Simple Hazard Model Research Articles

Review Paper on Composite Leading Index Creation for Forecasting the Bangladeshi Financial Sector

In perspective of the economic vulnerability faced by banks in financial sector, this study mirrors the methodology used by Shumway (2001) – the dynamic hazard model that is able to forecast systemic risk in financial market arena. Here, the terminology followed is based on the CAMELS framework variables: capital adequacy, asset, management, earnings, liquidity and sensitivity to market risk. The objective of this study is to construct a macroprudential indicator (MPI) for the case of Bangladeshi financial market. The result will then be tested for robustness with macro-stress test. Lagged independent variables will be used in the simple hazard model to allow early prediction of MPI in the year in which the crisis happens. The empirical findings can be used as a guideline for the Bangladesh Government and policy makers in accessing, examining and forecasting the health of the Bangladeshi financial system and formulate suitable financial system policies for control. MPI generates information about systemic risk allowing the detection of potential economic crises functioning as an early warning indicator. Government and policy makers will be able to make early preparation in cushioning any potential crises by means of the MPI. Thus the impact of the crises could be minimized and eventually reduce its impact on the Bangladesh economy. The specific objectives are to assemble a novel MPI that is able to recommend early signals of financial market vulnerability, to identify the MPI turning points and establish a comprehensive reference chronology for Bangladeshi financial market and to evaluate the predictive performance of newly constructed MPI on characterizing Bangladeshi financial sector.

Forecasting forward defaults: a simple hazard model with competing risks

A forward default prediction method based on the discrete-time competing risk hazard model (DCRHM) is proposed. The proposed model is developed from the discrete-time hazard model (DHM) by replacing the binary response data in DHM with the multinomial response data, and thus allowing the firms exiting public markets for different causes to have different effects on forward default prediction. We show that DCRHM is a reliable and efficient model for forward default prediction through maximum likelihood analysis. We use actual panel data-sets to illustrate the proposed methodology. Using an expanding rolling window approach, our empirical results statistically confirm that DCRHM has better and more robust out-of-sample performance than DHM, in the sense of yielding more accurate predicted number of forward defaults. Thus, DCRHM is a useful alternative for studying forward default losses on portfolios.

Bank distress prediction: Empirical evidence from the Gulf Cooperation Council countries

In this paper we apply a simple hazard model to develop an early warning system of bank distress in the Gulf Cooperation Council countries. Specifically, we identify a set of leading indicators of bank distress that are used subsequently to predict the probability of bank failure in these countries. The investigation covers a wide set of bank level variables and other variables including the influence of bank management, competition, diversification, ownership and regulation. Similar to the previous empirical evidence, we found that good management lowers the likelihood of distress. Moreover, competition and diversification were found to be bad for the health of banks. This result is consistent with some empirical evidence on diversification; however, it contradicts the bulk of literature on competition, which suggests a negative influence on the probability of distress. The ownership structure and the capital requirement index were uninformative. Similar to the previous literature, the institutional development index was statistically relevant predictor. The bank specific and other CAMEL type variables as well as the systematic shocks in the financial and macroeconomic environment were all found to be in line with the findings of related empirical studies. Finally, we find that by conditioning on the relevant covariates, a simple hazard model has performed fairly well in predicting bank distress in the GCC countries.

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

I argue that hazard models are more appropriate than single-period models for forecasting bankruptcy. Single-period models are inconsistent, while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model. I find that about half of the accounting ratios that have been used in previous models are not statistically significant. Moreover, market size, past stock returns, and idiosyncratic returns variability are all strongly related to bankruptcy. I propose a model that uses both accounting ratios and market-driven variables to produce out-of-sample forecasts that are more accurate than those of alternative models. Copyright 2001 by University of Chicago Press.

Forecasting Bankruptcy More Accurately: A Simple Hazard Model

I argue that hazard models are more appropriate for forecasting bankruptcy than the single-period models used previously. Single-period bankruptcy models give biased and inconsistent probability estimates while hazard models produce consistent estimates. I describe a simple technique for estimating a discrete-time hazard model with a logit model estimation program. Applying my technique, I find that about half of the accounting ratios that have been used in previous models are not statistically significant bankruptcy predictors. Moreover, several market-driven variables are strongly related to bankruptcy probability, including market size, past stock returns, and the idiosyncratic standard deviation of stock returns. I propose a model that uses a combination of accounting ratios and market-driven variables to produce more accurate out-of-sample forecasts than alternative models.