Stochastic Properties Research Articles

Monetary policy and inflation rate in the behavior of consumer sentiment in the us. A fractional integration and cointegration analysis

Consumer sentiment is a relevant tool for experts when it comes to determining the economic situation in a country, making it possible to analyze the spending trend in advance; for this reason, this paper analyzes links between monetary policy and the inflation rate in the behavior of consumer sentiment in the United States. We use methodologies based on fractional integration and fractional cointegration to obtain the stochastic properties of the monthly time series, from December 2019 to August 2022. The results using fractional integration methodologies exhibit a high degree of persistence and the consumer price index (CPI) and consumer sentiment index present a non-mean reversion I(1) behavior. Focusing on the cointegrating part, we conclude from the results that: 1) an increase in the variable “Total Monetary Base” produces an increase in the CPI, 2) the “variation in the total monetary base” in the United States does not affect consumer sentiment, and 3) a positive variation in the consumer sentiment indicator affects the increase in the inflation rate in the United States.

Common and idiosyncratic conditional volatility: Theory and empirical evidence from electricity prices

. We propose and develop a robust score-driven multiplicative dynamic factor model for the conditional volatility of electricity prices. This model allows us to identify both common and idiosyncratic conditional volatility factors in the spot price of electricity across key Nord Pool European markets. Both factors are extracted using a single observation-driven filter that is simple and parsimonious. The common factor is robust by construction as it averages information over the cross-section of the high-dimensional vector of electricity price changes. The idiosyncratic factors are designed to capture the erratic shocks in prices and therefore rely on a robust updating equation which allows for outliers and fat-tailed innovations. Our model is suitable for high-dimensional applications and does not necessarily suffer from the curse of dimensionality. The parsimonious nature of the observation-driven filter leads to simple computations for the estimation of parameters and the signal extraction of factors. We derive the stochastic properties of the filter and its underlying factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish the consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the filter are explored in a Monte Carlo study. Finally, we apply our model and filter to the Nord Pool spot price data and find evidence that net-import and net-export electricity markets exhibit substantially different common and idiosyncratic volatilities. In particular, we confirm that net-import markets are considerably more exposed to cross-market volatility spillovers, and we quantify the relevant degree of exposition.

Growth and pinch-off of sidearm necks: a model of dendrite fragmentation

Dendrite fragmentation commonly occurs by pinch-off of sidearms at the narrow neck with the parent stem. Comprehensive measurements of the local neck dynamics are performed using in-situ synchrotron radiography experiments in which a flat sample of a Ga–35wt%. In alloy is solidified under both steady and transient growth conditions with minimal effects of melt convection. The measurements reveal two characteristic limits where the neck radius follows universal self-similar t1/3 power laws, with a positive rate constant in the limit of steady growth and a negative rate constant in the isothermal pinch-off limit. The temporal power law for steady growth found here relates the sidearm neck radii to the tip radius of the primary dendrite stem, and the neck radius probability density distribution is shown to be symmetric. For the more general case of decelerated growth, it is observed that the neck initially widens along the steady growth limit, is then increasingly limited by the pinching tendency, and for sufficiently large decelerations converges ultimately to the pinch-off limit. A dynamical model is derived that predicts the behavior of the sidearm neck radius under arbitrary decelerating growth conditions. The highly variable nature of dendrite fragmentation is explained by the stochastic properties of the initial sidebranch development process. The model can be used to estimate the number of dendrite fragments forming during a certain time interval at any location behind the primary tips.

Pedestrian Trajectory Prediction Based on an Intention Randomness Influence Strategy

Pedestrian trajectory prediction is a key technical prerequisite for autonomous vehicle trajectory planning. However, a pedestrian is a changeable individual, and their intentions exhibit certain degrees of randomness and uncertainty, which leads to the issue that modeling only past trajectories does not enable the effective description of the random intentions and future trajectory directions of the pedestrian. Therefore, this paper proposes a flexible and embeddable stochastic intention vector construction strategy for modeling sudden pedestrian intention changes in real scenes and for better fitting the stochastic properties of pedestrian behaviors. First, we dynamically fuse historical trajectory information with random factors and construct an intention change probability based on the historical trajectory fitting errors of pedestrians, aiming to explicitly model the associated direction and velocity changes caused by random pedestrian intentions. Second, a new intention loss function is designed to guide the model to adaptively learn the probability of intention changes, which is used to dynamically describe pedestrian intention changes. Our proposed method is generalizable and can be applied as an embeddable module to any baseline pedestrian trajectory prediction method. The experimental results obtained on multiple large-scale public pedestrian trajectory prediction datasets demonstrate that our strategy achieves consistent performance improvements over different baselines.

Non-Markovian Feedback Retrial Queue with Two Types of Customers and Delayed Repair Under Bernoulli Working Vacation

This paper deals with an M/G/1 feedback retrial G-queue and delayed repair incorporating Bernoulli working vacation. Arrivals of both favorable and unfavorable consumers are two independent Poisson processes. The server is subjected to breakdown during the regular busy period due to the arrival of unfavorable customers and then the server will be down for a short period of time. Further, the concept of delay time is also discussed. After the fulfillment of regular service, the dissatisfied consumer may re-join the orbit to receive another service as a feedback consumer. During the working vacation period, the server provides the service to consumers at a reduced rate. By applying the supplementary variable technique (SVT), we have analyzed the steady-state probability generating function (PGF) for the system size and orbit size. In addition, we have presented the system performance, reliability indicators, and stochastic decomposition property. Finally, we provided numerical examples to illustrate our model’s mathematical results.

Reduction of oxygen content in laser powder bed fusion process atmosphere – Effects on stochastic defect formation and mechanical properties

Stochastic defects in materials manufactured via laser powder bed fusion (LPBF) can severely compromise mechanical performance and are challenging to predict and detect, thus motivating the development of defect mitigation strategies. Particle oxidation is a factor well-known to generate defects by disturbing melt pool dynamics. If the particles are spatters, additional disturbances increase the likelihood of defect formation. In this study, restricting oxygen content in the process atmosphere to 50 ppm is investigated to minimize stochastic spatter-induced defects and improve the mechanical properties of Hastelloy X. Specimens were manufactured under this condition at two nominal layer thicknesses, analyzed for internal defects, and mechanically tested. Contrary to expectations, reducing the oxygen content did not prevent spatter-induced defect formation; rather, it could exacerbate the formation of more numerous and larger defects. Nevertheless, this tighter control of the process atmosphere led to significant microstructural refinement, which, when combined with sparse defects, resulted in improved fatigue performance. Despite the inherent ductility of Hastelloy X, the presence of abundant defects significantly larger than the microstructural characteristic size proved detrimental to fatigue performance. Notably, the occurrence of defects exhibited considerable variation across the build area, contributing to scatter in fatigue data. However, quantitative analysis of in-situ monitoring data enabled prediction of variability in defect content and mechanical performance.

Persistence analysis of the real estate US index and major cities

This paper analyses the stochastic properties of the US real estate market in the long term (1987–2023). For this purpose, we rely on fractionally integrated methods to fill the gap left by studies focusing on possible cointegration that use linear unit root tests. Both original and log transformed data of house price indices have been used with and without taking inflation into account. Even though several studies mentioned mean reversion in the short term, in the long term the real estate market shows a clear pattern of high persistence and no reversion to the mean. The effect of inflation seems to add correlation to real prices and conversely, an increase in the integration factor. Structural breaks reveal that recent values of this integration factor are greater, thus prices are becoming more persistent.

A New Conjugate Gradient Method for Impact Load Identification of Stochastic Composite Structures

This paper presents a novel conjugate gradient method to identify the impact force of stochastic composite structure. Firstly, the proposed method is established based on constructing a new gradient regularization operator, and its stability and global convergence are strictly proven. Moreover, the proposed method solves the deterministic inverse problem of composite laminated cylindrical shells. Then, using the matrix perturbation method, the uncertain inverse problem of the impact force reconstruction of stochastic structure is converted to definite inverse problems. At last, the statistical properties of the reconstructed impact load are also analyzed. Numerical simulations show that the proposed method performs well in identifying the impact force of composite laminated cylindrical shells with stochastic and non-stochastic properties.

Exact Characterization of the Jointly Optimal Restocking and Auditing Policy in Inventory Systems with Record Inaccuracy

We present a continuous-time stochastic model of an inventory system with record inaccuracy. In this formulation, demand is modeled by a point process and is observable only when it leads to sales. In addition to demand that can reduce the stock, an unobservable stochastic loss process can also reduce the stock. The retailer’s goal is to identify the restocking and auditing policy that minimizes the expected discounted cost of carrying a product over an infinite horizon. We analytically characterize the optimal restocking and jointly optimal auditing policy. We prove that the optimal restocking policy is a threshold policy. Our proof of this result is based on a coupling argument that is valid for any demand and loss model. Unlike the optimal restocking policy, the jointly optimal auditing policy is not of threshold type. We show that a complete proof of this statement cannot be obtained by solely resorting to the first-order stochastic dominance property of the Bayesian shelf stock distribution induced by the demand and loss process. Instead, our characterization of the jointly optimal auditing policy is based on proving that the dynamics of the shelf stock distribution constitute a (strictly) sign-regular kernel. To our knowledge, this is the first paper that characterizes the optimal policy of a complex control problem by establishing sign regularity of its underlying Markovian dynamics. Our theoretical results lead to a fast algorithm for computing the exact jointly optimal auditing/restocking policy over the problem’s entire state space. This enables comparative statics analysis, which allows us to determine how inventory record inaccuracy affects the economic significance of various cost drivers. This, in turn, allows us to determine when or, better, under what conditions auditing can be an effective tool for reducing the total cost.

Stochastic monotone wing property: A new dependence structure for copulas

In this study, we introduce a novel concept of a copula dependence structure termed the stochastic monotone wing property and investigate its associated properties. This concept is closely related to the monotone conditional variance property, which describes the behavior of the conditional variance of V given U=u as either an increasing or a decreasing function of u for a given random vector (U,V) with a certain distribution function. The need for such a dependence structure arises in various fields, including insurance, where a copula family with the monotone conditional variance property is needed for the joint distribution of frequency and average severity in the collective risk model. Beyond insurance, its applications extend to economics and climatology. Furthermore, this study proposes a new parametric copula family that exhibits the stochastic monotone wing property and provides a method for constructing copulas with this characteristic.

Nanophotonic resistive switch based on tapered copper-silicon structure with low power and high extinction ratio

A non-volatile nanophotonic resistive switch in tapered copper-on-silicon structure is proposed with a potential of electrical writing and optical readout. Incorporating tapered hybrid plasmonic structure in the device enables variable areas of the density of conduction filaments in the direction of optical propagation causing enhanced interaction of light with the lossy metal filaments. The tapered Cu-SiO2-Si is shown to have large optical extinction ratio where high mobility of copper ions in thin SiO2 leads to the low power operation. Optical readout of the resistive switching is demonstrated in the 10 nm thin ion conducting layer (SiO2) at a 1550 nm wavelength with well-defined hysteresis curve. Switching between off and on states is attained by voltage induced annihilation/formation of the nanoscale metal filament inside a thin layer of SiO2 sandwiched between copper and silicon. An optical extinction ratio of 18 dB is reported for a 30 μm long device. In addition, the proposed nanophotonic switch exhibits an intrinsic stochastic property of set voltage reduction in each set-reset cycle. High extinction ratio, reduced set voltage, low power consumption, and non-volatility make the device excellent choice for applications in realizing ultra-compact on-chip devices for optical switching, modulation, and neuromorphic computing.

Analyzing the degree persistence of shocks to energy security of the G7 countries: Evidence using panel SPSM-quantile unit root test

Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980–2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security.

Permanent VS Transitory Shocks to Electricity Consumption: Panel Evidence from 19 African Countries

ABSTRACT: Considering Africa's increasing electricity demands and the pivotal role of electricity in socio-economic development, this study addresses a research gap in understanding the stochastic properties of per capita electricity consumption in African contexts. Through examining the integration properties of per capita electricity consumption across 19 African countries, the research determines whether 'shocks' yield transitory or permanent effects, aiding policy formulation and future demand forecasting. Annual electric power consumption data (1971 – 2014) (kWh per capita) from the WDI across 19 African countries are used. Our methodology starts with formally testing for cross-sectional dependence (CSD) using various tests to avoid potential size distortions. Subsequently, we employ the novel panel stationarity test of Nazlioglu and Karul (2017), a Fourier KPSS panel stationarity test. The robustness of the analysis is enriched by incorporating the Hadri and Kurozumi (2011) panel stationarity test and Nazlioglu et al. (2021) new panel stationary tests. The CSD test results reveal mutual dependency in electricity consumption among the sampled African countries, underscoring the need for a panel testing methodology that accommodates CSD. The results of the panel stationarity tests that allow CSD and structural breaks—specifically, the Nazlioglu and Karul (2017) test, which models structural breaks as a gradual process—reject the null hypothesis of trend stationarity for both single frequencies, i.e., k =1, and 2. Similarly, all new, complementary panel stationarity tests from Nazlioglu et al. (2021) reject the null hypothesis of stationarity for both frequencies. Collectively, the outcomes from various panel stationarity tests present strong evidence of nonstationary per capita electricity consumption within the sampled African countries. Additionally, the Nazlioglu and Karul panel stationarity test enables an analysis of the individual Fourier KPSS test for cross-sectional units in the panel. Of the 19 countries, only Togo does not reject the null hypothesis of stationarity for both frequencies. These individual country results align with the panel outcomes, providing compelling evidence for the nonstationarity of the per capita electricity consumption series across the investigated African nations. Accordingly, shocks to electricity consumption will have permanent effects, indicating that per capita electricity consumption will not return to its pre-shock equilibrium path. In this case, demand or consumption stabilizing policies will have long-run effects.

Characterization and prediction of tensile properties of carbon fiber-reinforced thermoplastics composed of hybrid short carbon fiber/PA6 fiber nonwoven mats

Hybrid carbon fiber/resin fiber nonwoven mats are considered as a highly promising application of recycled carbon fibers due to their low cost and simple synthesis process. However, the mechanical properties of hybrid nonwoven mats are subject to uncertainties due to nonuniformity of fiber orientations during production procedures, which restricts their use in structural components. This study investigates the characteristics of various types of hybrid nonwoven mats with different volume fractions and fiber orientation distributions. Notably, the investigation yields noteworthy tensile properties, exemplified by a tensile strength of 367.20 MPa and a stiffness of 32.85 GPa, when the volume fraction is 28.9%. Tensile properties of the hybrid nonwoven mats and their fluctuations are then analyzed through statistical analyses. A three-dimensional microstructure-based fiber network model is established using the Monte Carlo method to represent the statistical and stochastic properties of the material. Furthermore, a prediction method of tensile properties based on microstructural properties in the model is proposed, which shows good agreement with all experimental results for adjustments of the volume fraction of carbon fibers, distribution of fiber lengths, and anisotropy of the materials, significantly reducing the experimental burden for optimization research. The tensile characteristics of composites are reliably anticipated with a nominal error margin below 10%. Additionally, this prediction method enables the prediction of scatter and stable standard deviation, making it possible to apply hybrid nonwoven mats in recycling and industrial processes.

The Circumstance-Driven Bivariate Integer-Valued Autoregressive Model.

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model for non-stationary count time series is proposed. The non-stationarity of the bivariate count process is defined by a joint categorical sequence, which expresses the current state of the process. Additional cross-dependence can be generated via cross-dependent innovations. The model can also be equipped with a marginal bivariate Poisson distribution to make it suitable for low-count time series. Important stochastic properties of the new model are derived. The Yule-Walker and conditional maximum likelihood method are adopted to estimate the unknown parameters. The consistency of these estimators is established, and their finite-sample performance is investigated by a simulation study. The scope and application of the model are illustrated by a real-world data example on sales counts, where a soap product in different stores with a common circumstance factor is investigated.

What is the nature of responses of energy security to shocks in the E7 countries? Fresh evidence by applying unit root tests

ABSTRACT Energy security, characterized by equitable distribution of sustainable, acceptable and affordable energy sources, carries significant social, economic,\\ and environmental consequences. Achieving energy security is influenced by unanticipated shocks, such as oil price volatility, and policy decisions, including diversifying energy sources and developing green technologies. To gauge the efficiency of shocks and policies in altering energy security dynamics of E7 countries, the present study uses the Energy Security Risk Index (ESRI) as a proxy of energy security and employs panel Lagrange multiplier (LM) with smooth breaks and univariate LM unit root test with sharp breaks. In addition, both tests allow for cross-sectional dependence. Our findings reveal distinct dynamics across countries over the period 1980–2018. Specifically, Brazil, India, Indonesia and Turkey exhibit stationary behaviour in their ESRI, while China shows a unit root process. The stochastic properties of Mexico are inconclusive. Our results have important implications, which are discussed in detail.

α-divergence improves the entropy production estimation via machine learning.

Recent years have seen a surge of interest in the algorithmic estimation of stochastic entropy production (EP) from trajectory data via machine learning. A crucial element of such algorithms is the identification of a loss function whose minimization guarantees the accurate EP estimation. In this study we show that there exists a host of loss functions, namely, those implementing a variational representation of the α-divergence, which can be used for the EP estimation. By fixing α to a value between -1 and 0, the α-NEEP (Neural Estimator for Entropy Production) exhibits a much more robust performance against strong nonequilibrium driving or slow dynamics, which adversely affects the existing method based on the Kullback-Leibler divergence (α=0). In particular, the choice of α=-0.5 tends to yield the optimal results. To corroborate our findings, we present an exactly solvable simplification of the EP estimation problem, whose loss function landscape and stochastic properties give deeper intuition into the robustness of the α-NEEP.

Analysis of Stochastic Properties of MEMS Accelerometers and Gyroscopes Used in the Miniature Flight Data Recorder

Analysis of Stochastic Properties of MEMS Accelerometers and Gyroscopes Used in the Miniature Flight Data Recorder

Experimental Investigation of Compressive Concrete with Different Immersion Times and Its Stochastic Damage Model

Continuous large amounts of precipitation can lead to a rapid increase in the water content of concrete in village building foundations, which can adversely affect the mechanical properties, such as the compressive strength of concrete. There are few experimental studies on the compressive stochastic mechanical properties of concrete in the wet state after considering different soaking times (different water contents and saturations), but there is no corresponding stochastic damage principal structure model. In this study, the mechanical properties of concrete under different immersion times were tested to obtain the mechanical properties of the concrete degradation law, and the random damage intrinsic model of wet concrete was established. The results of this paper were compared with the classical test results from the literature to verify the validity of the model. The results show that the proposed stochastic damage model is able to consider both the effects of the saturation and the damage behavior of wet concrete under compression, which is beneficial to the structural design and maintenance protection of village buildings in areas with abundant precipitation.

Co-volatility dynamics in global cryptocurrency and conventional asset classes: a multivariate stochastic factor volatility approach

PurposeThis study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.Design/methodology/approachIt used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.FindingsThe study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.OriginalityThis study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.