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Because of the growing financial market integration, China’s stock market’s volatility spillover effect has gradually increased. Traditional strategies do not capture stock volatility in dependence and dynamic conditions. Therefore, this study aims to find an effective stochastic model to predict the volatility spillover effect in the dynamic stock markets.

To assess the time-varying dynamics and volatility spillover, this study has used an integrated approach of dynamic conditional correlation model, copula and extreme-value theory. A daily log-returns of three leading indices of Pakistan Stock Exchange (PSX) and Shanghai Stock Exchange (SSE) from the period of 2009 to 2019 is used in the modeling of value-at-risk (VaR) for volatility estimation. The Student’s t copula has been selected based on maximum likelihood estimation and Akaike’s information criteria values of all the copulas using the goodness-of-fit test.

The model results show stronger dependency between all major portfolios of PSX and SSE, with the parametric value of 0.98. Subsequently, the results of dependence structure positively estimate the spillover effect of SSE over PSX. Furthermore, the back-testing results show that the VaR model performs well at 99% and 95% levels of confidence and gives more accurate estimates upon the maximum level of confidence.

This study is helpful for the investment managers to manage the risk associated to portfolios under dependence conditions. Moreover, this study is also helpful for the researchers in the field of financial risk management who are trying to improve the returns by addressing the issues of volatility estimations.

This study contributes to the body of knowledge by providing a practical model to manage the volatility spillover effect in dependence conditions between as well as across the financial markets.

Financial asset return series usually exhibit nonnormal characteristics such as high peaks, heavy tails and asymmetry. Traditional risk measures like standard deviation or variance are inadequate for nonnormal distributions. Value at Risk (VaR) is consistent with people's psychological perception of risk. The asymmetric Laplace distribution (ALD) captures the heavy-tailed and biased features of the distribution. VaR is therefore used as a risk measure to explore the problem of VaR-based asset pricing. Assuming returns obey ALD, the study explores the impact of high peaks, heavy tails and asymmetric features of financial asset return data on asset pricing.

A VaR-based capital asset pricing model (CAPM) was constructed under the ALD that follows the logic of the classical CAPM and derive the corresponding VaR-β coefficients under ALD.

ALD-based VaR exhibits a minor tail risk than VaR under normal distribution as the mean increases. The theoretical derivation yields a more complex capital asset pricing formula involving β coefficients compared to the traditional CAPM.The empirical analysis shows that the CAPM under ALD can reflect the β-return relationship, and the results are robust. Finally, comparing the two CAPMs reveals that the β coefficients derived in this paper are smaller than those in the traditional CAPM in 69–80% of cases.

The paper uses VaR as a risk measure for financial time series data following ALD to explore asset pricing problems. The findings complement existing literature on the effects of high peaks, heavy tails and asymmetry on asset pricing, providing valuable insights for investors, policymakers and regulators.

WHEN considering a flat plate of finite width at a low angle of attack, it is observed that, downstream from the plate, where the air flow from the suction and pressure sides join, a transverse velocity appears. On the pressure side there is an outward flow toward the ends of the plate, while on the suction surface the component is inward. Since all streamlines have the same origin, in the case of steady flow the pressure where the flow over upper and lower surfaces joins must be the same and no velocity ‘jump’ is possible. It follows then that any velocity change must be purely transverse and that this will extend to the ends of the plate. The same phenomenon occurs with wings of finite span and the vortices are formed continually in flight. These vortices absorb energy, for which power has to be supplied continuously—in other words the induced drag caused solely by the vortex formation appears.

The authors examine the dependence structure of the BRICS exchange rates.

The authors construct a regular vine copula model to study the co-movements of exchange rates in BRICS controlling the influences from the SDR currencies and the oil prices.

The main findings show that, after the financial crisis, RMB pursued a more balanced strategy shifting from USD-centered to USD-EUR dependency and the oil prices become more dependent on RUB than USD, which could weaken the dollar hegemony. From robustness tests, we find that the inclusion of RMB in SDR has certain but limited impacts on the dependence structure and the influence of the GBP weakened as well. The results have important implications for currency trade, policy design and the future of the BRICS.

The contribution of this paper is twofold. First, we examine the interdependence structure of the BRICS exchange rates controlling for the influence of SDR currencies and the oil prices with R-Vine copula model. Second, we compare the pre- and after-crisis structure and see if the financial crisis and the BRICS summits have changed the structure.

The purpose of this paper is to establish a paint deposition pattern model applied to robotic air spray painting in order to achieve the accuracy and uniformity of paint film thickness on free‐form surface.

The paper opts for an exploratory study using the curvature circle method for air spray painting on free‐form surface to construct a spray gun model. First, a paint deposition pattern model of ellipse dual‐β distribution is fitted on the basic of experimental data from robotic air spray painting. Second, a spray gun model is proposed using the curvature circle method for air spray painting on free‐form surface. The theoretical result is coincident with the film thickness in verification experiment spraying a cylinder surface. The biggest error of the sample points between the theoretical and experimental results is less than 4 μm, thereby the correctness and effectiveness of the proposed model is validated.

The paper provides a specific theoretical and methodological support for the realization of process planning and simulation system in surface spray manufacturing. It will make the future developed system meet the actual processing requirement. At the same time, it is more representative.

The paper finds an approach to solve paint deposition pattern model suitable to free‐form surface. The present method can be applied to the complex reality of topological relation for actual workpiece surface to be painted.

The purpose of this paper is the reliability analysis for systems with dependent gamma degradation process and Weibull failure time.

Consider a life testing experiment in which a sample of n devices starts to operate at t=0 and the data are available on failure time and failure-evolving process on each individual, called in some contents wear or degradation. Ignoring the between performance characteristics dependency structure may lead us to different reliability estimations, while the dependency justly exists. In previous research, dependency between the degradation process and hard failure time has been studied in limited detail (special closed form expression). Thereafter, the dependency between two degradation processes with the same structure (gamma process) in a system is considered using the copula function.

The results indicate that ignoring the dependency structure may lead us to different reliability estimations while the dependency justly exists.

This study gives some contributions that evaluate reliability metrics with more than one failure mechanism that may not be independent and possibly follow a different distribution function. The authors have used the copula function as a basis to develop a proposal model and analysis methods. In addition, the authors discussed the identifiability of the copula. Finally, simulation data were used to review the suggested approach.

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Incremental value at risk (IVaR) is becoming a standard tool to identify investment strategies that enhance risk‐adjusted returns. Recently, practice‐oriented research has focused applying IVaR to hedging and speculating with options and risk reduction. IVaR approximation methods provide easily applied preliminary guidelines for risk allocation. This article examines two such approaches.

IS there anything magic about the shape of a wing section? Asked to sketch the profile of a wing on the back of an envelope, one would have no difficulty in representing a shape which would probably, for most purposes, be adequate. Assuming this generalization to be true—perhaps it is a rather rash one—one might equally well question the need for an article on aerofoil design, or indeed the need for the long and painstaking research which, over the years, has been conducted on this particular subject. But it is this same research which, in the long run, has resulted in the recognition of certain general rules relating to aerofoil geometry, which are now taken so much for granted that they would probably be embodied in one's preconceived notion of what a wing section should look like. Recently, also, rather complicated theoretical techniques have made possible the design of profiles which, if manufactured faithfully and carefully in each detail, can provide a performance which is considerably better than any more arbitrary shaping to general rules would produce. Finally, of course, one must recognize that there are exceptional conditions where the application of conventional ideas is inadvisable, and where theoretical and experimental research is needed to suggest what is more appropriate. This article will be concerned for the most part with amplifying these remarks; but, by and large, it must be admitted at the outset that we cannot point to any revolutionary discontinuities in the progress of aerofoil design such as have characterized advances in the means of aircraft propulsion, or structural design.

The objective of inventory management models is to determine efficient policies for managing the trade-off between customer satisfaction and the cost of goods. This chapter presents a methodology that uses the Monte Carlo Method (MCM) to estimate the behavior of a raw material supply model, considering uncertain variables such as demand, prices, and exchange rates. In order to show how to use this methodology, we analyze the case of a Colombian company in the aluminum industry. This company imports aluminum sheets from China. In this case, we analyze the financial impact of the raw material supply contract proposed by the Chinese supplier. The model considers different supply scenarios for the raw material. We calculate robust indicators such as Value at Risk (VaR), the Conditional Value at Risk (CVaR) and the probability of success for each scenario analyzed. Finally, we conduct a sensitivity analysis with respect to the sales price to validate the proposed models and solution approaches. The results show that considering risk metrics to evaluate the impact of endogenous factors over the supply process is a useful approach to improve decision-making related to this process and also can help to ensure the profitability of the company.