Search results

The authors propose an alternative robust technique to test for discontinuities in distributions and provide consistent evidence of discontinuities around zero for both scaled and unscaled earnings levels and changes. The advantage of the proposed test is that it does not rely on arbitrary choice of bin width choices.

To evaluate the power of the test, the authors examine the density function of non-discretionary earnings and detect no evidence of discontinuities around zero in levels and changes of these non-discretionary earnings. As robustness, the authors use pre-managed earnings excluding accrual and real manipulation and find similar evidence.

The finding using our technique support the Burgstahler and Dichev (1997) interpretation on earnings management, even for smaller sample sizes and reject the theory that discontinuities arise from scaling and sampling methods.

The study provides an overview of those studies that support and those that oppose using “testing for discontinuities” as a way to examine earnings management. The authors advance the literature by providing an alternative methodology supporting the view that the kink in the distribution represents earnings management.

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.

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 aim is to evaluate the performance of symmetric and asymmetric ARCH models in forecasting both the one‐day‐ahead Value‐at‐Risk (VaR) and the realized intra‐day volatility of two equity indices in the Athens Stock Exchange.

Two volatility specifications are estimated, the symmetric generalized autoregressive conditional heteroscedasticity (GARCH) and the asymmetric APARCH processes. The data set consisted of daily closing prices of the General and the Bank indices from 25 April 1994 to 19 December 2003 and their intra day quotation data from 8 May 2002 to 19 December 2003.

Under the VaR framework, the most appropriate method for the Bank index is the symmetric model with normally distributed innovations, while the asymmetric model with asymmetric conditional distribution applies for the General index. On the other hand, the asymmetric model tracks closer the one‐step‐ahead intra‐day realized volatility with conditional normally distributed innovations for the Bank index but with asymmetric and leptokurtic distributed innovations for the General index.

As concerns the Greek stock market, there are adequate methods in predicting market risk but it does not seem to be a specific model that is the most accurate for all the forecasting tasks.

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.