Macro Stress Testing of Credit Risk Focused on the Tails

This paper investigates macro stress testing of system-wide credit risk with special focus on the tails of the credit risk distributions conditional on bad macroeconomic scenarios. These tails determine the ex-post solvency probabilities derived from the scenarios. This paper estimates the macro-credit risk link by the traditional Wilson (1997) model as well as by an alternative proposed quantile regression (QR) method (Koenker and Xiao, 2002), in which the relative importance of the macro variables can vary along the credit risk distribution, conceptually incorporating uncertainty in default correlations. Stress-testing exercises on the Brazilian household sector at the one-quarter horizon indicate that unemployment rate distress produces the most harmful effect, whereas distressed inflation and distressed interest rate show higher impacts at longer periods. Determining which of the two stress-testing approaches perceives the scenarios more severely depends on the type of comparison employed. The QR approach is revealed more conservative based on a suggested comparison of vertical distances between the tails of the conditional and unconditional credit risk cumulative distributions.

Evaluating Asset Pricing Models in a Fama-French Framework

In this work we propose a methodology to compare different stochastic discount factor (SDF) proxies based on relevant market information. The starting point is the work of Fama and French, which evidenced that the asset returns of the U.S. economy could be explained by relative factors linked to characteristics of the firms. In this sense, we construct a Monte Carlo simulation to generate a set of returns perfectly compatible with the Fama and French factors and, then, investigate the performance of different SDF proxies. Some goodness-of-fit statistics and the Hansen and Jagannathan distance are used to compare asset pricing models. An empirical application of our setup is also provided.

An Econometric Cntribution to the Intertemporal Approach of the Current Account

This paper investigates an intertemporal optimization model to analyze the current account through Campbell & Shiller’s (1987) approach. In this setup, a Wald test is conducted to analyze a set of restrictions imposed to a VAR, used to forecast the current account for a set of countries. We focused here on three estimation procedures: OLS, SUR and the two-way error decomposition of Fuller & Battese (1974). We also propose an original note on Granger causality, which is a necessary condition to perform the Wald test. Theoretical results show that, in the presence of global shocks, OLS and SUR estimators might lead to a biased covariance matrix, with serious implications to the validation of the model. A small Monte Carlo simulation confirms these findings and indicates the Fuller & Battese procedure in the presence of global shocks. An empirical exercise for the G-7 countries is also provided, and the results of the Wald test substantially change with different estimation techniques. In addition, global shocks can account up to 40% of the total residuals of the G-7.

Stochastic simulation of a DSGE model for Brazil

In this paper we investigate the Smets & Wouters (2003a) DSGE model for Brazil, through a numerical simulation based on the Dynare code (developed by Cepremap). Impulse Response functions are presented and a Bayesian estimation is also conducted from the prior distributions of the parameters.Bayesian estimation; Dynare; DSGE model

Evaluating Value-at-Risk models via Quantile Regression

This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series

Evaluating Value-at-Risk models via Quantile Regression

This paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables, such as whether or not there was an exception, sacrifices too much information. However, most of the specification tests (also called backtests) available in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not rely solely on binary variables. It is shown that the new backtest provides a sufficient condition to assess the finite sample performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theoretical findings are corroborated through a Monte Carlo simulation and an empirical exercise with daily S&P500 time series.Value-at-Risk, Backtesting, Quantile Regression

An essay on the foreign exchange rate expectations in Brazil

This article analyses the behaviour of the brazilian exchange rate (Real/US dollar) and the corresponding values forecasted by the market agents, from 2001 (november) to 2004 (may). We use the data-base of the Brazilian Central Bank, called “Sistema de Expectativas de Mercado”, which has been created in 1999. We evaluate the rational expectations hypothesis (REH) for the exchange rate market, comparing the mean value predicted by some brazilian financial institutions with the daily exchange rate that has really occurred (PTAX). The particular arrangement of the data-base allows us to make the analysis in two different ways: with fixed-event forecasts and also with “rolling-event” forecasts. The main result suggests that the brazilian exchange rate market support the weak form of the REH, for short horizons of forecasting.taxa de câmbio; volatilidade; expectativas racionais

Stochastic simulation of a DSGE model for Brazil

In this paper we investigate the Smets & Wouters (2003a) DSGE model for Brazil, through a numerical simulation based on the Dynare code (developed by Cepremap). Impulse Response functions are presented and a Bayesian estimation is also conducted from the prior distributions of the parameters

Um ensaio sobre expectativas da taxa de câmbio no Brasil

This article analyses the behaviour of the brazilian exchange rate (Real/US dollar) and the corresponding values forecasted by the market agents, from 2001 (november) to 2004 (may). We use the data-base of the Brazilian Central Bank, called “Sistema de Expectativas de Mercado”, which has been created in 1999. We evaluate the rational expectations hypothesis (REH) for the exchange rate market, comparing the mean value predicted by some brazilian financial institutions with the daily exchange rate that has really occurred (PTAX). The particular arrangement of the data-base allows us to make the analysis in two different ways: with fixed-event forecasts and also with “rolling-event” forecasts. The main result suggests that the brazilian exchange rate market support the weak form of the REH, for short horizons of forecasting