Artículo de revistaFinancial stability is aimed at preventing and mitigating systemic risk, which is largely associated to the tail risk of macrofinancial variables. In this context, policy makers need to consider not only the most likely (central tendency) future path of macrofinancial variables, but also the distribution of all possible outcomes about that path, and focus on the downside risk. Against this background, the so-called at-risk methods provide a useful framework for the assessment of financial stability by the recognition of non-linear effects on the distribution of macrofinancial variables. We describe the use of quantile regressions for this purpose and illustrate two empirical applications related to the house prices and th...
In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo expe...
In this study we consider the risk estimation as a stochastic process based on the Sample Quantile P...
In the recent years, quantile regression methods have attracted relevant interest in the statistical...
Artículo de revistaFinancial stability is aimed at preventing and mitigating systemic risk, which is...
This study conducts an empirical analysis on how the build-up of systemic risk in the financial syst...
This paper provides evidence that tails in the distribution of macroeconomic forecasts are time-vary...
Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power...
We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quanti...
With the development of the times and the progress of science and technology, the financial market i...
Recently, Bayesian solutions to the quantile regression problem, via the likeli-hood of a Skewed-Lap...
Recent financial crises have placed an increased accent on methods dealing with risk management. Des...
In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo expe...
This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeli...
A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this ...
The financial turmoil has aroused the need for risk management tools. Value at Risk (VaR) has been u...
In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo expe...
In this study we consider the risk estimation as a stochastic process based on the Sample Quantile P...
In the recent years, quantile regression methods have attracted relevant interest in the statistical...
Artículo de revistaFinancial stability is aimed at preventing and mitigating systemic risk, which is...
This study conducts an empirical analysis on how the build-up of systemic risk in the financial syst...
This paper provides evidence that tails in the distribution of macroeconomic forecasts are time-vary...
Quantile regression is applied in two retail credit risk assessment exercises exemplifying the power...
We evaluate the role of financial conditions as predictors of macroeconomic risk first in the quanti...
With the development of the times and the progress of science and technology, the financial market i...
Recently, Bayesian solutions to the quantile regression problem, via the likeli-hood of a Skewed-Lap...
Recent financial crises have placed an increased accent on methods dealing with risk management. Des...
In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo expe...
This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeli...
A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this ...
The financial turmoil has aroused the need for risk management tools. Value at Risk (VaR) has been u...
In this paper, we compare four different Value-at-Risk (V aR) methodologies through Monte Carlo expe...
In this study we consider the risk estimation as a stochastic process based on the Sample Quantile P...
In the recent years, quantile regression methods have attracted relevant interest in the statistical...