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As the trendsetter of the digital currency market, Bitcoin fluctuates dramatically in a short period of time and has received increasing attention from investors. However, its high volatility has brought great uncertainty to the financial market. In this paper, we focus on forecasting the realized volatility of Bitcoin by using an optimized deep learning model. Firstly, we construct a more comprehensive system of factor indicators and employ different methods for feature selection, and find that the Random Forest-based feature selection fits better on the deep learning model. Then, we use the particle swarm optimization (PSO) algorithm to optimize the parameters of gated recurrent unit (GRU) model to improve the prediction accuracy, and the results show that the prediction accuracy of PSO–GRU model is 10.47%, 15.28%, 21.73%, 34.79% better than the GRU model, long-short term memory model, machine learning models and the generalized autoregressive conditional heteroscedasticity model on the mean absolute error, respectively. Finally, we establish an early risk warning scheme for Bitcoin volatility and a butterfly option arbitrage strategy, that provide investors with a reference for reasonable arrangement of trading strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric behavior of oil price volatility using different types of Asymmetric Power ARCH (APARCH) model. We compare the estimation and forecasting performance of the models estimated from the maximum likelihood estimation (MLE) method and support vector machine (SVM) based regressions. Combining nonparametric SVM method with parametric APARCH model not only enables to keep interpretations of the parametric models but also leads to more precise estimation and forecasting results. Daily or weekly oil price volatility is investigated from March 8, 1991 to September 13, 2019. This whole sample period is split into four sub-periods based on the occurrence of certain economic events, and we examine whether the asymmetric behavior of the volatility exists in each sub-period. Our results indicate that SVM regression generally outperforms the other method with lower estimation and forecasting errors, and it is more robust to the choice of different APARCH models than the MLE counterparts are. Besides, the estimation results of the SVM based regressions in each sub-period show that the ARCH models with asymmetric power generally perform better than the models with symmetric power when the data sub-period includes large swings in oil price. The asymmetric behavior of oil price volatility, however, is not detected when the analysis is done using the whole sample period. This result underscores the importance of identifying the dynamics of the dataset in different periods to improve estimation and forecasting performance in modelling oil price volatility. This paper, therefore, examines volatility behavior of oil price with both methodological and economic underpinnings. [ABSTRACT FROM AUTHOR]

Rock anchors, which can withstand substantial forces and are essential for securing the foundations of the built environment, can fail due to steel tensile failure, bond detachment, or rock mass fracturing through uplift. In this study, uplift failure of the rock mass surrounding rock anchors was simulated using both physical and numerical models. The physical models used a specially designed testing rig with predefined sizes and different joint layouts to simulate rock mass uplift failure. The numerical models were calibrated using the physical models and used to investigate scenarios involving increased complexity and greater depths. This paper presents the load distribution within rock masses with orthogonal joints when exposed to an anchor load. Extensive analysis of the numerical models revealed that symmetrical load arches were induced in rock masses that contained a joint set oriented parallel to the loading direction. However, joints that were obliquely oriented relative to the anchor axis induced asymmetrical load arching. The greatest displacement occurred in the direction parallel to the joint set with the smallest angle to the anchor loading axis. Large-scale models with zero or near-zero in situ horizontal stress revealed that the load capacity in a rock mass with continuous vertical joints was relatively low, especially when compared to rock masses with discontinuous vertical joints. It was observed that the uplift capacity of the rock mass around an anchor decreased if the load arches of adjacent anchors overlapped; consequently, a decrease in anchor spacing was found to correspond to a decrease in uplift capacity. Highlights: Symmetrical load arches are induced within a rock mass if the joint sets are oriented parallel to or close to parallel to the anchor axis. An oblique orientation of the joints relative to the anchor axis results in asymmetrical load arching in the rock mass when the anchor is loaded. Rock masses with continuous vertical joints have a low load capacity, especially compared to rock masses with discontinuous vertical joints. In a row of rock anchors, the rock mass uplift capacity of individual anchors is decreased if the anchor spacing is too low. [ABSTRACT FROM AUTHOR]

This paper analyses the volatility transmission between European Global Systemically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe's GSIBs and the world's most prominent measure of market "fear", the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics. [ABSTRACT FROM AUTHOR]

In time-series analysis, particularly in finance, generalized autoregressive conditional heteroscedasticity (GARCH) models are widely applied statistical tools for modelling volatility clusters (i.e., periods of increased or decreased risk). In contrast, it has not been considered to be of critical importance until now to model spatial dependence in the conditional second moments. Only a few models have been proposed for modelling local clusters of increased risks. In this paper, we introduce a novel spatial GARCH process in a unified spatial and spatiotemporal GARCH framework, which also covers all previously proposed spatial ARCH models, exponential spatial GARCH, and time-series GARCH models. In contrast to previous spatiotemporal and time series models, this spatial GARCH allows for instantaneous spill-overs across all spatial units. For this common modelling framework, estimators are derived based on a non-linear least-squares approach. Eventually, the use of the model is demonstrated by a Monte Carlo simulation study and by an empirical example that focuses on real estate prices from 1995 to 2014 across the postal code areas of Berlin. A spatial autoregressive model is applied to the data to illustrate how locally varying model uncertainties (e.g., due to latent regressors) can be captured by the spatial GARCH-type models. [ABSTRACT FROM AUTHOR]

Soil arch effect has been widely used in the determination of pile spacing, whereas its application on the design of composite retaining structures to stabilize potentially unstable slopes is still rare. As a typical composite retaining structure, stabilizing piles combined retaining wall could effectively avoid slope failure, while the problem of evaluating the load distribution between stabilizing piles and retaining wall based on soil arch effect remains to be solved. In this paper, a novel soil arch model is proposed and used aiming to theoretically analyze the soil arch effect on the load against stabilizing piles and retaining wall. The results show that the load acting on stabilizing piles should be the residual sliding force derived from rear soil mass, and the load against retaining wall should be the maximum value between the Coulomb's active earth pressure derived from sliding wedge before soil arch and the residual sliding force produced by front soil mass. Then, on this basis, a simplified method for calculating the load on stabilizing piles and retaining wall respectively considering the soil arch effect is put forward. A railway cutting slope reinforced with stabilizing piles combined with retaining wall is taken as an application case, and the simplified method is adopted to quantitatively analyze the soil arch effect on the load distribution between stabilizing piles and retaining wall. The conclusion of this paper would provide useful help toward the process of safer and more economical design of composite retaining structures in slope engineering harness. [ABSTRACT FROM AUTHOR]

We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market. [ABSTRACT FROM AUTHOR]

In this paper, we study the hedging effectiveness of crude oil futures on the basis of the lower partial moments (LPMs). An improved kernel density estimation method is proposed to estimate the optimal hedge ratio. We investigate crude oil price hedging by contributing to the literature in the following twofold: First, unlike the existing studies which focus on univariate kernel density method, we use bivariate kernel density to calculate the estimated LPMs, wherein the two bandwidths of the bivariate kernel density are not limited to the same, which is our main innovation point. According to the criterion of minimizing the mean integrated square error, we derive the conditions that the optimal bandwidths satisfy. In the process of derivation, we make a distribution assumption locally in order to simplify calculation, but this type of local distribution assumption is far better than global distribution assumption used in parameter method theoretically and empirically. Second, in order to meet the requirement of bivariate kernel density for independent random variables, we adopt ARCH models to obtain the independent noises with related to the returns of crude oil spot and futures. Genetic algorithm is used to tune the parameters that maximize quasi-likelihood. Empirical results reveal that, at first, the hedging strategy based on the improved kernel density estimation method is of highly efficiency, and then it achieves better performance than the hedging strategy based on the traditional parametric method. We also compare the risk control effectiveness of static hedge ratio vs. time-varying hedge ratio and find that static hedging has a better performance than time-varying hedging. [ABSTRACT FROM AUTHOR]

This paper studies the dynamics and determinants of financial well-being in the EU-27 area, by comparatively examining its objective and subjective dimensions, based on Eurostat aggregated data. A common set of socio-economic drivers, including economic growth, has been explained in relation with the objective and subjective well-being, instrumented here by variables in the area of the upward income transitions and economic strain, with a view to finding out whether a common set of governmental policies could effectively target both the objective and subjective dimensions of financial well-being. The main econometric tools used in the paper are the generalized method of moments and the feasible generalized least-squares. The empirical results suggest that, while the impact of some determinants is similar for the objective and subjective financial well-being, the impact of others is divergent, in the sense that they could stimulate one dimension of well-being and hinder another. For instance, the paper finds that the social protection expenditure is more effective than the labour market policy expenditure when targeting the financial subjective well-being and the short upward income transitions, but the labour market policy expenditure becomes more effective when targeting the large upward income transitions. Overall, the empirical results indicate that it is difficult to aim at improving all dimensions of well-being by a common set of governmental policies within the EU-27. [ABSTRACT FROM AUTHOR]

This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and risk evaluation of Bitcoin are discussed via three major parametric nonlinear time series models, namely the self-exciting threshold autoregressive model, the generalized autoregressive conditional heteroscedasticity model and the stochastic volatility model. [ABSTRACT FROM AUTHOR]

This paper deals with simultaneous prediction for time series models. In particular, it presents a simple procedure which gives well-calibrated simultaneous prediction intervals with coverage probability close to the target nominal value. Although the exact computation of the proposed intervals is usually not feasible, an approximation can be easily attained by means of a suitable bootstrap simulation procedure. This new predictive solution is much simpler to compute than those ones already proposed in the literature, based on asymptotic calculations. Applications of the bootstrap calibrated procedure to AR, MA and ARCH models are presented. [ABSTRACT FROM AUTHOR]

Circular concrete filled double skin steel tubular (CFDST) has broader application aspects in engineering practice due to its superior confinement and elegant architectural appearance compared with square CFDST. However, there is still lack of an effective and reliable assembled method to connect the circular CFDST column to the beam. This paper focused on the theoretical and structural behavior on the assembled joint between circular CFDST column and composite beam. A sophisticated theoretical model was proposed to evaluate the initial rotational stiffness and moment capacity of the assembled joint. This theoretical model firstly accounted for the curved end plate in bending considering the clamping forces of blind bolts. Furthermore, a simplified arch model was developed to assess the circular steel tube in compression and the stiffness coefficients related to the circular CFDST column determined by the load transfer mechanism were also involved in the theoretical model. Subsequently, an experimental investigation on such kind of joint was conducted as a basis to verify the developed numerical model, indicating the numerical model could well replicate the typical failure modes and hysteresis curves. Parametric analyses based on the validated numerical model were performed to identify the effects of various parameters on the typical assembled joint. The theoretical model was verified to be capable of predicting the initial stiffness and moment capacity of assembled composite joint to circular CFDST column by comparing with the numerically observed results. The theoretical and analytical results performed in this paper would be beneficial for the application of the assembled beam to column joint in CFDST structures. [ABSTRACT FROM AUTHOR]

As a linear regression parameter estimation method, the least square method plays an indispensable role in the parameter estimation of ARCH model. Although the least squares solution can minimize the sum of the squared errors, it will cause uneven distribution of errors, that is, some fitting errors are too large, and some fitting errors are too small, which will lead to overfitting. In response to this situation, we adopt a novel perturbation method to solve this problem. The specific theoretical derivation of the perturbation method is given in this paper. It takes parameters estimated by the least squares method as its initial iteration value. The maximum fitting error will decrease continuously from a series of iterations to final convergence. Furthermore, based on the perturbation method, this paper finds a condition that makes the ARCH model satisfy the non-negative limit of parameters. The experimental process uses real stock fund's fluctuation data for fitting analysis and prediction. The experimental results show that the perturbation method can achieve the expected effect in the parameter estimation of the ARCH model, it can also effectively ensure that the fitting errors fluctuate within the controllable range when predicting the price fluctuation of stock funds in the future. [ABSTRACT FROM AUTHOR]

In this paper, we provide some results on the class of spatial autoregressive conditional heteroscedasticity (ARCH) models, which have been introduced in recent literature to model spatial conditional heteroscedasticity. That means that the variance in some locations depends on the variance in neighboring locations. In contrast to the temporal ARCH model, for which the distribution is known, given the full information set for the prior periods, the distribution is not straightforward in the spatial and spatiotemporal settings. Thus, we focus on the probability structure of these models. In particular, we derive the conditional and unconditional moments of the process as well as the distribution of the process, given a known error distribution. Eventually, it is shown that the process is strictly stationary under certain conditions. [ABSTRACT FROM AUTHOR]

This paper considers the parameter estimation problem of a first-order threshold autoregressive conditional heteroscedasticity model by using the empirical likelihood method. We obtain the empirical likelihood ratio statistic based on the estimating equation of the least squares estimation and construct the confidence region for the model parameters. Simulation studies indicate that the empirical likelihood method outperforms the normal approximation-based method in terms of coverage probability. [ABSTRACT FROM AUTHOR]

The umbrella arch method is a pre-reinforcement technique that ensures tunnel stability in poor ground conditions or shallow overburden. Quantitative assessment of this method is still challenging due to its complexity, there is no accurate method to model the behavior of reinforced arch forming with steel pipes and grout. This paper uses finite difference numerical simulation to model and analyze the effectiveness of the umbrella arch method in Qazvin–Rasht railway tunnel as well as in-situ measurement. Umbrella arch has modeled considering pipe, grout and soilcrete as pipe umbrella components; each pipe simulated individually. The structural behaviour of pipes in the excavation sequence is analyzed as well as the importance of geometrical and physical design elements, on tunnel stability using FLAC 3D software. Results indicate that the umbrella arch method has reduced tunnel convergence by 49%. By continuing the excavation step, residual pipe length ahead of the face and tunnel arch, the location of maximum pipe displacement, force, and moment are changed. Furthermore, by considering executive limitation, an increase in pipe angle, diameter and thickness, length and overlapping length, as well as pipes distance decrease, center to center grout penetration radius, and water-cement ratio 0.5 have had a positive effect in providing more strength arch and tunnel stability. [ABSTRACT FROM AUTHOR]

In this paper, we consider a discrete-time risk model with the claim number following a Poisson ARCH process. In this model, the mean of the current claim number depends on the previous observations. We study the large deviations for the aggregate amount for claims. For a heavy-tailed case, we obtain a precise large deviation formula, which agrees with existing ones in the literature. In computing the moderate deviation principle required by the structure of the claim-number process, our treatment substantially relies on an algorithm specifically designed for the autoregressive structure of our models. [ABSTRACT FROM AUTHOR]

Most accessible mineral deposits are nearly exhausted. Consequently, some resources that were previously abandoned are now being considered for remining. This paper describes a case study that aims at revealing the instability mechanism and selecting the optimal control countermeasures for a cataclastic roadway regenerated roof (CRRR) during the extraction of the remaining mineral resources. Based on the structural model of a cataclastic regenerated roof (CRR) of a remaining lower slice, a modified natural arch mechanical model for a CRRR was established by considering the lateral pressure and stress concentration coefficient of the bearing arch, through which the modified bearing arch curve equation and arch rise expression were deduced. The relationship between the modified arch rise and the lateral stress was obtained through a practical case. Next, a numerical model of a CRRR, which accounted for the cataclastic features, was built in the Universal Distinct Element Code (UDEC). The development of subsidence and fractures in the roadway roof was determined through modeling under various roof support scenarios, namely no support, rock bolt (RB) support, rock bolt and cable (RBC) support and a combined support of the rock bolt, cable and steel beam (RBCS). The theoretical analysis, numerical simulation and monitoring results of the RB and RBC stress all indicated that a bearing arch existed in the CRRR and that the arch rise underwent changes during the roof hanging time and with different support scenarios. The support structure should exceed the bearing arch rise in height and should be leak proof to avoid roof leakage. After a comparative analysis of the numerical and field support effects provided by various supporting scenarios, the RBCS support scenario was shown to be the optimal roof support choice, providing a good controlling effect for a CRRR and meeting the production requirements. [ABSTRACT FROM AUTHOR]

This paper discusses mixture periodic GARCH (M-PGARCH) models that constitute very flexible class of nonlinear time series models of the conditional variance. It turns out that they are more parsimonious comparatively to MPARCH models. We first provide some probabilistic properties of this class of models. We thus propose an estimation method based on the expectation-maximization algorithm. Finally, we apply this methodology to model the spot rates of the Algerian dinar against euro and US dollar. This empirical analysis shows that M-PGARCH models yield the best performance among the competing models. [ABSTRACT FROM AUTHOR]

To provide accurate value-at-risk (VaR) forecasts for the returns of international stock indices portfolio, this paper proposes a dynamic spatial panel with generalized autoregressive conditional heteroscedastic model (DSP-GJR-GARCH). The proposed model considers the spatiotemporal dependence as well as asymmetric volatility of returns, with the theories of spatial econometrics. We construct an economic spatial weight matrix and set part of the initial estimated values as unknown parameters to get more acute of parameter estimations. After that, we compare the proposed model with three closely related models including GARCH, spatiotemporal-AR, dynamic spatial panel GARCH models, with respect to the performances of daily volatility and VaR forecasting. The empirically comparative data involve six composite indices of major countries, namely USA (DJI), German (DAX), France (FCHI), U.K. (ISEQ), Japan (N225) and China (SSE). The comparative computational results show that, since the proposed model considers spatial dependence and time series correlation simultaneously, it could get more accurate prediction of VaR than the three ones. Moreover, the findings reveal that the predictive accuracy of a spatial regressive model can be improved by considering asymmetric volatility in the disturbances. Thus, we can conclude that DSP-GJR-GARCH model performs better than the other three compared models. [ABSTRACT FROM AUTHOR]

First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft/granular RBF network is investigated. To forecast the high-frequency financial data, we apply statistical ARMA and asymmetric GARCH-class models. A novel RBF network architecture is proposed based on incorporation of an error-correction mechanism, which improves forecasting ability of feed-forward neural networks. These proposed modelling approaches and SVM models are applied to predict the high-frequency time series of the BUX stock index. We found that it is possible to enhance forecast accuracy and achieve significant risk reduction in managerial decision making by applying intelligent forecasting models based on latest information technologies. On the other hand, we showed that statistical GARCH-class models can identify the presence of leverage effects, and react to the good and bad news. [ABSTRACT FROM AUTHOR]

This paper examines the implications of the seasonal adjustment by an ARIMA model based (AMB) approach in the context of seasonal fractional integration. According to the AMB approach, if the model identified from the data contains seasonal unit roots, the adjusted series will not be invertible that has serious implications for the posterior analysis. We show that even if the ARIMA model identified from the data contains seasonal unit roots, if the true data generating process is stationary seasonally fractionally integrated (as it is often found in economic data), the AMB seasonal adjustment produces dips in the periodogram at seasonal frequencies, but the adjusted series still can be approximated by an invertible process. We also perform a small Monte Carlo study of the log-periodogram regression with tapered data for negative seasonal fractional integration. An empirical application for the Spanish economy that illustrates our results is also carried out at the end of the article. [ABSTRACT FROM AUTHOR]

This paper concerns the specification of multivariate prediction regions which may be useful in time series applications whenever we aim at considering not just one single forecast but a group of consecutive forecasts. We review a general result on improved multivariate prediction and we use it in order to calculate conditional prediction intervals for Markov process models so that the associated coverage probability turns out to be close to the target value. This improved solution is asymptotically superior to the estimative one, which is simpler but it may lead to unreliable predictive conclusions. An application to general autoregressive models is presented, focusing in particular on AR and ARCH models. [ABSTRACT FROM AUTHOR]

The paper reviews the topic of extremal time series. The literature documenting the presence of extremes in time series data is first reviewed, followed by a discussion of various probabilistic measures, along with the associated statistical inference problems. The impact of extremes upon statistical analyses is discussed, and the connection to extremal latent components is emphasized. Two data sets illustrate the methods. [ABSTRACT FROM AUTHOR]

In this paper, we estimate generalized autoregressive conditional heteroskedasticity (GARCH) and vector autoregressive (VAR) models to examine whether investor sentiment impacts the returns and volatility of various U.S. Dow Jones Islamic equity indices. The results from GARCH estimations show that changes in investor sentiment are positively correlated with the returns of the Shari'ah-compliant market portfolio. In addition, we find similar results for the three Shari'ah-compliant firm-size portfolios (i.e., large-, medium-, and small-cap). However, this relationship is stronger for harder to arbitrage Shari'ah-compliant stocks; that is, investor sentiment has a greater influence on small-cap equities. Additionally, estimations from the vector autoregressive model confirm the aforementioned results. In terms of volatility, GARCH estimations suggest that bullish shifts in investor sentiment in the current period are accompanied by lower conditional volatility in the ensuing period. In general, our findings suggest that as noise traders create more risk the market seems to reward them with higher expected returns. [ABSTRACT FROM AUTHOR]

This paper examines the role of R&D spending on economic growth of developing economies between the periods of 2000 and 2009. The impact of R&D spending on these economies is determined by using dynamic system GMM, pooled mean group and three stage least square-GMM models. Sixty-six countries are studied and further grouped as; upper-middle-income economies and lower middle-income economies. The result reveals a beneficial impact of R&D spending on economic growth in developing countries. The effect of R&D spending on growth is positive for upper middle-income economies while insignificant in lower income economies. R&D spending has different short and long run effects on growth. Using simultaneous equation models to account for simultaneity and endogeneity, R&D spending remains beneficial to growth and the results remain consistent with the dynamic system estimator. [ABSTRACT FROM AUTHOR]

The effect of a supports settlement in a symmetric masonry arch is studied by modeling the arch as a system of rigid plastic blocks with frictional sliding. It is shown that in a collapse state due to a horizontal settlement the thrust attains its minimum statically admissible value, so extending to the case of frictional sliding a well-known result of standard limit analysis, which considers collapse states only due to the opening of joints. The case of both vertical and horizontal settlements is also analyzed. It is demonstrated that the collapse thrust in case of horizontal settlement is a lower bound for the collapse thrust in case of generic settlement. Extensive numerical analyses illustrate the above results. [ABSTRACT FROM AUTHOR]

A large part of economies around the world rely on stock markets. To predict stock prices or commodities, econometric techniques are used. Analysts choose the suitable econometric technique according to error measures which may create confusion, especially in the case where there is no preference amongst error measures. Therefore, there is no unique score to rank econometric techniques based on multiple error measures. To bridge this gap, we propose, a MCDM like methodology [geometrical Benefit of Doubt (BoD)] that considers econometric techniques as alternatives and error measures as criteria. A real application with 194 econometric techniques and 8 error measures is presented. The efficiency scores derived from geometrical BoD model provide better discrimination and experiemental results indicate that ARMA and ARCH models are ranked higher. The proposed geometrical BoD model is compared to similar geometrical MCDM formulations. [ABSTRACT FROM AUTHOR]

Streamflow missing data rises to a real challenge for calibration and validation of hydrological models as well as for statistically based methods of streamflow prediction. Although several algorithms have been developed thus far to impute missing values of hydro(geo)logical time series, the effectiveness of methods in imputation when the time series are influenced by different seasonalities and variances have remained largely unexplored. Therefore, we evaluated the efficacy of five different statistical algorithms in imputation of streamflow and groundwater level missing data under variegated periodicities and variances. Our performance evaluation is based on the streamflow data, procured from a hydrological model, and the observed groundwater data from the federal state of Brandenburg in Northeast Germany. Our findings revealed that imputations methods embodying the time series nature of the data (i.e., preceding value, autoregressive integrated moving average (ARIMA), and autoregressive conditional heteroscedasticity model (ARCH)) resulted in MSEs (Mean Squared Error) that are between 20 and 40 times smaller than the MSEs obtained from the Ordinary least squares (OLS) regression, which do not consider this quality. ARCH and ARIMA excelled in imputing missing values for hydrological time series, specifically for the streamflow and groundwater level data. ARCH outperformed ARIMA in both the streamflow and groundwater imputation under various conditions, such as without seasonality, with seasonality, low and high variance, and high variance (white noise) conditions. For the streamflow data, ARCH achieved average MSEs of 0.0000704 and 0.0003487 and average NSEs of 0.9957710 and 0.9965222 under without seasonality and high variance conditions, respectively. Similarly, for the groundwater level data, ARCH demonstrated its capability with average MSEs of 0.000635040 and average NSEs of 0.9971351 under GWBR1 condition. The effectiveness of ARCH, originated from econometric time series methods, should be further assessed by other hydro(geo)logical time series obtained from different climate zones. [ABSTRACT FROM AUTHOR]

This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons. [ABSTRACT FROM AUTHOR]

Volatility forecasting is important in financial econometrics and is mainly based on the application of various GARCH-type models. However, it is difficult to choose a specific GARCH model that works uniformly well across datasets, and the traditional methods are unstable when dealing with highly volatile or short-sized datasets. The newly proposed normalizing and variance stabilizing (NoVaS) method is a more robust and accurate prediction technique that can help with such datasets. This model-free method was originally developed by taking advantage of an inverse transformation based on the frame of the ARCH model. In this study, we conduct extensive empirical and simulation analyses to investigate whether it provides higher-quality long-term volatility forecasting than standard GARCH models. Specifically, we found this advantage to be more prominent with short and volatile data. Next, we propose a variant of the NoVaS method that possesses a more complete form and generally outperforms the current state-of-the-art NoVaS method. The uniformly superior performance of NoVaS-type methods encourages their wide application in volatility forecasting. Our analyses also highlight the flexibility of the NoVaS idea that allows the exploration of other model structures to improve existing models or solve specific prediction problems. [ABSTRACT FROM AUTHOR]

The aims are to analyze the fluctuation forecast of exchange rate markets, including the Chinese Yuan (CNY), and discuss applying the neural network model in the Value at Risk (VaR) measure. Therefore, six exchange rate markets are selected as the research objects, with the CNY exchange rate market as the main body, to analyze and explain the exchange rate fluctuation risks. Second, based on the overview of the neural network model, the Deep Belief Network (DBN), Multilayer Perceptron (MLP), and Long Short-Term Memory Network (LSTM) are introduced, and a VaR method based on risk measurement is proposed. Finally, based on the number of excess days (Exc) and the Kupiec test, the VaR measure results under different models are analyzed. Results demonstrate that the CNY exchange rate market's historical data are relatively concentrated, with minor fluctuations, and the overall change is a sharp right shift. Compared with the benchmark model Generalized AutoRegressive Conditional Heteroskedasticity, the three neural network models show excellent risk measurement performance for different exchange rate markets. Based on Exc, the DBN model has the optimal risk forecast performance. In the CNY exchange rate market, the Exc values corresponding to the DBN and LSTM models are small, and the forecast performance is fair. Based on the Kupiec test, in addition to the Great Britain Pound exchange rate market, the three neural network models perform well in measuring the risks of the other five exchange rate markets. Besides, the MLP model has the optimal performance in measuring the CNY risks. Hence, the neural network models have excellent applicability in measuring the risks of exchange rate markets. [ABSTRACT FROM AUTHOR]

Statistical analysis and simulation of annual maximum discharge values, while considering the corresponding maximum daily rainfall, provide a comprehensive view of flood management. This research presents the application of copula functions for simulating and modeling two variables of annual maximum discharge and corresponding precipitation. In this research, the performance of copula-based models and ARCH-based models including VAR-GARCH, copula, and copula-GARCH models was then evaluated to simulate the annual maximum discharge values. The simulation results of all three models were evaluated using NSE and NRMSE statistics. According to the 95% confidence intervals, the accuracy of all three models was confirmed. The correlation results of the studied pair variables confirmed the possibility of using copula-based models. The results of simulations revealed that a higher accuracy of the copula-GARCH approach compared with two models copula and VAR-GARCH. Considering 76% efficiency (NSE = 0.76) of the copula-GARCH approach, the results indicated 20 and 2.7% improvements in the performance of the proposed approach compared to both VAR-GARCH and copula models. The results also illustrated that by combining nonlinear ARCH models with copula-based simulations, the reliability of simulation results increased. The results obtained in this study suggest that the proposed method is very effective for increasing the certainty of frequency analysis of two variables. Because the copula-GARCH approach simulates the average values, the first and third quarters, as well as the amplitude of changes of 5 and 95% of the data better than the other two models. Violin plot of AMD series in copula scale [ABSTRACT FROM AUTHOR]

This paper presents a capital asset pricing model-based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and 'abnormal trading volume'. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions. [ABSTRACT FROM AUTHOR]

One of the most important aspects of tunnelling in soft grounds is to reduce any possible adverse environmental impact. That is, pre-supporting techniques are found to boost tunnelling advance speed whilst, at the same time, keeping the environmental consequences due to tunnelling process appreciably low. The umbrella arch method is one of the most common ground pre-supporting techniques used in tunnelling. The interactions between the ground and umbrella arches are very complex. There are also several parameters affecting the performance of the umbrella arch technique. Sophisticated numerical modeling is, therefore, required to gain a proper understanding of the mechanism of ground movement based on the umbrella arch method. In this paper, the role of the pipe roofing and face bolting on the stabilization of the northern portal of Sabzkooh tunnel is assessed using three-dimensional (3D) numerical method. Furthermore, the effects of design parameters such as the pipes installation angle, the pipes diameter and the transverse spacing of pipes on the performance of umbrella arch system are investigated through numerical modeling. The vertical displacements of the tunnel crown and the internal forces induced in the initial support system are extracted from the numerical modeling and are carefully assessed to identify the role of the pre-supporting methods. The outcomes of the numerical modeling demonstrate that when pipe roofing and face bolts are utilized, the tunnel convergence decreases and the induced axial forces in the initial support system tend to increase. These clearly highlight the role of pre-supporting technique on stabilizing the tunnel and transferring ground loads to the support measure. Results also show that reduction in the installation angle and transverse spacing of pipes and increasing the diameter of pipes can all considerably reduce the tunnel crown displacement while they increase the maximum induced axial forces in the initial support system. Stability analysis using bearing capacity diagrams also indicates that the initial support system with different configurations of pipe roofing and face bolts at different cross sections along the tunnel alignment is fairly sturdy and robust against the applied loads. [ABSTRACT FROM AUTHOR]

A ground modification technique with rigid columns and ground beams for supporting an embankment over multilayered soft soils is introduced and investigated. A fully coupled three-dimensional finite element analysis was performed to examine the effect of beam inclusion into a piled embankment system. After validating the numerical model against field measurements, the time-dependent behavior of the innovative support system was further investigated with an isolated pile-supported embankment used as a control. A three-dimensional soil arch model is proposed for piles arranged in a square pattern with beam reinforcement. The kinematics of soil arching under staged construction of embankments and subsoil consolidation illustrates that the height of the outer boundary is generally lower with ground beams, suggesting that soil arching can be more easily mobilized in this configuration than in individual pile-supported embankments. When the tops of adjacent piles are connected with ground beams, load transfer to piles becomes more efficient, preventing the arch failure at the crown and punching shear failure at pile heads. Wider pile spacing leads to an increase in size and critical height of soil arches, while the fill over tops of beams may undergo larger plastic deformation. The inclusion of beams substantially reduces post-construction settlement and accelerates the dissipation of excess pore water pressure within soft soils. Performance of piled beam-supported embankment (PBSE) was ultimately evaluated by using two efficient comparison models. Under the same area replacement ratio conditions, PBSE significantly outperforms a geosynthetic-reinforced piled embankment with pile caps. Although PBSE is little less efficient than an embankment supported by slab–pile structure, the construction cost is substantially higher for the latter. A wider application of PBSE to high-speed rails is promising but it needs to be assessed against more field evidence. [ABSTRACT FROM AUTHOR]

Duration series often exhibit long-range dependence and local nonstationarities. Here, exponential FARIMA (EFARIMA) and exponential SEMIFAR (ESEMIFAR) models are introduced. These models capture simultaneously nonstationarities in the mean as well as short- and long-range dependence, while avoiding the complication of unobservable latent processes. The models can be thought of as locally stationary long-memory extensions of exponential ACD models. Statistical properties of the models are derived. In particular the long-memory parameter in the original and the log-transformed process is the same. For Gaussian innovations, exact explicit formulas for all moments and autocovariances are given, and the unconditional distribution is log-normal. Estimation and model selection can be carried out with standard software. The approach is illustrated by an application to average daily transaction durations. [ABSTRACT FROM AUTHOR]

The importance of cryptocurrency to the global economy is increasing steadily, which is evidenced by a total market capitalization of over $2.18T as of December 17, 2021, according to coinmarketcap.com (Coin, 2021). Cryptocurrencies are too confusing for laymen and require more investigation. In this study, we analyze the impact that the effective reproductive rate, an epidemiological indicator of the spread of COVID-19, has on both the price and trading volume of eight of the largest digital currencies—Bitcoin, Ethereum, Tether, Ripple, Litecoin, Bitcoin Cash, Cardano, and Binance. We hypothesize that as the rate of spread decreases, the trading price of the digital currency increases. Using Generalized Autoregressive Conditional Heteroskedasticity models, we find that the impact of the spread of COVID-19 on the price and trading volume of cryptocurrencies varies by currency and region. These findings offer novel insight into the cryptocurrency market and the impact that the viral spread of COVID-19 has on the value of the major cryptocurrencies. [ABSTRACT FROM AUTHOR]

This study examines the statistical properties required to model the dynamics of both the returns and volatility series of the daily stock market returns in six Gulf Cooperation Council countries, namely Bahrain, Oman, Kuwait, Qatar, Saudi Arabia, and the United Arab Emirates, under different financial and economic circumstances. The empirical investigation is conducted using daily data from June 1, 2005 to July 1, 2019. The analysis is conducted using a set of double long-memory specifications with some significant features such as long-range dependencies, asymmetries in conditional variances, non-linearity, and multiple seasonality or time-varying correlations. Our study indicates that the joint dual long-memory process can adequately estimate long-memory dynamics in returns and volatility. The in-sample diagnostic tests as well as out-of-sample forecasting results demonstrate the prevalence of the Autoregressive Fractionally Integrated Moving Average and Hyperbolic Asymmetric Power Autoregressive Conditional Heteroskedasticity modeling process over other competing models in fitting the first and the second conditional moments of the market returns. Moreover, the empirical results show that the proposed model offers an interesting framework to describe the long-range dependence in returns and seasonal persistence to shocks in conditional volatility and strongly support the estimation of dynamic returns that allow for time-varying correlations. A noteworthy finding is that the long-memory dependencies in the conditional variance processes of stock market returns appear important, asymmetric, and differ in their volatility responses to unexpected shocks. Our evidence suggests that these markets are not completely efficient in processing regional news, thus providing a sound alternative for regional portfolio diversification. [ABSTRACT FROM AUTHOR]

In this study, two efficient approaches for bivariate simulation are presented, which include meteorological and hydrological variables. For this purpose, the applicability of support vector regression (SVR) model optimized by Ant colony and Copula-GARCH (Generalized Autoregressive Conditional Heteroscedasticity) algorithms were investigated and compared in simulating the river discharge based on total monthly rainfall in Talezang Basin, Iran. Entropy theory was used to select a suitable meteorological station corresponding to a hydrometric station. The vector autoregressive model was also used as the base model in Copula-GARCH simulations. According to the 99% confidence intervals of the simulations, the accuracy of both models was confirmed. The simulation results showed that the Copula-GARCH model was more accurate than the optimized SVR (OSVR) model. Considering the 90% efficiency (NSE=0.90) of the Copula-GARCH approach, the results show a 36% improvement of RMSE statistics by the Copula-GARCH model compared to the OSVR model in simulating the river discharge on a monthly scale. The results also showed that by combining nonlinear ARCH models with the copula-based simulations, the reliability of the simulation results increases, which was also confirmed using the violin plot. The results also showed an increase in the accuracy of the Copula-GARCH model at the minimum and maximum values of the data. [ABSTRACT FROM AUTHOR]

The classical ARCH model together with its extensions have been widely applied in the modeling of financial time series. We study a variant of the ARCH model that takes account of liquidity given by a positive stationary process. We provide minimal assumptions that ensure the existence and uniqueness of the stationary solution for this model. Moreover, we give necessary and sufficient conditions for the existence of the autocovariance function. After that, we derive an AR(1) characterization for the stationary solution yielding Yule–Walker type quadratic equations for the model parameters. In order to define a proper estimation method for the model, we first show that the autocovariance estimators of the stationary solution are consistent under relatively mild assumptions. Consequently, we prove that the natural estimators arising out of the quadratic equations inherit consistency from the autocovariance estimators. Finally, we illustrate our results with several examples and a simulation study. [ABSTRACT FROM AUTHOR]

We establish consistency and asymptotic normality of the maximum likelihood estimator in the level-effect ARCH model of Chan et al. (J Financ 47(3):1209–1227, 1992). Furthermore, it is shown by simulations that the asymptotic properties also apply in finite samples. [ABSTRACT FROM AUTHOR]

The stability of an arching slope in deformable materials above strong rocks strongly depends on the shape and width of the span. Equations for a free surface problem that incorporate these two parameters were derived using a simplified two-dimensional arching slope model, and were validated using physical model tests under 1g and centrifugal conditions. The results are used to estimate the maximum excavation width for a weak claystone slope in a lignite mine, for which we calculate a safety factor of 1.31. [ABSTRACT FROM AUTHOR]

Real-world time series such as econometric time series are rarely linear and they have characteristics of volatility. Although autoregressive conditional heteroscedasticity models have used for forecasting financial time series, these models are specific models for time series, so they are not generally applied for all-time series. ARCH–GARCH models usually applied on financial time series. Because, since these time series include features like volatility clustering and leptokurtic and therefore cause problem of heteroscedastic. These problems can be handled thanks to these models. However, These model can be modelled by ARCH–GARCH models only if they include arch effect after being checked that whether ARCH effect exists or not. Therefore, in recent years artificial neural networks have been commonly used various fields by many researchers for any nonlinear-or linear time series, especially multiplicative neuron model-based artificial neural networks are commonly used that have successful forecasting results. It is known that hybrid methods in artificial neural networks are useful techniques for forecasting time series. In this study, a new hybrid forecasting method has a multiplicative neural network structure AR–ARCH–ANN model has been proposed. The proposed method is a recurrent model and also it can model volatility with having autoregressive conditional heteroscedasticity structure. In the proposed approach, particle swarm optimization is used for training neural network. Possibilities of avoiding local minimum traps are increased by this algorithm in using trained process. Istanbul Stock Exchange daily data sets from 2011 to 2013 and some time series in using for 2016 International Time Series Forecasting Competition are obtained to evaluate the forecasting performance of AR–ARCH–ANN. Then, results produced by the proposed method were compared with other methods and it has better performance from other methods. [ABSTRACT FROM AUTHOR]

The nonlinear dynamic behaviors of a double cable-stayed shallow arch model are investigated under the one-to-one-to-one internal resonance among the lowest modes of cables and the shallow arch and external primary resonance of cables. The in-plane governing equations of the system are obtained when the harmonic excitation is applied to cables. The excitation mechanism due to the angle-variation of cable tension during motion is newly introduced. Galerkin's method and the multi-scale method are used to obtain ordinary differential equations (ODEs) of the system and their modulation equations, respectively. Frequency- and force-response curves are used to explore dynamic behaviors of the system when harmonic excitations are symmetrically and asymmetrically applied to cables. More importantly, comparisons of frequency-response curves of the system obtained by two types of trial functions, namely, a common sine function and an exact piecewise function, of the shallow arch in Galerkin's integration are conducted. The analysis shows that the two results have a slight difference; however, they both have sufficient accuracy to solve the proposed dynamic system. [ABSTRACT FROM AUTHOR]

ARIMA-GARCH models are used in the analysis of financial series with time-varying conditional variance. A calibrated model of underlying asset returns allows computing all derivatives of the original money flow. The article describes an ARIMA-GARCH model of the underlying asset returns, the forms of ARIMA- and GARCH-components, and the corresponding stationarity conditions. A survey of the results on option pricing by ARIMA-GARCH and GARCH models of underlying asset returns is presented. [ABSTRACT FROM AUTHOR]

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model. [ABSTRACT FROM AUTHOR]

Tests for error autocorrelation (AC) are derived under the assumption of independent and identically distributed errors. The tests are not asymptotically valid if the errors are conditionally heteroskedastic. In this article we propose wild bootstrap (WB) Lagrange multiplier tests for error AC in vector autoregressive (VAR) models. We show that the WB tests are asymptotically valid under conditional heteroskedasticity of unknown form. WB tests based on a version of the heteroskedasticity-consistent covariance matrix estimator are found to have the smallest error in rejection probability under the null and high power under the alternative. We apply the tests to VAR models for credit default swap prices and Euribor interest rates. An important result that we find is that the WB tests lead to parsimonious models while the asymptotic tests suggest that a long lag length is required to get white noise residuals. [ABSTRACT FROM AUTHOR]

The persistence property of inflation is an important issue not only for economists, but especially for central banks, given that the degree of inflation persistence determines the extent to which central banks can control inflation. Further, not only is it the level of inflation persistence that is important in economic analyses, but also the question of whether the persistence varies over time, for instance, across business cycle phases, is equally pertinent, since assuming constant persistence across states of the economy is sure to lead to misguided policy decisions. Against this backdrop, we extend the literature on long-memory models of inflation persistence for the US economy over the monthly period of 1920:1-2014:5, by developing an autoregressive fractionally integrated moving-average-generalized autoregressive conditional heteroskedastic model with a time-varying memory coefficient which varies across expansions and recessions. In sum, we find that inflation persistence does vary across recessions and expansions, with it being significantly higher in the former than in the latter. As an aside, we also show that persistence of inflation volatility is higher during expansions than in recessions. Understandably, our results have important policy implications. [ABSTRACT FROM AUTHOR]

By considering a deformable geodetic network, deforming in a linear-in-time mode, according to a coordinate-invariant model, it becomes possible to get an insight into the rank deficiency of the stacking procedure, which is the standard method for estimating initial station coordinates and constant velocities, from coordinate time series. Comparing any two out of the infinitely many least squares estimates of stacking unknowns (initial station coordinates, velocity components and transformation parameters for the reference system in each data epoch), it is proven that the two solutions differ only by a linear-in-time trend in the transformation parameters. These pass over to the initial coordinates (the constant term) and to the velocity estimates (the time coefficient part). While the difference in initial coordinates is equivalent to a change of the reference system at the initial epoch, the differences in velocity components do not comply with those predicted by the same change of reference system for all epochs. Consequently, the different velocity component estimates, obtained by introducing different sets of minimal constraints, correspond to physically different station velocities, which are therefore non-estimable quantities. The theoretical findings are numerically verified for a global, a regional and a local network, by obtaining solutions based on four different types of minimal constraints, three usual algebraic ones (inner or partial inner) and the lately introduced kinematic constraints. Finally, by resorting to the basic ideas of Felix Tisserand, it is explained why the station velocities are non-estimable quantities in a very natural way. The problem of the optimal choice of minimal constraints and, hence, of the corresponding spatio-temporal reference system is shortly discussed. [ABSTRACT FROM AUTHOR]