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The purpose of this paper is to focus on the performance of three alternative value‐at‐risk (VaR) models to provide suitable estimates for measuring and forecasting market risk. The data sample consists of five international developed and emerging stock market indices over the time period from 2004 to 2008. The main research question is related to the performance of widely‐accepted and simplified approaches to estimate VaR before and after the financial crisis.

VaR is estimated using daily data from the UK (FTSE 100), Germany (DAX30), the USA (S&P500), Turkey (ISE National 100) and Greece (GRAGENL). Methods adopted to calculate VaR are: EWMA of Riskmetrics; classic GARCH(1,1) model of conditional variance assuming a conditional normally distributed returns; and asymmetric GARCH with skewed Student‐t distributed standardized innovations.

The paper provides evidence that the tools of quantitative finance may achieve their objective. The results indicate that the widely accepted and simplified ARCH framework seems to provide satisfactory forecasts of VaR, not only for the pre‐2008 period of the financial crisis but also for the period of high volatility of stock market returns. Thus, the blame for financial crisis should not be cast upon quantitative techniques, used to measure and forecast market risk, alone.

Knowledge of modern risk management techniques is required to resolve the next financial crisis. The next crisis can be avoided only when financial risk managers acquire the necessary quantitative skills to measure uncertainty and understand risk.

The main contribution of this paper is that it provides evidence that widely accepted/used methods give reliable VaR estimates and forecasts for periods of financial turbulence (financial crises).

Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one‐day‐ahead value‐at‐risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions.

The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance, and power transformation of conditional variance.

Based on back‐testing measures and a loss function evaluation method, finds that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions.

Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns.

The behavior of the risk management techniques is examined for both long and short VaR trading positions; to the best of one's knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, a two‐stage model selection is implemented in contrast with the most commonly used back‐testing procedures to identify a unique model. Finally, parametric, nonparametric, and semiparametric techniques are employed to investigate their performance in a unified environment.

The purpose of this paper is to study the sensitivity of provident fund investors to past performance and how market conditions, changes in risk and liquidity levels influence the net flows into provident funds by using a unique sample from Israel.

The study checks the impact of different levels of fund performance on provident fund flows using three alternative proxies for performance: raw return and the risk adjusted returns based on the Sharpe ratio and the Jensen’s α. The analysis relies on the time fixed effect and fund fixed effect regression models.

Results reveal that there exists an approximately concave flow–performance relationship and performance persistence among Israeli provident funds. Israeli provident fund investors are risk averse so they overreact to bad performance both in bull and bear markets. Moreover, liquidity is an important factor to influence the flow–performance curve. The investors’ strong negative response to poor performance and relative insensitivity to outperformance show that provident fund managers are not rewarded for their risk-shifting activities as in mutual funds.

The authors explore the behavior of investor flows in non-institutional retirement savings funds specifically outside of the USA, which is a topic not properly investigated in literature. Moreover, examining inflows and outflows separately gives the authors a richer understanding of investors in pension schemes. This study also enhances the understanding of the impact of fund liquidity on the flow–performance relationship for the retirement funds segment.

This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY.

By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation.

In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure.

To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

This paper seeks to propose a conceptual framework to promote value‐enhanced collaborative working (VECW) for a small management advisory firm.

The research methodology was qualitative. It involved a comprehensive review of literature leading to a better understanding of collaborative working requirements in a small firm context and the barriers to derive value from collaborative working. Initially, exploratory observations helped to identify issues of collaborative working in the case study organisation. Following the exploratory observations, a detailed case study was undertaken. The emphasis was on semi‐structured interviews under a guiding questionnaire along with field observations to produce a conceptual framework for VECW.

The research enables an understanding of the apparent failure of a particular small and medium‐sized enterprise (SME) management advisory firm (case study organisation) to derive value from its collaborative working model. The study identifies major issues that affect the long‐term relationships of the organisation with their collaborating stakeholders and recognises the common understanding required by the stakeholders working in collaboration. Further, in order to solve these issues, the research develops a conceptual VECW framework within the current context. The major components of the conceptual VECW framework consist of process, people and tools factors to give a structured approach to agree common goals, share risks and rewards, provide faster and clearer communications and information transparency between collaborating stakeholders.

Owing to the nature of the study (case study), current paper is based on the findings of a single SME management advisory firm. Hence, further research for organisations of similar size and providing similar services would be required to investigate the robustness of the approach.

Most studies on collaboration are concentrated on either larger organisations or product organisations. Here, the attempt is to understand the collaboration among small firms providing professional services. The research paper is an important milestone on an ongoing research to produce a detailed framework eventually to be presented to the industry for evaluation to ensure its contribution to the industry as well as increasing the knowledge on the subject.

While previous studies have primarily assumed dysfunctional effects of cultural distance in joint ventures and M&A, this paper elucidates from a positive organizational scholarship perspective how perceived cultural distance can advance firms’ new product development within non-equity alliances. The purpose of this paper is to explain how perceived cultural distance stimulates task discourse that supports alliance partners’ employees in recognizing and applying culture-related differences as complementary problem-solving potentials. Due to a lower integration level in non-equity alliances compared to joint ventures or M&A, this paper assumes that the positive effects outweigh the negative effects of cultural distance.

This study applies structural equation modeling to test the hypothesized effects on a sample of 246 international alliances in the manufacturing industry.

The analysis mainly supports the hypothesized model and unravels how positive effects can emerge from perceived cultural distance.

The findings provide managerial implications. Alliance managers should note that cultural distance can have positive and negative effects, and thus it is not a barrier per se in alliances. Firms can benefit from cultural distance if they are able to leverage culture-specific complementarities through task discourse among partners in alliances.

The manuscript uses a unique data set of 246 international alliances from the global manufacturing industry. The manuscript has not been published elsewhere.

The Basel Committee regulations require the estimation of value-at-risk (VaR) at 99 percent confidence level for a ten-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real estate portfolio returns. The purpose of the paper is to estimate accurate ten-day-ahead 99%VaR forecasts for real estate markets along with stock markets for seven countries across the world (the USA, the UK, Germany, Japan, Australia, Hong Kong and Singapore) following the Basel Committee requirements for financial regulation.

A 14-dimensional multivariate Diag-VECH model for seven equity indices and their relative real estate indices is estimated. The authors evaluate the VaR forecasts over a period of two weeks in calendar time, or ten-trading-days, and at 99 percent confidence level based on the Basle Committee on Banking Supervision requirements.

The Basel regulations require ten-day-ahead 99%VaR forecasts. This is the first study that provides successful evidence for ten-day-ahead 99%VaR estimations for real estate markets. Additionally, the authors provide evidence that there is a statistically significant relationship between the magnitude of the ten-day-ahead 99%VaR and the level of dynamic correlation for real estate and stock market indices; a valuable recommendation for risk managers who forecast risk across markets.

Risk managers, investors and financial institutions require dynamic multi-period VaR forecasts that will take into account properties of financial time series. Such accurate dynamic forecasts lead to successful decisions for controlling market risks.

This paper is the first approach which models simultaneously the volatility and VaR estimates for real estate and stock markets from the USA, Europe and Asia-Pacific over a period of more than 20 years. Additionally, the local correlation between stock and real estate indices has statistically significant explanatory power in estimating the ten-day-ahead 99%VaR.

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.

Employees often expect an emphasis on financial aspects to be predominant when their employers choose a fund management company for the investment of employees’ pension fund deposits. By contrast, in an attempt to appear as socially responsible company managers may emphasize social responsibility (SR) in pension fund choices. The purpose of this paper is to examine to what extent managers for small- and medium-sized companies emphasize SR vs expected returns when choosing investment managers for their employees’ pension funds.

A conjoint experiment among 276 Norwegian SMEs’ decision makers examines their trade-offs between social and financial goals in their choice of employees’ pension management. Furthermore, the study examines how the companies’ decision makers’ characteristics influence their pension fund management choices.

The findings show that the employers placed the greatest weight to suppliers providing funds adhering to socially responsible investment (SRI) practices, followed by the suppliers’ corporate brand credibility, the funds’ expected return, and the suppliers’ management fees. Second, employers with investment expertise emphasized expected returns and downplayed SR in their choice, whereas employers with stated CSR-strategies downplayed expected return and emphasized SR.

Choice of supplier to manage employees’ pension funds relates to a general discussion on whether companies should do well – maximizing value, or do good, – maximizing corporate SR. In this study, doing well means maximizing expected returns and minimizing costs of the pension investments, whereas doing good means emphasizing SRI in this choice. Unfortunately, the employees might pay a price for their companies’ ethicality as moral considerations may conflict with maximizing the employees’ pension fund value.

The U.S. textile industry has gone through much upheaval in the past two decades. As protective barriers are gradually phased out the industry is faced with stiff foreign competition. Regional trade pacts, such as NAFTA and CBI, on the other hand help to improve the competitiveness of the domestic textile industry. This paper looks at the trends in U.S. textile trade with the various trading zones and the various factors influencing textile imports and exports. We examine the impact of the new global environment, the regional trade pacts, NAFTA and CBI on the changing nature and pattern of trade. The overall trends indicate a significant decline in imports from the EU countries, Asia remains significant, but NAFTA and CBI countries are quickly gaining ground over the old trading partners. The OECD remains the most significant destination for U.S. textile exports followed by NAFTA and Latin American countries.