Systemic event prediction by early warning system

This work develops an early warning system framework for assessing systemic risks and for predicting systemic events, i.e. periods of extreme financial instability with potential real costs, over the short horizon of six quarters and the long horizon of twelve quarters on the panel of 14 countries, both advanced and developing. First, we build Financial Stress Index to identify starting dates of systemic financial crises for each country in the panel. Second, early warning indicators for assessment and prediction of systemic risks are selected in a two-step approach; relevant prediction horizons for each indicator are found by the univariate logit model followed by the application of Bayesian model averaging method to identify the most useful indicators. Next, we validate early warning model, containing only useful indicators, for both horizons on the panel. Finally, the in-sample performance of the constructed EWS over both horizons is assessed for the Czech Republic. We find that the model over the 3 years' horizon slightly outperforms the EWS with the horizon of 1.5 years on the Czech data. The long model attains the maximum utility in crises detection as well as it maximizes area under Receiver Operating Characteristics curve which measures the quality of the forecast

Comparing different early warning systems: Results from a horse race competition among members of the Macro-prudential Research Network

Over the recent decades researchers in academia and central banks have developed early warning systems (EWS) designed to warn policy makers of potential future economic and financial crises. These EWS are based on diverse approaches and empirical models. In this paper we compare the performance of nine distinct models for predicting banking crises resulting from the work of the Macroprudential Research Network (MaRs) initiated by the European System of Central Banks. In order to ensure comparability, all models use the same database of crises created by MaRs and comparable sets of potential early warning indicators. We evaluate the models’ relative usefulness by comparing the ratios of false alarms and missed crises and discuss implications for pratical use and future research. We find that multivariate models, in their many appearances, have great potential added value over simple signalling models. One of the main policy recommendations coming from this exercise is that policy makers can benefit from taking a broad methodological approach when they develop models to set macro-prudential instruments

Comparing different early warning systems: Results from a horse race competition among members of the Macro-prudential Research Network

Over the recent decades researchers in academia and central banks have developed early warning systems (EWS) designed to warn policy makers of potential future economic and financial crises. These EWS are based on diverse approaches and empirical models. In this paper we compare the performance of nine distinct models for predicting banking crises resulting from the work of the Macroprudential Research Network (MaRs) initiated by the European System of Central Banks. In order to ensure comparability, all models use the same database of crises created by MaRs and comparable sets of potential early warning indicators. We evaluate the models’ relative usefulness by comparing the ratios of false alarms and missed crises and discuss implications for pratical use and future research. We find that multivariate models, in their many appearances, have great potential added value over simple signalling models. One of the main policy recommendations coming from this exercise is that policy makers can benefit from taking a broad methodological approach when they develop models to set macro-prudential instruments

Comparing different early warning systems: Results from a horse race competition among members of the Macro-prudential Research Network

Over the recent decades researchers in academia and central banks have developed early warning systems (EWS) designed to warn policy makers of potential future economic and financial crises. These EWS are based on diverse approaches and empirical models. In this paper we compare the performance of nine distinct models for predicting banking crises resulting from the work of the Macroprudential Research Network (MaRs) initiated by the European System of Central Banks. In order to ensure comparability, all models use the same database of crises created by MaRs and comparable sets of potential early warning indicators. We evaluate the models’ relative usefulness by comparing the ratios of false alarms and missed crises and discuss implications for pratical use and future research. We find that multivariate models, in their many appearances, have great potential added value over simple signalling models. One of the main policy recommendations coming from this exercise is that policy makers can benefit from taking a broad methodological approach when they develop models to set macro-prudential instruments

NManagement Board Composition of Banking Institutions and Bank Risk-Taking: The Case of the Czech Republic

The paper investigates how management board composition of banking institutions impacts their risk-taking behavior in the Czech Republic. More specifically, we examine the effect of average director age, proportion of female directors, non-national directors and proportion of their attained education on four different bank risk proxies. We build a unique data set comprising selected biographical information on management board members of the Czech financial institutions holding a banking license over 2001-2012 period. For the Czech banking sector overall, we find that higher proportions of non-national directors increase bank risk measured by profit volatility and decrease bank stability captured by Z-score. Similarly, a larger proportion of directors holding an MBA raises bank riskiness measured by profit volatility. On the other hand, the presence of directors holding a PhD on boards of large Czech banks enhances bank stability captured by Z-score. Moreover, we detect risk-enhancing implications of board size for the segments of building savings societies and small and midsized banks. As for average board tenure, its effect on risk-taking varies depending on bank characteristics. We find mixed evidence on the effect of female directors and do not find any strong effect of directors' age on risk in the Czech banking sector

Updating the Long Term Rate in Time: A Possible Approach

This study proposes the potential methodological approach to be utilized by regulators when setting up a Long-Term Rate (LTR) for the evaluation of insurers' liabilities beyond the last liquid point observable in the market. Our approach is based on the optimization of two contradictory aspects - stability and accuracy implied by economic fundamentals. We use U.S. Treasury term structure data over the period 1985-2015 to calibrate an algorithm that dynamically revises LTR based on the distance between the value implied by long-term growth of economic fundamentals in a given year and the regulatory value of LTR valid in a year prior. We employ both Nelson-Siegel and Svensson models to extrapolate yields over maturities of 21-30 years employing the selected value of the LTR and compare them to the observed yields using mean square error statistic. Furthermore, we optimise the parameter of the proposed LTR formula by minimising the defined loss function capturing both mentioned factors

How Puzzling Is the Forward Premium Puzzle? A Meta-Analysis

A key theoretical prediction in financial economics is that under risk neutrality and rational expectations a currency's forward rates should form unbiased predictors of future spot rates. Yet scores of empirical studies report negative slope coefficients from regressions of spot rates on forward rates, which is inconsistent with the forward rate unbiasedness hypothesis. We collect 3,643 estimates from 91 research articles and using recently developed techniques investigate the effect of publication and misspecification biases on the reported results. Correcting for these biases we estimate the slope coefficients of 0.31 and 0.98 for developed and emerging currencies respectively, which implies that empirical evidence is in line with the theoretical prediction for emerging economies and less puzzling than commonly thought for developed economies. Our results also suggest that the coefficients are systematically influenced by the choice of data, numeraire currencies, and estimation methods