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This paper aims to present a low-cost, edge-fed, windmill-shaped, notch-band eliminator, circular monopole antenna which is practically loaded with a complementary split ring resonator (CSRR) in the middle of the radiating conductor and also uses a partial ground to obtain wide-band performance.

To compensate for the reduced value of gain and reflection coefficient because of the full (complete) ground plane at the bottom of the substrate, the antenna is further loaded with a partial ground and a CSRR. The reduction in the length of ground near the feed line improves the impedance bandwidth, and introduced CSRR results in improved gain with an additional resonance spike. This results in a peak gain 3.895dBi at the designed frequency 2.45 GHz. The extending of three arms in the circular patch not only led to an increase of peak gain by 4.044dBi but also eliminated the notch band and improved the fractional bandwidth 1.65–2.92 GHz.

The work reports a –10dB bandwidth from 1.63 GHz to 2.91 GHz, which covers traditional coverage applications and new specific uses applications such as narrow LTE bands for future internet of things (NB-IoT) machine-to-machine communications 1.8/1.9/2.1/2.3/2.5/2.6 GHz, industry, automation and business-critical cases (2.1/2.3/2.6 GHz), industrial, society and medical applications such as Wi-MAX (3.5 GHz), Wi-Fi3 (2.45 GHz), GSM (1.9 GHz), public safety band, Bluetooth (2.40–2.485 GHz), Zigbee (2.40–2.48Ghz), industrial scientific medical (ISM) band (2.4–2.5 GHz), WCDMA (1.9, 2.1 GHz), 3 G (2.1 GHz), 4 G LTE (2.1–2.5 GHz) and other personal communication services applications. The estimated RLC electrical equivalent circuit is also presented at the end.

Because of full coverage of Bluetooth, Zigbee, WiFi3 and ISM band, the proposed fabricated antenna is suitable for low power, low data rate and wireless/wired short-range IoT-enabled medical applications.

The antenna is fabricated on a piece (66.4 mm × 66.4 mm × 1.6 mm) of low-cost low profile FR-4 epoxy substrate (0.54 λ_g × 0.54 λ_g) with a dielectric constant of 4.4, a loss tangent of 0.02 and a thickness of 1.6 mm. The antenna reflection coefficient, impedance and VSWR are tested on the Keysight technology (N9917A) vector network analyzer, and the radiation pattern is measured in an anechoic chamber.

This paper aims to present the design development and measurement of two aerodynamic slotted X-bands back-to-back planer substrate-integrated rectangular waveguide (SIRWG/SIW) to Microstrip (MS) line transition for satellite and RADAR applications. It facilitates the realization of nonplanar (waveguide-based) circuits into planar form for easy integration with other planar (microstrip) devices, circuits and systems. This paper describes the design of a SIW to microstrip transition. The transition is broadband covering the frequency range of 8–12 GHz. The design and interconnection of microwave components like filters, power dividers, resonators, satellite dishes, sensors, transmitters and transponders are further aided by these transitions. A common planar interconnect is designed with better reflection coefficient/return loss (RL) (S₁₁/S₂₂ ≤ 10 dB), transmission coefficient/insertion loss (IL) (S₁₂/S₂₁: 0–3.0 dB) and ultra-wideband bandwidth on low profile FR-4 substrate for X-band and Ku-band functioning to interconnect modern era MIC/MMIC circuits, components and devices.

Two series of metal via (6 via/row) have been used so that all surface current and electric field vectors are confined within the metallic via-wall in SIW length. Introduced aerodynamic slots in tapered portions achieve excellent impedance matching and tapered junctions with SIW are mitered for fine tuning to achieve minimum reflections and improved transmissions at X-band center frequency.

Using this method, the measured IL and RLs are found in concord with simulated results in full X-band (8.22–12.4 GHz). RLC T-equivalent and p-equivalent electrical circuits of the proposed design are presented at the end.

The measurement of the prototype has been carried out by an available low-cost X-band microwave bench and with a Keysight E4416A power meter in the microwave laboratory.

The transition is fabricated on FR-4 substrate with compact size 14 mm × 21.35 mm × 1.6 mm and hence economical with IL lie within limits 0.6–1 dB and RL is lower than −10 dB in bandwidth 7.05–17.10 GHz. Because of such outstanding fractional bandwidth (FBW: 100.5%), the transition could also be useful for Ku-band with IL close to 1.6 dB.

This study aims to implement a novel approach of using the Realized generalized autoregressive conditional heteroskedasticity (GARCH) model within the conditional extreme value theory (EVT) framework to generate quantile forecasts. The Realized GARCH-EVT models are estimated with different realized volatility measures. The forecasting ability of the Realized GARCH-EVT models is compared with that of the standard GARCH-EVT models.

One-step-ahead forecasts of Value-at-Risk (VaR) and expected shortfall (ES) for five European stock indices, using different two-stage GARCH-EVT models, are generated. The forecasting ability of the standard GARCH-EVT model and the asymmetric exponential GARCH (EGARCH)-EVT model is compared with that of the Realized GARCH-EVT model. Additionally, five realized volatility measures are used to test whether the choice of realized volatility measure affects the forecasting performance of the Realized GARCH-EVT model.

In terms of the out-of-sample comparisons, the Realized GARCH-EVT models generally outperform the standard GARCH-EVT and EGARCH-EVT models. However, the choice of the realized estimator does not affect the forecasting ability of the Realized GARCH-EVT model.

It is one of the earliest implementations of the two-stage Realized GARCH-EVT model for generating quantile forecasts. To the best of the authors’ knowledge, this is the first study that compares the performance of different realized estimators within Realized GARCH-EVT framework. In the context of high-frequency data-based forecasting studies, a sample period of around 11 years is reasonably large. More importantly, the data set has a cross-sectional dimension with multiple European stock indices, whereas most of the earlier studies are based on the US market.

The purpose of this paper is to identify various factors influencing additive manufacturing (AM) implementation from operational performance in the Indian manufacturing sector and to establish the hierarchical relationship among them.

The methodology includes three phases, namely, identification of factors through systematic literature review (SLR), interviews with experts to capture industry perspective of AM implementation factors and to develop the hierarchical model and classify it by deriving the interrelationship between the factors using interpretive structural modeling (ISM), followed with the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis.

This research has identified 14 key factors that influence the successful AM implementation in the Indian manufacturing sector. Based on the analysis, top management commitment is an essential factor with high driving power, which exaggerates other factors. Factors, namely, manufacturing flexibility, operational excellence and firm competitiveness are placed at the top level of the model, which indicates that they have less driving power and organizations need to focus on those factors after implementing the bottom-level factors.

Additional factors may be considered, which are important for AM implementation from different industry contexts. The variations from different industry contexts and geographical locations can foster the theoretical robustness of the model.

The proposed ISM model sets the directions for business managers in planning the operational strategies for addressing AM implementation issues in the Indian manufacturing sector. Also, competitive strategies may be framed by organizations based on the driving and dependence power of AM implementation factors.

This paper contributes by identification of AM implementation factors based on in-depth literature review as per SLR methodology and validation of these factors from a variety of industries and developing hierarchical model by integrative ISM-MICMAC approach.

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

This paper aims to analyze the characteristics of stochastic volatility processes in globally listed private equity (LPE) markets, which are represented by nine global, regional and style indices, and reveals transmissions in the conditional variances between the different markets, based on weekly data covering the period January 2011 to December 2020.

The study uses the generalized autoregressive conditional heteroscedasticity [GARCH(p, q)] model and its exponential GARCH (EGARCH) and GARCH-in-mean extensions.

The estimates of the volatility models GARCH, EGARCH and GARCH-in-mean GARCH-M for testing the stylized properties persistence, asymmetry, mean reversion and risk premium lead to very different results, depending on the respective LPE index.

The knowledge of conditional volatilities of LPE returns as well as the detection of volatility transmissions between the different LPE markets under investigation serve to support asset allocation decisions with respect to risk management or portfolio allocation. Hence, the findings are important for all kinds of investors and asset managers who consider investments in LPE.

The authors present a novel study that examines the conditional variance for globally LPE markets by using LPX indices, offering valuable insight into this growing asset class.

This study aims to analyse the conditional volatility of the Vietnam Index (Ho Chi Minh City) and the Hanoi Exchange Index (Hanoi) with a specific focus on their application to risk management tools such as Expected Shortfall (ES).

First, the author tests both indices for long memory in their returns and squared returns. Second, the author applies several generalised autoregressive conditional heteroskedasticity (GARCH) models to account for asymmetry and long memory effects in conditional volatility. Finally, the author back tests the GARCH models’ forecasts for Value-at-Risk (VaR) and ES.

The author does not find long memory in returns, but does find long memory in the squared returns. The results suggest differences in both indices for the asymmetric impact of negative and positive news on volatility and the persistence of shocks (long memory). Long memory models perform best when estimating risk measures for both series.

Short-time horizons to estimate the variance should be avoided. A combination of long memory GARCH models with skewed Student’s t-distribution is recommended to forecast VaR and ES.

Up to now, no analysis has examined asymmetry and long memory effects jointly. Moreover, studies on Vietnamese stock market volatility do not take ES into consideration. This study attempts to overcome this gap. The author contributes by offering more insight into the Vietnamese stock market properties and shows the necessity of considering ES in risk management. The findings of this study are important to domestic and foreign practitioners, particularly for risk management, as well as banks and researchers investigating international markets.

The paper aims to investigate the determinants of China’s daily intervention in the foreign exchange market since the 2005 reform aimed at moving the Renminbi (RMB) exchange rate regime towards greater flexibility.

The paper uses bivariate probit models to test whether China’s intervention decision is driven by three sets of factors, comprising Model I (basic model), Model II and Model III.

Evidence from the models suggests that medium-term Chinese interventions tend to be leaning-against-the-wind, whereas long-term interventions are leaning-with-the-wind. Furthermore, by analyzing exchange rate volatility, this paper finds that intervention is used by the Chinese central bank to ensure that there are no big swings in the RMB exchange rate.

The paper will be of value to other researchers attempting to understand the policy of the central bank and, in particular, the factors that can lead to interventions during periods of financial crisis.

The purpose of this paper is to examine the extent to which Indian consumers of different demographic groups vary in terms of shopping mall visits (frequency of visit, hours spent in the mall, and number of shops visited) and purchase behaviour (total money spent, number of shops purchased from and number of items purchased).

The study used a self-administered survey of 400 Indian mall shoppers to examine Indian shoppers’ behaviour with respect to visiting and buying behaviour. Descriptive analyses and χ² tests were conducted to identify patterns and capture the significant relationships in shopping behaviour across different demographic segments.

The results show that shoppers of different age cohorts and from different household sizes behave differently from one another in a significant manner. In terms of gender, however, men and women tend to behave in a similar manner in terms of visit frequency, time and money spent per visit. The study also provides insight into where the differences occur and between which specific groups.

Data comes from one major city of India which limits the generalizability of the results.

For mall managers and retailers, the study findings indicate that the stores that serve recreational needs should focus more on younger segments, where men and women share similar buying patterns. Findings from this study could also be used for segmentation exercises and to build strategies to convert footfall into actual purchase, especially within the rapidly growing Indian mall market.

The study adds value to the body of retail literature and provides empirical evidence from the rapidly developing Indian market. The study also provides insight into where differences occur and between which specific groups. By highlighting the differences in greater detail, the study benefits retailers in general and specifically, mall managers.

The purpose of this paper is to compare the daily conditional variance forecasts of seven GARCH-family models. This paper investigates whether the advanced GARCH models outperform the standard GARCH model in forecasting the variance of stock indices.

Using the daily price observations of 21 stock indices of the world, this paper forecasts one-step-ahead conditional variance with each forecasting model, for the period 1 January 2000 to 30 November 2013. The forecasts are then compared using multiple statistical tests.

It is found that the standard GARCH model outperforms the more advanced GARCH models, and provides the best one-step-ahead forecasts of the daily conditional variance. The results are robust to the choice of performance evaluation criteria, different market conditions and the data-snooping bias.

This study addresses the data-snooping problem by using an extensive cross-sectional data set and the superior predictive ability test (Hansen, 2005). Moreover, it covers a sample period of 13 years, which is relatively long for the volatility forecasting studies. It is one of the earliest attempts to examine the impact of market conditions on the forecasting performance of GARCH models. This study allows for a rich choice of parameterization in the GARCH models, and it uses a wide range of performance evaluation criteria, including statistical loss functions and the Mince-Zarnowitz regressions (Mincer and Zarnowitz 1969). Therefore, the results are more robust and widely applicable as compared to the earlier studies.