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In order to avoid enterprise crisis and cause the domino effect, which influences the investment return of investors, the national economy, and financial crisis, establishing a complete set of feasible financial early warning model can help to prevent the possibility of enterprise crisis in advance, and thus, reduce the influence on society and the economy. The purpose of this paper is to develop an efficient financial crisis warning model.

First, the fruit fly optimization algorithm (FOA) is used to adjust the coefficients of the parameters in the ZSCORE model (we call it the FOA_ZSCORE model), and the difference between the forecasted value and the real target value is calculated. Afterward, the generalized regressive neural network (GRNN model), with optimized spread by FOA (we call it FOA_GRNN model), is used to forecast the difference to promote the forecasting accuracy. Various models, including ZSCORE, FOA_ZSCORE, FOA_ZSCORE+GRNN, and FOA_ZSCORE+FOA_GRNN, are trained and tested. Finally, different models are compared based on their prediction accuracies and ROC curves. Furthermore, more appropriate parameters, which are different from the parameters in the original ZSCORE model, are selected by using the multivariate adaptive regression splines (MARS) method.

The hybrid model of the FOA_ZSCORE together with the FOA_GRNN offers the highest prediction accuracy, compared to other models; the MARS can be used to select more appropriate parameters to further improve the performance of the prediction models.

This paper proposes a hybrid model, FOA_ZSCORE+FOA_GRNN which offers better performance than the original ZSCORE model.

Bankruptcy is the last phase of economic life of companies and has some impacts on all of the entity’s stakeholders. Thus, the prediction of bankruptcy is very important. The inherent aim of preparing and developing financial accounting information is to provide a basis for economic decision-making, and any decision requires information acquisition, processing and data analysis as well as logical and correct interpretation of information. Developing models for predicting financial crisis and comparing the capabilities of existing models can help to alert management about ongoing activities and investors about economic decision for purchase shares or granting loan facilities to companies. So, the purpose of this study is the predict bankruptcy of listed companies on the Tehran Stock Exchange.

From the statistical methods’ perspective, the present research is classified as modeling and with respect to research methodology, it is a correlative-descriptive study in which the relationship between variables is analyzed based on the research objective. Predictive variables are the best ratios of cost of goods sold, non-operating revenues, net sales, predicted earnings per share (EPS) and real EPS.

Prediction of corporate bankruptcy crisis is one of the vital research areas. Predictive models are means for estimating the company’s future situation. Investors and creditors are highly willing to predict the bankruptcy crisis because the high costs associated with bankruptcy crisis will spoil the economy as a whole. On the other hand, this raises concerns among owners, and they are always seeking to find ways to preserve their capital through prediction of stocks continuing operations in the future. Having knowledge about bankruptcy or non-bankruptcy of automotive parts companies makes it possible to recognize weaknesses and strengths in the companies’ current performance and to make investment decisions.

Development of financial markets and, subsequently, creation of fierce competition has resulted in bankruptcy of many companies. Investors are always looking for predicting possible bankruptcy of a firm to prevent their investments risks because bankruptcy costs are high for investors, creditors, lenders and government agencies. Hence, they are seeking ways to estimate corporate bankruptcy. For this reason, over the past four decades, bankruptcy prediction has been enumerated as a key issue in companies and consequently because of its importance, many studies have been conducted to achieve the best model to predict bankruptcy.

Bankruptcy forecast is an economically important issue in every organization and company. Financial and accounting researchers are trying to offer financial models using various combinations of financial ratios with better measuring ability for performance and dividends payments as well as company continued activities. Bankruptcy prediction models are among financial analysis techniques in which the purpose of financial analysis and bankruptcy forecasting is recognition of efficiency and management executive performances. Moreover, the analysis of stock value by shareholder is another application of such research results. Basically, shareholders are interested in knowing the future status of the companies that are going to buy. In this way, shareholders use this method of analysis to estimate future activity or inactivity of firms.

The current work studies the cause, process, and effects of financial reform in 10 countries in Eastern Asia for the period of 1993–2002, especially focusing upon comparisons between pre- and post-Asia financial crisis. This study utilizes Mann–Whitney U test and Intervention Analysis to explore the different effects of the changes of GDP, stock index, exchange rate, CPI index, and the changes of the unemployment rate before and after the Asia financial crisis. It shows the consistent relationship between stock index, exchange rate, CPI index, and the changes of unemployment rate.

The purpose of this paper is to apply alternative GARCH‐type models to daily volatility forecasting, and apply Value‐at‐Risk (VaR) to the Taiwanese stock index futures markets that suffered most from the global financial tsunami that occurred during 2008.

Rather than using squared returns as a proxy for true volatility, this study adopts three range‐based proxies (PK, GK and RS), and one return‐based proxy (realized volatility), for use in the empirical exercise. The forecast evaluation is conducted using various proxy measures based on both symmetric and asymmetric loss functions, while back‐testing and two utility‐based loss functions are employed for further VaR assessment with respect to risk management practice.

Empirical results demonstrate that the EGARCH model provides the most accurate daily volatility forecasts, while the performances of the standard GARCH model and the GARCH models with highly persistent and long‐memory characteristics are relatively poor. In the area of risk management, the RV‐VaR model tends to underestimate VaR and has been rejected owing to a lack of correct unconditional coverage. In contrast, the GARCH genre of models can provide satisfactory and reliable daily VaR forecasts.

The unobservable volatility can be proxied using parsimonious daily price range with freely available prices when applied to Taiwanese futures markets. Meanwhile, the GARCH‐type models remain valid downside risk measures for both regulators and firms in the face of a turbulent market.

This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

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).

The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

The purpose of this paper is to examine two different approaches in the prediction of the economic recession periods in the US economy.

A logit regression was applied and the prediction performance in two out‐of‐sample periods, 2007‐2009 and 2010 was examined. On the other hand, feed‐forwards neural networks with Levenberg‐Marquardt error backpropagation algorithm were applied and then neural networks self‐organizing map (SOM) on the training outputs was estimated.

The paper presents the cluster results from SOM training in order to find the patterns of economic recessions and expansions. It is concluded that logit model forecasts the current financial crisis period at 75 percent accuracy, but logit model is useful as it provides a warning signal three quarters before the current financial crisis started officially. Also, it is estimated that the financial crisis, even if it reached its peak in 2009, the economic recession will be continued in 2010 too. Furthermore, the patterns generated by SOM neural networks show various possible versions with one common characteristic, that financial crisis is not over in 2009 and the economic recession will be continued in the USA even up to 2011‐2012, if government does not apply direct drastic measures.

Both logistic regression (logit) and SOMs procedures are useful. The first one is useful to examine the significance and the magnitude of each variable, while the second one is useful for clustering and identifying patterns in economic recessions and expansions.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.