations indicate that TB epidemic could be mainly caused by contact communication and is directional. We propose that any TB epidemic may have high chance of approximately periodic recurrence and can be controlled by optimizing some of the parameters involved in the system modelling. 相似文献
144.
In structural dynamics, similitude laws usually deal with simple configurations as thin flat plates with point forces. Only recently, few papers have analyzed stiffened shells or stochastic pressure loads.This research activity extends the applicability of some similitude laws, developed for thin flat plates under a turbulent boundary layer load, to ribbed plates forced by the same wall pressure fluctuations.The work addresses the problem of designing a scaled experimental test-article and, successively, of re-modulating the measured data in order to get the structural response of an original (unscaled) configuration.Due to the complexity of the structural domain, the design of a scaled configuration leads to a distorted similitude. Then, a simple approach, to circumvent the distortion effects, is proposed. 相似文献
145.
146.
We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application. 相似文献
147.
Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators. 相似文献
148.
In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of these types of stochastic processes and strong empirical motivation, models with a long-term memory within the sequence of time intervals between observations are rare in the physics literature. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by assuming that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model, applied to high-frequency stock market data, can successfully describe the slope of decay of the nonlinear autocorrelation function of stock market returns. We achieve this result without imposing any dependence between consecutive price changes. This proves the crucial role of inter-event times in the volatility clustering phenomenon observed in all stock markets. 相似文献
149.
《Operations Research Letters》2021,49(5):715-719
We study the predictive value of transaction activity in the bitcoin network for the realized volatility of bitcoin returns constructed by high-frequency data. As an alternative modeling approach to the popular linear heterogeneous autoregressive model, we provide out-of-sample forecasts for realized volatility of bitcoin returns employing machine learning algorithms, and in particular by Random Forests. Our findings reveal that on-blockchain transaction activity does improve the out-of-sample forecast accuracy at all the forecast horizons considered. 相似文献
150.
《Operations Research Letters》2021,49(5):809-814
In this paper we discuss Optimal Control and Markov Decision Process (MDP) formulations of multistage optimization problems when the involved probability distributions are not known exactly, but rather are assumed to belong to specified ambiguity families. The aim of this paper is to clarify a connection between such distributionally robust approaches to multistage stochastic optimization. 相似文献