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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.

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.

The purpose of this paper is to elaborate the picture of the motivators for information seeking by comparing two cognitive psychological approaches to motivation: self-determination theory (SDT) and expectancy-value theories (EVTs).

The study draws on the conceptual analysis of 31 key investigations characterizing the nature of the above theories. Their potential is examined in light of an illustrative example of seeking information about job opportunities.

SDT approaches motivation by examining the degree to which one can make volitional choices while meeting the needs of autonomy and competence. Information-seeking behaviour is most volitional when it is driven by intrinsic motivation, while such behaviours driven by extrinsic motivation and amotivation are less volitional. Modern EVTs approach the motivators for information seeking by examining the individual’s beliefs related to intrinsic enjoyment, attainment value, utility value and relative cost of information seeking. Both theories provide useful alternatives to traditional concepts such as information need in the study of the motivators for information seeking.

As the study focusses on two cognitive psychological theories, the findings cannot be generalised to all represent all categories relevant to the characterisation of triggers and drivers of information seeking.

Drawing on the comparison of two cognitive psychological theories, the study goes beyond the traditional research approaches of information behaviour research confined to the analysis of information needs.

The purpose of this paper is to use filtered extreme‐value theory (EVT) model to forecast one of the main emerging market stock returns and compare the predictive performance of this model with other conditional volatility models.

This paper employs eight filtered EVT models created with conditional quantile to estimate value‐at‐risk (VaR) for the Istanbul Stock Exchange. The performances of the filtered EVT models are compared to those of generalized autoregressive conditional heteroskedasticity (GARCH), GARCH with student‐t distribution, GARCH with skewed student‐t distribution, and FIGARCH by using alternative back‐testing algorithms, namely, Kupiec test, Christoffersen test, Lopez test, Diebold and Mariano test, root mean squared error (RMSE), and h‐step ahead forecasting RMSE.

The results indicate that filtered EVT performs better in terms of capturing fat‐tails in stock returns than parametric VaR models. An increase in the conditional quantile decreases h‐step ahead number of exceptions and this shows that filtered EVT with higher conditional quantile such as 40 days should be used for forward looking forecasting.

The research results show that emerging market stock return should be forecasted with filtered EVT and conditional quantile days lag length should also be estimated based on forecasting performance.

This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as the variance‐covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long‐term VaR forecasts are desired. Extreme Value Theory (EVT) is proposed to overcome these problems. The application of EVT is illustrated by an example from the German hog market. Multi‐period VaR forecasts derived by EVT are found to deviate considerably from standard forecasts. We conclude that EVT is a useful complement to traditional VaR methods.

Application of financial risk models in the emerging markets poses special challenges. A fundamental challenge is to accurately model the return distributions which are particularly fat tailed and skewed. Value-at-Risk (VaR) measures based on the Extreme Value Theory (EVT) have been suggested, but typically data histories are limited, making it hard to test and apply EVT. The chapter addresses issues in (i) modeling the VaR measure in the presence of structural breaks in an economy, (ii) the choice of stable innovation distribution with volatility clustering effects, (iii) modeling the tails of the empirical distribution, and (iv) fixing the cut-off point for isolating extreme observations. Pakistan offers an instructive case since its equity market exhibits high volatility and incidence of extreme returns. The recent Global Financial Crisis has been another source of extreme returns. The confluence of the two sources of volatility provides us with a rich data set to test the VaR/EVT model rigorously and examine practical challenges in its application in an emerging market.

Small- and medium-sized enterprises can be highly affected by losses caused by exchange rate changes. The aim of this paper was to find the optimal Value-at-Risk (VaR) method for estimating future exchange rate losses within one year.

The analysis focuses on five VaR methods, some of them traditional and some of them more up to date with integrated EVT or GARCH. The analysis of VaR methods was concentrated on a time horizon (1–12 months), overestimation predictions and six scenarios based on trends and variability of exchange rates. This study used three currency pairs EUR/CZK, EUR/USD and EUR/JPY for backtesting.

In compliance with the backtesting results, the parametric VaR with random walk has been chosen, despite its shortcomings, as the most accurate for estimating future losses in a medium-term period. The Nonparametric VaR confirmed insensitivity to the current exchange rate development. The EVT-based methods showed overconservatism (overestimation predictions). Every parametric or semiparametric method revealed a severe increase of liberality with increasing time.

This research is limited to the analysis of suitable VaR models in a long- and short-run period without using artificial intelligence.

The result of this paper is the choice of a proper VaR method for the online application for estimating the future exchange rate for enterprises.

The orientation of medium-term period makes the research original and useful for small- and medium-sized enterprises.

Extreme value theory (EVT) holds promise for advancing the assessment and management of extreme financial risks. Recent literature suggests that the application of EVT generally results in more precise estimates of extreme quantiles and tail probabilities of financial asset returns. This article assesses EVT from the perspective of financial risk management. The authors believe that the recent optimism regarding EVT may be appropriate but exaggerated, and that much of its potential remains latent. They support their claim by describing various pitfalls associated with the current use of EVT techniques, and illustrate how these can be avoided. In conclusion, the article defines several specific research directions that may further the practical and effective application of EVT to risk management.

This paper investigates the dependence structure and market risk of the currency exchange rate portfolio from the Malaysian ringgit perspective.

The marginal return of the five major exchange rates series, i.e. United States dollar (USD), Japanese yen (JPY), Singapore dollar (SGD), Thai baht (THB) and Chinese Yuan Renminbi (CNY) are modelled by the Bayesian generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model with Student's t innovations. In addition, five different copulas, such as Gumbel, Clayton, Frank, Gaussian and Student's t, are applied for modelling the joint distribution for examining the dependence structure of the five currencies. Moreover, the portfolio risk is measured by Value at Risk (VaR) that considers the extreme events through the extreme value theory (EVT).

The finding shows that Gumbel and Student's t are the best-fitted Archimedean and elliptical copulas, for the five currencies. The dependence structure is asymmetric and heavy tailed.

The findings of this paper have important implications for diversification decision and hedging problems for investors who involving in foreign currencies. The authors found that the portfolio is diversified with the consideration of extreme events. Therefore, investors who are holding an individual currency with VaR higher than the portfolio may consider adding other currencies used in this paper for hedging.

This is the first paper estimating VaR of a currency exchange rate portfolio using a combination of Bayesian GARCH model, EVT and copula theory. Moreover, the VaR of the currency exchange rate portfolio can be used as a benchmark of the currency exchange market risk.

The purpose of this paper is to investigate and compare the extreme behavior of securitized real estate and stock market returns as well as their value‐at‐risk (VaR) dynamics in international investing. Extreme value theory using the block maxima method is applied to ten securitized real estate and equity market indices representing Asian, European and North American markets.

The paper models the maxima and minima of all return series within the extreme value theory (EVT) framework and derive the VaR estimates. It then compares the VaR estimates derived from the EVT and the normal distribution and investigates the impact of clustered returns on the VaR estimates. Finally, both the conventional standard deviation measure and VaR method are conducted to evaluate and compare the impact of the Asian financial turmoil on the real estate and stock market risk profiles.

Evidence shows that Asian real estate and equity maxima and minima return series are characterized by a fat‐tailed Fréchet distribution. The frequency and severity of extreme Asian real estate returns are greater than their European and North American counterparts. Securitized real estate markets are riskier than the broader stock markets before and during the Asian financial turmoil. In contrast, many stock markets become riskier after the financial crisis with their VaRs higher than the equivalent VaR estimates for the real estate series.

Knowledge about real estate market returns exhibit extreme behavior can help investors and fund managers understand the distribution of real estate market returns better and obtain potentially more accurate real estate return forecasts.

International real estate portfolio risk management should include both extreme risks and standard deviations. Accordingly, global investors should be even more cautious in formulating their diversification strategies since gains from diversification can be reduced significantly by the severity of extreme return levels.

The paper characterizes the distribution of extreme returns for a broad spectrum of international securitized real estate markets from three continents. The extreme value investigation is also conducted for broader stock markets corresponding to the individual real estate markets. The July 1997 turmoil that occurred in Asian financial markets provides interesting exploratory opportunities within which this paper estimates and compares the extreme market risk with the conventional standard deviation measure.