Search results

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.

This study aims to investigate the variation in overvaluation proxies and volatility across industry sectors and time.

Using industry sector data from the S&P Capital IQ database, this study applies traditional cross-sectional regressions to investigate the relationship between overvaluation and volatility over the 2001–2020 time period.

This study finds that the most volatile industry sectors generally do not coincide with overvalued industry sectors in the cross-section, implying that there are limitations to price-multiple methods for forecasting future volatility. Rather, this study finds that historical volatility significantly increases the goodness-of-fit when modeling volatility in the cross section of industry sectors. The findings of this study imply that firms should increase disclosures and transparency about corporate practices to decrease downside risk that stems from bad news. In addition, the findings underline the consistency between market efficiency and high levels of volatility in periods of significant uncertainty.

This study proposes a novel approach to examining the cross section of volatility across time for industry sectors.

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.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

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.

Section 529 college savings plans are tax-favored investment vehicles, which saw tremendous growth after the Economic Growth and Tax Relief Reconciliation Act of 2001 expanded 529 plan benefits to include tax-free distributions for qualified higher education expenses. However, regulators, the press, and fund advisors criticized the Section 529 college savings plan industry for inadequate and nonuniform disclosures of investor information, such as historical returns, fees, taxes, and underlying investments. We investigate consumers’ investment choices after a disclosure regime change in 2003 and find that after enhanced disclosures became widely available, investors selected fewer plans offered exclusively through brokers, increasingly chose portfolios based on past investment performance, but remained unresponsive to state tax benefit disclosures. We also analyze the plans’ performance and find evidence that 529 investors are constrained to invest in portfolios with high, return-eroding fees. Nearly 20 percent of the portfolios have a statistically significant negative alpha, the measure of risk-adjusted excess return, while less than 1 percent have a statistically significant positive alpha.

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.