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The FDIC Improvement Act of 1991 sets out five categories of capital and mandates corrective action for banks. Each bank based on its capital amount fall in the certain categories or states. The purpose of this paper is to consider the effect of banking regulations and supervisory practices on capital state transition.

First, the authors investigate how much the practices influence banks' capital adequacy using a dynamic panel data method, the generalized method of moments. Then, to scrutinize the results of the first phase, the authors estimate the effect of practices on some characteristics of capital state transition such as transition intensity, transition probability and state sojourn time using multi-state models for panel data in 107 developing countries over the period 2000 to 2012.

The dynamic regression results show that capital guidelines, supervisory power and supervisory structure can have significantly positive effects on the capital adequacy state. Moreover, the multi-state Markov panel data model estimation results show that the significantly positive-effect practices can change the capital state transition intensity considerably; for example, they can transmit the critical-under-capitalized (the lowest) capital state of banks directly to a well or the adequate-capitalized (the highest) capital state without passing through middle states (under-capitalized and significantly-undercapitalized). Moreover, the results present some new evidence on transition probability and state sojourn time.

The main contribution of this paper, unlike the existing literature, is to consider the power of banking regulations and supervisory practices to improve the capital state using a multi-state Markov panel data model.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.

The purpose of this paper is to evaluate the reliability and signature reliability of solar panel k-out-of-n-multiplex system with the help of universal generating function.

Energy scarcity and global warming issues have become important concerns for humanity in recent decades. To solve these problems, various nations work for renewable energy sources (RESs), including sun, breeze, geothermal, wave, radioactive and biofuels. Solar energy is absorbed by solar panels, referred to as photovoltaic panels, which then transform it into electricity that can be used to power buildings or residences. Remote places can be supplied with electricity using these panels. Solar energy is often generated using a solar panel that is connected to an inverter for power supply. As a result, a converter reliability evaluation is frequently required. This paper presents a study on the reliability analysis of k-out-of-n systems with heterogeneous components. In this research, the universal generating function methodology is used to identify the reliability function and signature reliability of the solar array components. This method is commonly used to assess the tail signature and Barlow-Proschan index with independent and identically distributed components.

The Barlow-Proschan index, tail signature, signature, expected lifetime, expected cost and minimal signature of independent identically distributed are all computed.

This is the first study of solar panel k-out-of-n-multiplex systems to examine the signature reliability with the help of universal generating function techniques with various measures.

The equity premium of the S&P 500 index is explained in this paper by several variables that can be grouped into fundamental, behavioral, and macroeconomic factors. We hypothesize that the statistical significance of these variables changes across economic regimes. The three regimes we consider are the low‐volatility, medium‐volatility, and high‐volatility regimes in contrast to previous studies that do not differentiate across economic regimes. By using the three‐state Markov switching regime econometric methodology, we confirm that the statistical significance of the independent variables representing fundamentals, macroeconomic conditions, and a behavioral variable changes across economic regimes. Our findings offer an improved understanding of what moves the equity premium across economic regimes than what we can learn from single‐equation estimation. Our results also confirm the significance of momentum as a behavioral variable across all economic regimes

We model transitions between unemployment, low-paid and high-paid employment by British men using a first order Markov model with endogenous switching that also takes into account the endogeneity of initial conditions, selection into employment, and sample attrition. Our estimates indicate that all three selectivity issues are non-ignorable. We demonstrate several interrelationships between the dynamics of (un)employment and low-paid work between one year and the next, represented by forms of (cross-)state dependence. Controlling for heterogeneity, the probability of a man having a low-paid job in one year depends not only whether he had a job a year before but also whether that job was low paid. The probability of his being employed at all depends on whether he had a job the previous year.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

The purpose of this paper is to develop a new approach for equipment states prediction and provide a method for early warning of possible trouble states.

A new two-stage multi-level equipment state classification system was proposed to forecast equipment operation status. The first stage involves predicting the equipment's normal state, and the second stage involves forecasting the equipment's abnormal status. Meanwhile, the equipment state classification is done according to the manufacturing company's internal specifications to define various equipment statuses. Then, the trouble state and waiting state were predicted by grey state prediction model.

A new two-stage multi-level equipment status classification system and a new approach for equipment states prediction has been proposed in this paper.

The application on a real-world case shown that the model is very effective for predicting equipment state. The equipment's major failure risk can be reduced significantly.

The proposed approach can help improve the effective prediction of the equipment's various operation states and reduce the equipment's major failure risk and thus maintenance costs.

The purpose of this paper is to design a ring network topology system and alter it into a series–parallel type framework. Then, reliability of the framework is analysed and authors also discussed the signature to analyse the most sensitive component.

This study presents a ring-shaped network system where this type of topology forms a single continuous pathway for signals through every node. In this study, a system consists of ring network topology is generalized in the series–parallel mixed configuration and reliability characteristics are evaluated with the assistance of universal generating function (UGF) technique. The system consists of wires, repeaters, components or computers and power supply. The wires and repeaters are in series, so, if they fail the whole system will fail and the signals will not go further. The components or computers are connected to each other in parallel configuration. So, the whole system will not fail until the last computer is working. There is also a two-unit power supply system which has one unit in a standby mode. On the failure of first power supply, the second one will start functioning and the whole system fails on the failure of both power supply.

By the assistance of UGF technique, reliability function of the framework is evaluated. Also, Barlow–Proschan index and expected lifetime for the designed system is estimated by considering a numerical example for the general ring-shaped network system.

UGF technique is very effective for estimating the reliability of a system with complex structure and having two performance rates, i.e. completely failed and perfectly working, or more than two, i.e. multi-state performance. This technique enables to estimate every components contribution in the working and failure of the whole system. A general model of ring-shaped network system is taken and generalized algorithm is drawn for the system. Then a particular numerical example is solved for illustrating the use of this technique.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

The purpose of this paper is to bridge the gap between the existing theoretical and empirical studies by examining the asymmetric return–volume relationship. Indeed, the authors aim to shed light on the return–volume linkages for French-listed small and medium-sized enterprises (SMEs) compared to blue chips across different market regimes.

This study includes both large capitalizations included in the CAC 40 index and listed SMEs included in the Euronext Growth All Share index. The Markov-switching (MS) approach is applied to understand the asymmetric relationship between trading volume and stock returns. The study investigates also the causal impact between stock returns and trading volume using regime-dependent Granger causality tests.

Asymmetric contemporaneous and lagged relationships between stock returns and trading volume are found for both large capitalizations and listed SMEs. However, the causality investigation reveals some differences between large capitalizations and SMEs. Indeed, causal relationships depend on market conditions and the size of the market.

This paper explains the asymmetric return–volume relationship for both large capitalizations and listed SMEs by incorporating several psychological biases, such as the disposition effect, investor overconfidence and self-attribution bias. Future research needs to deepen the analysis especially for SMEs as most of the literature focuses on large capitalizations.

This empirical study has fundamental implications for portfolio management. The findings provide a deeper understanding of how trading activity impact current returns and vice versa. The authors’ results constitute an important input to build and control trading strategies.

This paper fills the literature gap on the asymmetric return–volume relationship across different regimes. To the best of the authors’ knowledge, the present study is the first empirical attempt to test the asymmetric return–volume relationship for listed SMEs by using an accurate MS framework.