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We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...
Persistent link: https://www.econbiz.de/10003891679
This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the exible quantile regression framework and rely on recently developed...
Persistent link: https://www.econbiz.de/10010407475
This paper mainly focuses on the correlation between live hedge funds return and their value at risk (VaR), which is based on the historical data from May 2000 to April 2010. The authors adopt portfolio level analyses and fund level cross-sectional regression, and find that there is significant...
Persistent link: https://www.econbiz.de/10013137801
The catastrophic failures of risk management systems in 2008 bring to the forefront the need for accurate and flexible estimators of market risk. Despite advances in the theory and practice of evaluating risk, existing measures are notoriously poor predictors of loss in high-quantile events. To...
Persistent link: https://www.econbiz.de/10013100621
We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...
Persistent link: https://www.econbiz.de/10013155427
Financial experts assume that measures the risk of financial asset returns generally have a normal distribution. Reality often shows asset returns are not normally distributed, so that the constraints and make it difficult to estimate the risk of taking the measurements. For it is necessary to...
Persistent link: https://www.econbiz.de/10013056260
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for...
Persistent link: https://www.econbiz.de/10011722173
Investors are becoming more sensitive about returns and losses, especially when the investments are exposed to downside risk potential in the financial markets. Despite the computational intensity of the downside risk measures, they are very widely applied to construct a portfolio and evaluate...
Persistent link: https://www.econbiz.de/10013462061
Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility forecasting accuracy. Additionally, we explore the...
Persistent link: https://www.econbiz.de/10012910114
In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...
Persistent link: https://www.econbiz.de/10012127861