Portfolio Optimization and Long-Term Dependence

Whilst emphasis has been given to short-term dependence of financial returns, long-term dependence remains overlooked. Despite financial literature provides evidence of long-term’s memory existence, serial-independence assumption prevails. This document’s long-term dependence assessment relies on rescaled range analysis (R/S), a popular and robust methodology designed for Geophysics but extensively used in financial literature. Results correspond to most of the previous evidence of significant long-term dependence, particularly for small and illiquid markets, where persistence is its most common kind. Persistence conveys that the range of possible future values of the variable will be wider than the range of purely random and independent variables. Ahead of R/S financial literature, authors estimate an adjusted Hurst exponent in order to properly estimate the covariance matrix at higher investment horizons, avoiding the traditional -independence reliant- square-root-of-time rule. Ignoring long-term dependence within the mean-variance portfolio optimization results in concealed risk taking; conversely, by adjusting for long-term dependence the weight of high (low) persistence risk factors decreases (increases) as the investment horizon widens. This alleviates some well-known shortcomings of conventional portfolio optimization for long-term investors (e.g. central banks, pension funds and sovereign wealth managers), such as excessive risk taking in long-term portfolios, extreme weights, home bias, and reluctance to hold foreign currency-denominated assets.Portfolio optimization, Hurst exponent, long-term dependence, biased random walk, rescaled range analysis. Classification JEL: G11, G32, G20, C14.

The euro area Bank Lending Survey matters: empirical evidence for credit and output growth

This study examines empirically the information content of the euro area Bank Lending Survey for aggregate credit and output growth. The responses of the lending survey, especially those related to loans to enterprises, are a significant leading indicator for euro area bank credit and real GDP growth. Notwithstanding the short history of the survey, the findings are robust across various specifications, including “horse races” with other well-known leading financial indicators. Our results are supportive of the existence of a bank lending, balance sheet, and risk-taking channel of monetary policy. They also suggest that price as well as non-price conditions and terms of credit standards do matter for credit and business cycles. Finally, we discuss the implications for the 2007/2009 financial and economic crisis. JEL Classification: C23, E32, E51, E52, G21, G28bank lending survey, business cycle, credit cycle, euro area, Monetary policy transmission

Finance and diversification

We study how financial market efficiency affects a measure of diversification of output across industrial sectors borrowed from the portfolio allocation literature. Using data on sector-level value added for a wide cross section of countries and for various levels of disaggregation, we construct a benchmark measure of diversification as the set of allocations of aggregate output across industrial sectors which minimize the economy’s long-term volatility for a given level of long-term growth. We find that financial markets increase substantially the speed with which the observed sectoral allocation of output converges towards the optimally diversified benchmark. Convergence to the optimal shares of aggregate output is relatively faster for sectors that have a higher "natural" long-term risk-adjusted growth and which exhibit higher information frictions. Our results are robust to using various proxies for financial development, to accounting for the endogeneity of finance, and to controlling for investor’s protection, contract enforcement, and barriers to entry. Crucially, the observed patterns disappear when we employ "naive" measures of diversification based on the equal spreading of output across sectors. JEL Classification: E32, E44, G11, O16diversification, Financial Development, Growth, Mean-variance efficiency, Volatility

European Union enlargement and equity markets in accession countries

The announcement of European Union enlargement coincided with a dramatic rise in stock prices in accession countries. This paper investigates the hypothesis that the rise in stock prices was a result of the repricing of systematic risk due to the integration of accession countries into the world market. We find that firm-level stock price changes are positively related to the difference between a firm’s local and world market betas. This result is robust to controlling for changes in expected earnings, country effects and other controls, although the magnitude of the effect is not very large. The differences between local and world betas explain nearly 22% of the stock price increase. JEL Classification: F36, G15, G12asset pricing, EU enlargement, international financial integration

Too-connected-to-fail Institutions and Payments System’s Stability: Assessing Challenges for Financial Authorities

The most recent episode of market turmoil exposed the limitations resulting from the traditional focus on too-big-to-fail institutions within an increasingly systemic-crisis-prone financial system, and encouraged the appearance of the too-connected-to-fail (TCTF) concept. The TCTF concept conveniently broadens the base of potential destabilizing institutions beyond the traditional banking-focused approach to systemic risk, but requires methodologies capable of coping with complex, cross-dependent, context-dependent and non-linear systems. After comprehensively introducing the rise of the TCTF concept, this paper presents a robust, parsimonious and powerful approach to identifying and assessing systemic risk within payments systems, and proposes some analytical routes for assessing financial authorities’ challenges. Banco de la Republica’s approach is based on a convenient mixture of network topology basics for identifying central institutions, and payments systems simulation techniques for quantifying the potential consequences of central institutions failing within Colombian large-value payments systems. Unlike econometrics or network topology alone, results consist of a rich set of quantitative outcomes that capture the complexity, cross-dependency, context-dependency and non-linearity of payments systems, but conveniently disaggregated and dollar-denominated. These outcomes and the proposed analysis provide practical information for enhanced policy and decision-making, where the ability to measure each institution’s contribution to systemic risk may assist financial authorities in their task to achieve payments system’s stability.Payments systems, too-connected-to-fail, too-big-to-fail, systemic risk, network topology, simulation, central bank liquidity. Classification JEL:E58, E44, C63, G21, D85.

Getting beyond carry trade: what makes a safe haven currency?

There is already a substantial literature documenting the fact that low yield currencies typically appreciate during times of global financial stress and behave as safe havens. The main objective of this paper is to find out what the fundamentals of safe haven currencies are. We analyse a large panel of 52 currencies in advanced and emerging countries over almost 25 years of data. We find that only a few factors are robustly associated to a safe haven status, most notably the net foreign asset position, an indicator of external vulnerability, and to a lesser extent the absolute size of the stock market, an indicator of market size and development. The interest rate spread against the US is significant only for advanced countries, whose currencies are subject to carry trade. More generally, we find that it is hard to predict what currencies would do when global risk aversion is high, as estimates are imprecise and often not stable or robust. This suggests caution in over-interpreting exchange rate movements during financial crises. JEL Classification: E44, F31, G15carry trade, global risk aversion, Globalisation, safe haven currencies, VIX

Interbank contagion at work: evidence from a natural experiment

This paper tests financial contagion due to interbank linkages. For identification we exploit an idiosyncratic, sudden shock caused by a large-bank failure in conjunction with detailed data on interbank exposures. First, we find robust evidence that higher interbank exposure to the failed bank leads to large deposit withdrawals. Second, the magnitude of contagion is higher for banks with weaker fundamentals. Third, interbank linkages among surviving banks further propagate the shock. Finally, we find results suggesting that there are real economic effects. These results suggest that interbank linkages act as an important channel of contagion and hold important policy implications. JEL Classification: G21, G28, E58Bank runs, banking crisis, Contagion, Deposit Insurance, Interbank Market, liquidity dry-ups, macro-prudential analysis, systemic risk, wholesale depositors

Feature selection in credit risk modeling: an international evidence

This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers. As such, to examine the impact of the feature selection method on classifier performance, we use two Chinese and three other real-world credit scoring datasets. The utilized feature selection methods are the least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression trees (CART), logistic regression (LR), artificial neural network (ANN), and support vector machines (SVM). Empirical findings confirm that LASSO’s feature selection method, followed by robust classifier SVM, demonstrates remarkable improvement and outperforms other competitive classifiers. Moreover, ANN also offers improved accuracy with feature selection methods; LR only can improve classification efficiency through performing feature selection via LASSO. Nonetheless, CART does not provide any indication of improvement in any combination. The proposed credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding of this study has practical value, as to date, there is no consensus about the combination of feature selection method and prediction classifiers

Default Predictors and Credit Scoring Models for Retail Banking

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union