Forecasting financial crises for an enterprise by using the Grey Markov forecasting model

The business environment is rapidly changing and some enterprises have announced unexpected restructurings, leading to stagnating stock prices and declines in their business performance. To prepare for calamity, it is becoming increasingly important for enterprise managers to use current financial data for short-term financial forecasting. Managers and investors are increasingly concerned with immediately and accurately forecasting firm financial crises using a limited amount of financial data. This work employs Z-Score value, which can be used to measure multinomial financial crisis index for forecasting, and utilizes Grey Markov forecasting for valuation. Based on the research results, the accuracy of the Grey Markov forecasting model is as expected, with excellent Z-Score, and the model can rapidly forecast the likelihood of firm financial crises. The study results can provide a good reference for government and financial institutions in examining financial risk, and for investors in selecting investment targets.     

Spotting the Danger Zone: Forecasting Financial Crises With Classification Tree Ensembles and Many Predictors

This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out‐of‐sample forecasting performance over best‐practice early‐warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long‐run sample (1870–2011), and two broad post‐1970 samples which together cover almost all known systemic banking crises. I show that the marked improvement in forecasting performance results from the combination of many classification trees into an ensemble, and the use of many predictors. Copyright © 2016 John Wiley & Sons, Ltd.     

Currency crisis early warning systems: Why they should be dynamic

Traditionally, financial crisis Early Warning Systems (EWSs) have relied on macroeconomic leading indicators when forecasting the occurrence of such events. This paper extends such discrete-choice EWSs by taking the persistence of the crisis phenomenon into account. The dynamic logit EWS is estimated using an exact maximum likelihood estimation method in both a country-by-country and a panel framework. The forecasting abilities of this model are then scrutinized using an evaluation methodology which was designed recently, specifically for EWSs. When used for predicting currency crises for 16 countries, this new EWS turns out to exhibit significantly better predictive abilities than the existing static one, both in- and out-of-sample, thus supporting the use of dynamic specifications for EWSs for financial crises.     

Modeling and predicting U.S. recessions using machine learning techniques

The most representative machine learning techniques are implemented for modeling and forecasting U.S. economic activity and recessions in particular. An elaborate, comprehensive, and comparative framework is employed in order to estimate U.S. recession probabilities. The empirical analysis explores the predictive content of numerous well-followed macroeconomic and financial indicators, but also introduces a set of less-studied predictors. The predictive ability of the underlying models is evaluated using a plethora of statistical evaluation metrics. The results strongly support the application of machine learning over more standard econometric techniques in the area of recession prediction. Specifically, the analysis indicates that penalized Logit regression models, k-nearest neighbors, and Bayesian generalized linear models largely outperform ‘original’ Logit/Probit models in the prediction of U.S. recessions, as they achieve higher predictive accuracy across long-, medium-, and short-term forecast horizons.     

On the relation between currency and banking crises in developing countries, 1980–2010

This paper investigates the relationship between the occurrence of currency and banking crises using high-frequency data for a sample of 94 countries during 1980–2010. The two types of crises are proxied by continuous, multi-categorical and dummy variables based on market pressure indexes, and a dummy variable from the Laeven–Valencia banking crises database. Results suggest that a bidirectional leading relationship exists between the two types of crises. However, banking crises do not lead currency crises robustly when banking crises are proxied by dummies based on market pressure indexes. Finally, currency crises have robust state dependence, but this is not the case for banking crises.     

Does income inequality lead to banking crises in developing countries? Empirical evidence from cross-country panel data

This study empirically examines whether increasing income inequality results in banking crises using panel data for 68 countries covering the years 1973 to 2010. The results show that developing countries with high inequality tend to have higher levels of domestic credit and that domestic credit booms increase the probability of banking crises. We also find that developing economies display direct channels from inequality to banking crises without an association with credit booms. We find no consistent evidence that income inequality contributes to banking crises in advanced economies. In developing countries, the probability of banking crises increases dramatically as income inequality levels increase: The probability of a systemic banking crisis within three years is 9.5% when the Gini is as low as 0.2 in developing countries and increases to 57.4% when the Gini is 0.4. These results are robust to several specifications.     

Bayesian median autoregression for robust time series forecasting

We develop a Bayesian median autoregressive (BayesMAR) model for time series forecasting. The proposed method utilizes time-varying quantile regression at the median, favorably inheriting the robustness of median regression in contrast to the widely used mean-based methods. Motivated by a working Laplace likelihood approach in Bayesian quantile regression, BayesMAR adopts a parametric model bearing the same structure as autoregressive models by altering the Gaussian error to Laplace, leading to a simple, robust, and interpretable modeling strategy for time series forecasting. We estimate model parameters by Markov chain Monte Carlo. Bayesian model averaging is used to account for model uncertainty, including the uncertainty in the autoregressive order, in addition to a Bayesian model selection approach. The proposed methods are illustrated using simulations and real data applications. An application to U.S. macroeconomic data forecasting shows that BayesMAR leads to favorable and often superior predictive performance compared to the selected mean-based alternatives under various loss functions that encompass both point and probabilistic forecasts. The proposed methods are generic and can be used to complement a rich class of methods that build on autoregressive models.     

Evaluating alternative models of trend inflation

With the concept of trend inflation now being widely understood to be important to the accuracy of longer-term inflation forecasts, this paper assesses alternative models of trend inflation. Reflecting the models which are common in reduced-form inflation modeling and forecasting, we specify a range of models of inflation that incorporate different trend specifications. We compare the models on the basis of their accuracies in out-of-sample forecasting, both point and density. Our results show that it is difficult to say that any one model of trend inflation is the best. Several different trend specifications seem to be about equally accurate, and the relative accuracy is somewhat prone to instabilities over time.     

Modelling Currency Crises in Emerging Markets: A Dynamic Probit Model with Unobserved Heterogeneity and Autocorrelated Errors*

The paper investigates the causes of currency crises in emerging markets. We estimate the probability of a currency crisis by applying maximum smoothly simulated likelihood to a dynamic LDV model. This approach allows us to take explicit account of the existence of intertemporal links between crises. The results show that currency crises are influenced by real, monetary, debt and global variables. Past banking crises are significant determinants of the probability of currency crises. Moreover, countries that sharply devalued in the past are less prone to experience another currency crisis. We find evidence of unobserved heterogeneity, which may reflect differences in the countries’ institutional/historical background. Finally, the determinants of currency crises differ by type of exchange rate regime.     

Are universal banks bad for financial stability? Germany during the world financial crisis

This case study explores the contribution of universal banking to financial stability in Germany during the recent financial crisis. Germany is a prototype for universal banking and has suffered from a rather small number of banking crises in the past. We review the banking literature and analyze the major institutional and regulatory features of the German financial system to establish a nexus between universal banking and stability. We focus on the following questions. First, which banks failed and did they because they were universal or because of other reasons? Second, which types of distress beside outright bank failures resulted from the crisis and how did German universal banks dealt with them? We show that only few German banks failed and these banks did so not because they were universal banks but because they were publicly owned. Most banks instead contributed to reduce the impact of the recent crisis.     

An Exegesis on Currency and Banking Crises

Abstract.  This paper reviews the literature on currency and banking crises. Currency and banking crises are characterized according to some standards in the literature and their historical record summarized. The development of the literature from first through fourth‐generation, or so‐called 'institutional' models is reviewed. A digression on institutions is provided along with some sidebars on the development of the literature on institutions as it relates to economic growth. The empirical research on third‐generation (or twin crises) models and on institutional models of currency and banking crises, which are so far scarce, is covered too. A summary of the main policy issues for dealing with financial crises is presented. The paper closes with an emphasis on institutions and a call for more research directed at institutions and their role in the financial system.     

The study of contagious paces of financial crises

This study deals with the emergence of different regional crises and the comparison of early warning indicators to check for the accuracy of pace of exits. It was found that trade factors and monetary conditions clearly play a pivotal role in affecting the probability of existing time to currency crisis episodes and on the recurrence of crises. More specifically, using the index of market pressure methods, it is likely that the Asian Financial Crisis and the Mexico Tequila Crisis, when compared with the European Exchange Rate Mechanism (ERM) Crisis, were preceded by different spreads accelerating across those countries. The evidence suggests that efficient early warning indicators may exist and may be identified depending on the methods applied to the pace of exit involved.     

Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data

Accurate solar forecasts are necessary to improve the integration of solar renewables into the energy grid. In recent years, numerous methods have been developed for predicting the solar irradiance or the output of solar renewables. By definition, a forecast is uncertain. Thus, the models developed predict the mean and the associated uncertainty. Comparisons are therefore necessary and useful for assessing the skill and accuracy of these new methods in the field of solar energy.The aim of this paper is to present a comparison of various models that provide probabilistic forecasts of the solar irradiance within a very strict framework. Indeed, we consider focusing on intraday forecasts, with lead times ranging from 1 to 6 h. The models selected use only endogenous inputs for generating the forecasts. In other words, the only inputs of the models are the past solar irradiance data. In this context, the most common way of generating the forecasts is to combine point forecasting methods with probabilistic approaches in order to provide prediction intervals for the solar irradiance forecasts. For this task, we selected from the literature three point forecasting models (recursive autoregressive and moving average (ARMA), coupled autoregressive and dynamical system (CARDS), and neural network (NN)), and seven methods for assessing the distribution of their error (linear model in quantile regression (LMQR), weighted quantile regression (WQR), quantile regression neural network (QRNN), recursive generalized autoregressive conditional heteroskedasticity (GARCHrls), sieve bootstrap (SB), quantile regression forest (QRF), and gradient boosting decision trees (GBDT)), leading to a comparison of 20 combinations of models.None of the model combinations clearly outperform the others; nevertheless, some trends emerge from the comparison. First, the use of the clear sky index ensures the accuracy of the forecasts. This derived parameter permits time series to be deseasonalized with missing data, and is also a good explanatory variable of the distribution of the forecasting errors. Second, regardless of the point forecasting method used, linear models in quantile regression, weighted quantile regression and gradient boosting decision trees are able to forecast the prediction intervals accurately.     

3. Money, Credit, and Crisis

The financial crisis of 2008–2009 has antecedents in earlier crises, including the Great Depression. In order to understand how the current crisis arose, we must review the most fundamental principles of banking. Doing that, we find that the main service performed by banks is the creation of liquidity, a collective good that can be destroyed by the behavior of individual financial institutions. The key element in creating liquidity is the monetization of various types of collateral. When collateral takes the form of land or capital that turns over slowly, banks lose liquidity. That is why major banking crises have frequently been associated with real estate lending. The best way to restore health to the financial system is by restoring the principles of the "real bills" doctrine that requires loans to be self-liquidating.     

An Analytic Network Process model for financial-crisis forecasting

We discuss and develop an imbalance-crisis turning point model to forecast the likelihood of a financial crisis based on an Analytic Network Process framework. The Analytic Network Process (ANP) is a general theory of relative measurement used to derive composite-priority-ratio scales from individual-ratio scales that represent relative influence of factors that interact with respect to control criteria. Through its supermatrix, which is composed of matrices of column priorities, the ANP framework captures the outcome of dependence and feedback within and between clusters of explanatory factors. We argue that our framework is more flexible and is more comprehensive than traditional methods and previous models. We illustrate how the ANP model would be implemented for forecasting the probability of crises.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Forecasting electricity prices using bid data

Market liberalization and the expansion of variable renewable energy sources in power systems have made the dynamics of electricity prices more uncertain, leading them to show high volatility with sudden, unexpected price spikes. Thus, developing more accurate price modeling and forecasting techniques is a challenge for all market participants and regulatory authorities. This paper proposes a forecasting approach based on using auction data to fit supply and demand electricity curves. More specifically, we fit linear (LinX-Model) and logistic (LogX-Model) curves to historical sale and purchase bidding data from the Iberian electricity market to estimate structural parameters from 2015 to 2019. Then we use time series models on structural parameters to predict day-ahead prices. Our results provide a solid framework for forecasting electricity prices by capturing the structural characteristics of markets.     

Penalized maximum likelihood estimation of logit-based early warning systems

Panel logit models have proved to be simple and effective tools to build early warning systems (ews) for financial crises. But because crises are rare events, the estimation of ews does not usually account for country-specific fixed effects, so as to avoid losing all the information relative to countries that never face a crisis. I propose using a penalized maximum likelihood estimator for fixed-effects logit-based ews where all the observations are retained. I show that including country effects, while preserving the entire sample, improves the predictive performance of ews, both in simulation and out of sample, with respect to the pooled, random-effects and standard fixed-effects models.     

MULTIPLE EVENT INCIDENCE AND DURATION ANALYSIS FOR CREDIT DATA INCORPORATING NON‐STOCHASTIC LOAN MATURITY

Applications of duration analysis in economics and finance exclusively employ methods for events of stochastic duration. In application to credit data, previous research incorrectly treats the time to predetermined maturity events as censored stochastic event times. The medical literature has binary parametric ‘cure rate’ models that deal with populations that never experienced the modelled event. We propose and develop a multinomial parametric incidence and duration model, incorporating such populations. In the class of cure rate models, this is the first fully parametric multinomial model and is the first framework to accommodate an event with predetermined duration. The methodology is applied to unsecured personal loan credit data provided by one of Australia's largest financial services organizations. This framework is shown to be more flexible and predictive through a simulation and empirical study that reveals: simulation results of estimated parameters with a large reduction in bias; superior forecasting of duration; explanatory variables can act in different directions upon incidence and duration; and variables exist that are statistically significant in explaining only incidence or duration. Copyright © 2013 John Wiley & Sons, Ltd.