Conventional Stochastic Model Research Articles

Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow

Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow

A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance

BackgroundPrediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.MethodologyTo predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).ResultsThe study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.ConclusionIn conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

N-Player Stochastic Duel Game Model with Applied Deep Learning and Its Modern Implications.

This paper provides a conceptual foundation for stochastic duels and contains a further study of the game models based on the theory of stochastic duels. Some other combat assessment techniques are looked upon briefly; a modern outlook on the applications of the theory through video games is provided; and the possibility of usage of data generated by popular shooter-type video games is discussed. Impactful works to date are carefully chosen; a timeline of the developments in the theory of stochastic duels is provided; and a brief literature review for the same is conducted, enabling readers to have a broad outlook at the theory of stochastic duels. A new evaluation model is introduced in order to match realistic scenarios. Improvements are suggested and, additionally, a trust mechanism is introduced to identify the intent of a player in order to make the model a better fit for realistic modern problems. The concept of teaming of players is also considered in the proposed mode. A deep-learning model is developed and trained on data generated by video games to support the results of the proposed model. The proposed model is compared to previously published models in a brief comparison study. Contrary to the conventional stochastic duel game combat model, this new proposed model deals with pair-wise duels throughout the game duration. This model is explained in detail, and practical applications of it in the context of the real world are also discussed. The approach toward solving modern-day problems through the use of game theory is presented in this paper, and hence, this paper acts as a foundation for researchers looking forward to an innovation with game theory.

Modeling the Risk in Mortality Projections

Capturing the Uncertainty in Long-Term Mortality Forecasts The uncertainty in future longevity presents a substantial risk factor for insurance companies, pension funds, and retirement systems. In “Modeling the Risk in Mortality Projections,” Zhu and Bauer present novel stochastic models for analyzing this longevity risk that focus on the uncertainty associated with long-term mortality projections and capture the evolution of mortality forecasts over the past decades. They arrive at their models by analyzing time series of mortality forecasts in a forward modeling framework, which contrasts with conventional stochastic mortality models that are built on age-specific realized mortality rates. The authors showcase their models in exemplifying financial applications in both traditional life insurance markets and the emerging longevity risk transfer market. A key takeaway is that uncertainty in positions that depend on the long-term evolution of mortality is substantially greater under their models than suggested by conventional models.

Semi-stochastic generation of rock discontinuity networks based on traces exposed on cavern roof

The rapid development of digital mapping technologies such as terrestrial laser scanning, digital photogrammetry has allowed the exact acquisition of superficial information of discontinuities in rock exposures. Nevertheless, it remains challenging to quantify the spatial dimension and areal persistence of discontinuities due to the impossibility of direct observation of complete extent of discontinuities in three dimensions. The difficulties of characterizing individual discontinuities deterministically lead to the development of stochastic 3-D discontinuity networks. The stochastic modeling scheme are based on the statistical analysis of the measured discontinuity parameters; hence, the generated discontinuity networks represent discontinuity system in a statistical sense, and in most cases, the discontinuity traces formed by stochastic model are inconsistent with those observed at rock outcrops. To deal with this issue, this study presented a new method to simulate discontinuity network, which employs the real traces exposed on the cavern roof as the constraint condition of stochastic modeling and is described herein as semi-stochastic. The method takes discontinuities as circular discs and establishes geometrical relationship between the radius and the center of circular disc respectively for three classes of traces (i.e. trace has two ends, only one end, and neither end, visible in the sampling window). A case study with the proposed method shows that the traces yielded by the intersections of modeled discontinuity network with the cavern roof are in close agreement with actual situation. This semi-stochastic method further improves conventional stochastic modeling technique in terms of enabling a more realistic representation of rock discontinuities.

A formal dynamic stochastic model for single-frequency precise point positioning constrained by global ionosphere map

Single-frequency precise point positioning (SF-PPP), having few redundant observations, needs a long convergence time to achieve a positioning accuracy level of decimetres to centimetres. An analytic use of external ionospheric information can improve the positioning performance of SF-PPP. However, it will instead deteriorate the SF-PPP performance, if the stochastic characteristics of the ionospheric pseudo-observations are determined incorrectly. In this contribution, a method for determining a formal dynamic stochastic model is proposed for ionosphere-constrained SF-PPP using ionospheric information from global ionosphere map. First, a temporal loosening constraint is adopted for ionospheric pseudo-observations, to gradually decrease their contributions to SF-PPP solutions over time. Second, for each epoch, a search algorithm based on the principle of minimizing the standard deviation of the unit weight is used to find the optimal variances of the ionospheric pseudo-observations. Experiments based on 7d of Global Positioning System observation data from 44 Multi-Global Navigation SatelliteSystem experimental stations were carried out to validate the proposed stochastic model, using positioning performance and tropospheric delay retrieval. The results indicate that ionosphere-constrained SF-PPP with a conventional stochastic model exhibits little improvement and even worse positioning performance, compared to traditional ionosphere-free-half (IFH) SF-PPP. Nevertheless, ionosphere-constrained SF-PPP with the proposed stochastic model significantly improves positioning performance, compared with IFH SF-PPP. The use of the proposed model can reduce the convergence time by 89.2%, 34.2%, and 0.0% in the static mode and 57.3%, 50.6%, and 24.1% in the kinematic mode for the east, north, and up directions, respectively, and increase the positioning accuracy by 41.1%, 29.6%, and 23.7% in the static mode and 45.5%, 38.4%, and 30.6% in the kinematic mode for the three directions, respectively. For tropospheric-delay retrieval, the zenith total-delay-estimation accuracy of ionosphere-constrained SF-PPP with the conventional stochastic model is much poorer than that of IFH SF-PPP, while that of the ionosphere-constrained SF-PPP with the proposed stochastic model is comparable to IFH SF-PPP.

STRATEGIC ALLIANCE FOR BLOCKCHAIN GOVERNANCE GAME

This paper deals with design of an alternative secure Blockchain network framework to prevent damages from an attacker. The alliance concept from the strategic management perspectives is applied on the top of a general stochastic game framework. This new enhanced hybrid theoretical model is designed to find the best strategies toward preparation for preventing a network malfunction from an attacker through strategic alliances with other genuine nodes and it is developed based on the combination of a strategic management framework and a conventional stochastic model based on the Blockchain Governance Game. Analytically, tractable results for decision-making parameters are fully obtained to predict of the moment for operations and also to provide the optimal number of allegiance nodes to protect a Blockchain network. This research helps those whom are considering initial coin offering or launching new Blockchain-based services by enhancing security features through strategic alliances in a decentralized network.

VoIP-HDK – A novel channel allocation technique for QoS aware VoIP communication over heterogeneous networks

Abstract Channel allocation for VoIP (Voice over IP) is a critical problem in modern-day heterogeneous networks having diverse application requirements. Conventional stochastic and machine-learning based models that predict channel suitability for VoIP underperform under dynamic conditions. In contrast, this paper deploys VoIP-HDK2 – a novel technique that uses an efficient combination of K-Nearest Neighbors algorithm, 2-order Hidden Markov Model, Damerau-Levenshtein distance based model and K-Means Clustering technique to accurately predict and allocate channels with sufficient QoS (Quality of Service) guarantees to VoIP calls. The primary contribution of this paper lays both in the formulation of the proposed methodology involving tailor-made algorithms as well as complete test-bed based dataset creation, execution of VoIP-HDK2 and validation of its performance in terms of maintaining high quality VoIP calls. Comparative performance evaluation records significant reduction in call drops and packet-loss and enhancement in call quality and throughput for VoIP-HDK2 with respect to existing works in literature.

The Zumbach effect under rough Heston

Previous literature has identified an effect, dubbed the Zumbach effect, that is nonzero empirically but conjectured to be zero in any conventional stochastic volatility model. Essentially this effect corresponds to the property that past squared returns forecast future volatilities better than past volatilities forecast future squared returns. We provide explicit computations of the Zumbach effect under rough Heston and show that they are consistent with empirical estimates. In agreement with previous conjectures however, the Zumbach effect is found to be negligible in the classical Heston model.

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

AbstractThe leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on‐line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.

Heterogeneity, transient and persistent technical efficiency of Polish crop farms

Accounting for heterogeneity in the measurement of farm efficiency is crucial to avoid biases related to climate and soil quality diversity in a given area. Therefore, this paper investigates the level of technical efficiency (TE) of Polish crop farms based on several stochastic frontier panel data models with different approaches to the measurement of unobserved heterogeneity, short- and long- run inefficiency. In our study, we show that ignoring farm heterogeneity can lead to underestimation of the level of TE in conventional stochastic frontier panel data models. Moreover, we have found empirically that not accounting for heterogeneity in the Generalized True Random Effects model may lead to incorrect estimates of persistent TE. The obtained results for Polish crop farms indicate that the level of transient TE (0.81) is lower than the level of persistent TE (0.88). This result suggests that Polish farms may have, for example, problems with adopting new technologies and poor managerial skills.

О вейвлет-преобразованиях при моделировании цен акций нечеткими системами

Models for time series are very important for the stock market. Fuzzy Takagi - Sugeno models (functional fuzzy systems) are a promising and already common approach, in which different regression dependencies are used for different areas of variation of certain parameters, and soft switching is performed using the fuzzy logic rules. This is the advantage of this approach over conventional stochastic models. Each Takagi-Sugeno model is based on its set of fuzzy rules. These models can be viewed as a generalization of classical econometric models, if one such model corresponds to one fuzzy rule. This paper studies the possibility of using the wavelet transform and fuzzy Takagi - Sugeno model to analyze the dynamics of stock prices for the following Russian companies: Gazprom, Sberbank, Magnit, Yandex and Aeroflot; this approach was previously used to study some foreign stock markets. Wavelet analysis quite often acts as a tool for signal processing, including time series, as it allows for a multi-level approximation. In this paper, the Takagi - Sugeno model is based on untransformed data as well as data transformed using Haar wavelets. Fuzzy clustering is used to construct membership functions. Calculations show that the use of wavelets often improves the predictive characteristics of the model.

COMPARISON OF STOCHASTIC FRONTIER APPROACHES FOR ESTIMATING NATIONAL EFFICIENCY: AN APPLICATION TO SUB-SAHARAN AFRICAN COUNTRIES

In this paper, we attempt to estimate pure national (technical) efficiency for 19 SSA countries over the 1960-2010 periods. In doing this, we compare conventional stochastic frontier models for panel data with a number of recently developed models which seek to control for unobserved heterogeneity in the inefficiency component. We find that the i®truei¯ random effects model that treats unobserved heterogeneity in our national dataset generates more reasonable efficiency estimates. Moreover the results confirm that most SSA countries operate far from the efficient frontier.

The Zumbach Effect Under Rough Heston

Previous literature has identified an effect, dubbed the Zumbach effect, that is nonzero empirically but conjectured to be zero in any conventional stochastic volatility model. Essentially this effect corresponds to the property that past squared returns forecast future volatilities better than past volatilities forecast future squared returns. We provide explicit computations of the Zumbach effect under rough Heston and show that they are consistent with empirical estimates. In agreement with previous conjectures however, the Zumbach effect is found to be negligible in the classical Heston model.

Inter-well scale natural fracture geometry and permeability variations in low-deformation carbonate rocks

Regional natural fracture networks often show variations on a scale below that captured by seismic reflection data. This variability is not considered in most reservoir models, but likely impacts uncertainties in permeability. We quantify this uncertainty using a database of 13,000 fractures in nine outcrops digitised in the carbonate Jandaíra Formation (Potiguar basin, Brazil). Distance between outcrops is on average 11 km, with a minimum of 300 m, which is comparable to the distance between wells in naturally fractured reservoirs. In between outcrops, significant variations exist in orientation, intensity, length and topology. Using discrete fracture-matrix flow models, we model the permeability of each deterministic pattern and find that small changes in geometry and topology result in permeability variations that are not captured by connectivity-based analyses such as percolation probabilities, particularly when the matrix is permeable. The permeability variations associated with subseismic-scale fracture variability are not captured in conventional stochastic models, but can be captured using deterministic outcrop models with flow through discrete fractures. The deterministic models provide a permeability range associated with subseismic fracture variability, that can be assigned to grid cells of fractured reservoir flow models, as an alternative to assuming constant permeability in the absence of subseismic-scale deformation.

Microfoundations for Performance, Competition and Econometric Implications

We consider two agency-theory-based structural models and a reduced form model of bank inefficiency. The two structural models assume that the inefficiencies and quantities are sequentially and simultaneously determined, respectfully. The reduced form model assumes that inefficiency of a bank depends on other banks' inefficiencies. We argue that conventional stochastic frontier models would give inconsistent parameter estimates as they ignore systematic nature of managerial decision making process. We estimate the inefficiencies of Eurozone banks using our novel structural and reduced form approaches that calls for certain MCMC techniques. Bayes factor favors the sequential model where the efficiency is determined first. In the empirical application for the Eurozone, we find a pick in structural and non-structural inefficiency after the financial crisis and considerable variability across Member States. A way of tackling such banking imbalances is to move towards a banking union.

A stochastic inversion workflow for monitoring the distribution of CO2 injected into deep saline aquifers

We present a two-step stochastic inversion approach for monitoring the distribution of CO2 injected into deep saline aquifers for the typical scenario of one single injection well and a database comprising a common suite of well logs as well as time-lapse vertical seismic profiling (VSP) data. In the first step, we compute several sets of stochastic models of the elastic properties using conventional sequential Gaussian co-simulations (SGCS) representing the considered reservoir before CO2 injection. All realizations within a set of models are then iteratively combined using a modified gradual deformation algorithm aiming at reducing the mismatch between the observed and simulated VSP data. In the second step, these optimal static models then serve as input for a history matching approach using the same modified gradual deformation algorithm for minimizing the mismatch between the observed and simulated VSP data following the injection of CO2. At each gradual deformation step, the injection and migration of CO2 is simulated and the corresponding seismic traces are computed and compared with the observed ones. The proposed stochastic inversion approach has been tested for a realistic, and arguably particularly challenging, synthetic case study mimicking the geological environment of a potential CO2 injection site in the Cambrian-Ordivician sedimentary sequence of the St. Lawrence platform in Southern Quebec. The results demonstrate that the proposed two-step reservoir characterization approach is capable of adequately resolving and monitoring the distribution of the injected CO2. This finds its expression in optimized models of P- and S-wave velocities, density, and porosity, which, compared to conventional stochastic reservoir models, exhibit a significantly improved structural similarity with regard to the corresponding reference models. The proposed approach is therefore expected to allow for an optimal injection forecast by using a quantitative assimilation of all available data from the appraisal stage of a CO2 injection site.

High order fuzzy time series forecasting method based on an intersection operation

The use of non-stochastic models such as fuzzy time series forecasting models for time series analysis has attracted the attention of researchers in recent years. Fuzzy time series forecasting models do not need strict assumptions, whereas conventional stochastic models need to satisfy some assumptions. In addition, fuzzy time series methods can be used if the observations of time series have uncertainty. Fuzzy time series approaches comprise three basic steps: fuzzification of the crisp observations, identification of fuzzy relations, and defuzzification. In previous studies, many methods have been proposed that allow all of these stages to obtain more accurate forecasting results. One of the weakest features of fuzzy time series methods is that the membership values are not considered in the forecasting process. This problem can be eliminated in first order approaches by using artificial neural networks to describe fuzzy relations. When determining the fuzzy relations, the membership values are not ignored if the inputs and outputs of the neural networks are the membership values for the periods t−1 and t, respectively. However, the number of inputs of neural networks will increase greatly if this approach is extended to high-order models. Thus, it will be very difficult to train these neural networks. In this study, we propose a novel high-order fuzzy time series approach that considers the membership values, where artificial neural networks are employed to identify the fuzzy relations. In the proposed method, intersection operators are utilized to deal with an excessive number of inputs. In addition, the fuzzy c-means method is employed for fuzzification. The forecasting performance was evaluated by applying the proposed method to well-known time series data sets and the results obtained were compared with those produced by previously described forecasting methods. The superior performance of our proposed method was also supported by a simulation study.

Accounting for unobserved management in renewable energy & growth

The paper employs a management random parameters frontier stochastic frontier and a simple frontier stochastic model to benchmark European countries according to their management efficiency in growth and renewable energy development. The results come from an empirical application of a panel with 31 European countries over a 14 year old period using a translog type stochastic frontier production function. In particular the paper focuses on results from a management random coefficients model and compares results with the conventional stochastic frontier model with inputs such as renewable energy, fossil fuel energy, employment and capital. The results suggest that the interaction of renewable energy with management affects growth in Europe and that the technical efficiency estimated by the management model is by 6.05% higher than the one produced by the simple stochastic frontier model.

Degradation kinetics of dense nonaqueous phase liquids in the environment under impacts of mixed white and colored noises

This study presents three stochastic dense nonaqueous phase liquid degradation kinetic models, forced by Gaussian white noise, Ornstein–Uhlenbeck colored noise and their mixture, respectively. Compared to the previous efforts, the proposed mixed-noise forced model is a more generalized representation of conventional stochastic models. This feature exists because any of the stochastic terms (no matter they come from the inside or out of the system) can be accounted for by a deterministic term and a type of colored, white, or mixed noise. The implicit in the model solutions include: the colored noise may lead to a misunderstanding results and much attention should be received to recognize the impact of colored noises; the decrease of noise intensity and/or increase of correlation time change the probability distribution functions from bimodal to approximate unimodal shapes, implying increased ease of capturing the statistical properties of model solutions; the introduction of a new colored noise may counteract the function of a white noise, thus providing us an alternative for mitigating the impact of a known noise on model solutions. The ongoing work is undertaken to develop an integrated numerical-simulation and statistical-inference method for the characterization of white and colored noises based on observation data.