Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union

Existing papers on extreme dependence use symmetrical thresholds to define simultaneous stock market booms or crashes such as the joint occurrence of the upper or lower one percent return quantile in both stock markets. We show that the probability of the joint occurrence of extreme stock returns may be higher for asymmetric thresholds than for symmetric thresholds. We propose a non-parametric measure of extreme dependence which allows capturing extreme events for different thresholds and can be used to compute different types of extreme dependence. We find that extreme dependence among the stock markets of ten initial EMU member countries, the United Kingdom, and the United States is largely asymmetrical in the pre-EMU period (1989–1998) and largely symmetrical in the EMU period (1999–2010). Our findings suggest that ignoring the possibility of asymmetric extreme dependence may lead to an underestimation of the probability of co-booms and co-crashes.     

Marriage and other risky assets: A portfolio approach

We study the joint impact of gender and marital status on financial investments by testing the hypothesis that marriage represents – in a portfolio framework – a sort of safe asset and that this attribute may change over time. We show that married individuals have a higher propensity to invest in risky assets than single ones, that this marital status gap is stronger for women and that, for women only, it evolves and declines at the end of the sample period. Next we explore a number of possible explanations of the observed gender differences by controlling for background factors that capture the evolution of family and society. We find that both the higher female marital status gap and its time variability vanish for those women who are employed. Our empirical investigation is based on a dataset drawn from the 1993–2006 Bank of Italy Survey of Household Income and Wealth.     

Conditional beta pricing models: A nonparametric approach

We propose a two-stage procedure to estimate conditional beta pricing models that allows for flexibility in the dynamics of asset betas and market prices of risk (MPR). First, conditional betas are estimated nonparametrically for each asset and period using the time-series of previous data. Then, time-varying MPR are estimated from the cross-section of returns and betas. We prove the consistency and asymptotic normality of the estimators. We also perform Monte Carlo simulations for the conditional version of the three-factor model of Fama and French (1993) and show that nonparametrically estimated betas outperform rolling betas under different specifications of beta dynamics. Using return data on the 25 size and book-to-market sorted portfolios, we find that the nonparametric procedure produces a better fit of the three-factor model to the data, less biased estimates of MPR and lower pricing errors than the Fama–MacBeth procedure with betas estimated under several alternative parametric specifications.     

Long term dependence in stock returns

We show, using the modified rescaled range statistic, that none of the return series of indices of five European countries, the United States and Japan exhibits long term dependence. This statistic — introduced by Lo (1991) — correct Hurst's (1951) ‘classical’ rescaled range statistic for short term dependence. We also report the classical rescaled range statistic after adjusting the series for short term dependence. This procedure shows, for cases where the results of the modified rescaled range statistic are mixed, that no long term dependence can be found. Simulations indicate reasonable power of this adjustment procedure. Furthermore, we find that estimates of the Hurst exponent, a related measure of long term dependence, are also biased by short term dependence. Simulations show that this measure — that has recently attracted growing interest — cannot distinguish between models with or without long term dependence.     

The behavior of consumer loan rates during the 1990 credit slowdown

This study re-examines the 1990 credit slowdown by investigating the loan pricing behavior of commercial banks. We find strong evidence that large, undercapitalized banks contributed to the credit slowdown by charging consumers a higher-than-average loan rate relative to better-capitalized institutions. This disparity in lending exists even after accounting for bank funding costs. Thus, we argue that there was a lending slowdown that occurred among large, undercapitalized banks. The reluctance to lend among undercapitalized banks is at least suggestive of behavior that is consistent with a credit crunch.     

The effects of loan portfolio concentration on Brazilian banks’ return and risk

This paper tests whether diversification of the credit portfolio at the bank level leads to better performance and lower risk. We employ a new high frequency (monthly) panel data for the Brazilian banking system with information at the bank level for loans by economic sector. We find that loan portfolio concentration increases returns and also reduces default risk; the impact of concentration on bank’s return is decreasing on bank’s risk; there are significant size effects; foreign and state-owned banks seem to be less affected by the degree of diversification. An important additional finding is that there is an increasing concentration trend after the breakout of the recent international financial crisis, specially after the failure of Lehman Brothers.     

Systemic risk contributions: A credit portfolio approach

We put forward a framework for measuring systemic risk and attributing it to individual banks. Systemic risk is coherently measured as the expected loss to depositors and investors when a systemic event occurs. The risk contributions are calculated so as to ensure a full risk allocation among institutions. Applying our methodology to a panel of 54–86 of the world’s major commercial banks for a 13-year time span with monthly frequency not only allows us to closely match the list of G-SIBs; we can also use individual risk contributions to compute bank-specific surcharges: systemic capital charges as well as countercyclical buffers. We therefore address both dimensions of systemic risk – cross-sectional and time-series – in a single integrated approach. As the analysis of risk drivers confirms, the main focus of macroprudential supervision should be on a solid capital base throughout the financial cycle and de-correlation of banks’ asset values.     

Do institutional investors have superior stock selection ability in China?

This paper uses unique data on the shareholdings of both institutional and individual investors to directly investigate whether institutional investors have better stock selection ability than individual investors in China. Controlling for other factors, we find that institutional investors increase (decrease) their shareholdings in stocks that subsequently exhibit positive (negative) short- and long-term cumulative abnormal returns. In contrast, individual investors decrease (increase) their shareholdings in stocks that subsequently exhibit positive (negative) short- and long-term cumulative abnormal returns. These findings indicate that institutional investors have superior stock selection ability in China.     

Modeling default probabilities: The case of Brazil

Using disaggregated data from the Brazilian stock market, we calculate default probabilities for 30 different economic sectors. Empirical results suggest that domestic macroeconomic factors can explain these default probabilities. In addition, we construct the Minimum Spanning Tree (MST) and the ultrametric hierarchical tree with the MST based on default probabilities to disclose common trends, which reveals that some sectors form clusters. The results of this paper imply that macroeconomic variables have distinct effects on default probabilities, which is important to take into account in credit risk modeling and the generation of stress test scenarios.     

Volatility and covariation of financial assets: A high-frequency analysis

Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance–covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of n assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting.     

Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions

Financial returns typically display heavy tails and some degree of skewness, and conditional variance models with these features often outperform more limited models. The difference in performance may be especially important in estimating quantities that depend on tail features, including risk measures such as the expected shortfall. Here, using recent generalizations of the asymmetric Student-t and exponential power distributions to allow separate parameters to control skewness and the thickness of each tail, we fit daily financial return volatility and forecast expected shortfall for the S&P 500 index and a number of individual company stocks; the generalized distributions are used for the standardized innovations in a nonlinear, asymmetric GARCH-type model. The results provide evidence for the usefulness of the general distributions in improving fit and prediction of downside market risk of financial assets. Constrained versions, corresponding with distributions used in the previous literature, are also estimated in order to provide a comparison of the performance of different conditional distributions.     

Securities class actions in the US banking sector: Between investor protection and bank stability

This paper investigates whether securities class actions (SCAs) can play a role in banking supervision, both as a warning signal of insolvency and as an instrument of market discipline to encourage bank managers to carefully evaluate risk. Two groups of US banks are compared over the 2000–2008 period. One includes banks that have faced at least one SCA, while the other is composed of non-targeted banks (control group). Results indicate that collective private litigation procedures are more frequently directed at financially fragile intermediaries exhibiting inadequate governance standards. Furthermore, banks which have been subjected to SCAs are likely to reduce their excessive risk positions. This supports the idea that SCAs could be efficiently employed as a complement to public supervisory activity in the banking sector.     

A note on the returns from minimum variance investing

Disappointed with the performance of market weighted benchmark portfolios yet skeptical about the merits of active portfolio management, investors in recent years turned to alternative index definitions. Minimum variance investing is one of these popular concepts. I show in this paper that the portfolio construction process behind minimum variance investing implicitly picks up risk-based pricing anomalies. In other words the minimum variance tends to hold low beta and low residual risk stocks. Long/short portfolios based on these characteristics have been associated in the empirical literature with risk adjusted outperformance. This paper shows that 83% of the variation of the minimum variance portfolio excess returns (relative to a capitalization weighted alternative) can be attributed to the FAMA/FRENCH factors as well as to the returns on two characteristic anomaly portfolios. All regression coefficients (factor exposures) are highly significant, stable over the estimation period and correspond remarkably well with our economic intuition. The paper also shows that a direct combination of market weighted benchmark portfolio and risk based characteristic portfolios will provide a statistically significant improvement over the indirect pickup via the minimum variance portfolio.     

Incorporating the dynamics of leverage into default prediction

A firm’s current leverage ratio is one of the core characteristics of credit quality used in statistical default prediction models. Based on the capital structure literature, which shows that leverage is mean-reverting to a target leverage, we forecast future leverage ratios and include them in the set of default risk drivers. An out-of-sample analysis of default predictions from a hazard model reveals that the discriminative power increases substantially when leverage forecasts are included. We further document that credit ratings contain information beyond the one contained in standard variables but that this information is unrelated to forecasts of leverage ratios.     

A computing bias in estimating the probability of informed trading

This study identifies a factor that leads to a bias in estimating the probability of informed trading (PIN), a widely-used microstructure measure. It is shown that, along with the numerical maximization of the likelihood function for PIN, the floating-point exception (i.e., overflow or underflow) may eliminate feasible solutions to the actual parameters in the optimization problem. Approximately 44% of PIN estimates for recent stock market data may have been subject to a downward bias that is more pronounced for active stocks than for inactive stocks. This study develops a remedy to mitigate the resulting bias.     

Banking 3.0—Designing financial regulation systems: The case for simple rules

In the past quarter century, financial innovation and deregulation have increasingly pushed the banks into shadow banking, magnifying the risks and leaving the regulators bereft of the tools needed to analyze and manage an increasingly complex and interconnected system. The purpose of the new regulatory process should be to return banks their intermediation function. However, the system has to focus on simplicity, and its purpose has to be to promote effective risk management, transparency and accountability by changing incentives without burdening the banking system with crippling capital and regulatory requirements.     

The persistent effects of a false news shock

In September 2008, a six-year-old article about the 2002 bankruptcy of United Airlines' parent company resurfaced on the Internet and was mistakenly believed to be reporting a new bankruptcy filing by the company. This episode caused the company's stock price to drop by as much as 76% in just a few minutes, before NASDAQ halted trading. After the “news” had been identified as false, the stock price rebounded, but still ended the day 11.2% below the previous close. We explore this natural experiment by using a simple asset-pricing model to study the aftermath of this false news shock. We find that, after three trading sessions, the company's stock was still trading below the two-standard-deviation band implied by the model and that it returned to within one standard deviation only during the sixth trading session. On the seventh day after the episode, the stock was trading at the level predicted by the asset-pricing model. We investigate several potential explanations for this finding, but fail to find empirical evidence supporting any of them. We also document that the false news shock had a persistent negative effect on the stock prices of other major airline companies. This is consistent with the view that contagion effects would have dominated competitive effects had the bankruptcy actually taken place.     

Comparison of modeling methods for Loss Given Default

We compare six modeling methods for Loss Given Default (LGD). We find that non-parametric methods (regression tree and neural network) perform better than parametric methods both in and out of sample when over-fitting is properly controlled. Among the parametric methods, fractional response regression has a slight edge over OLS regression. Performance of the transformation methods (inverse Gaussian and beta transformation) is very sensitive to ε, a small adjustment made to LGDs of 0 or 1 prior to transformation. Model fit is poor when ε is too small or too large, although the fitted LGDs have strong bi-modal distribution with very small ε. Therefore, models that produce strong bi-model pattern do not necessarily have good model fit and accurate LGD predictions. Even with an optimal ε, the performance of the transformation methods can only match that of the OLS.     

International business: determinants of interbank activities

This paper investigates the determinants of the international interbank market, a significant component of international trade in financial services. The sample encompasses both monthly and quarterly data from 1983 to 1993. The superiority of the monthly results suggest that the interbank market should be modelled within a short-term framework. This data interval captures the short-term movement of funds between currencies, Eurobonds, the nonbank market and the domestic banking market by banks to maximize returns. Moreover, the interbank market does not necessarily move in line with fundamental trade and income variables. Rather, the market is sensitive to return differentials, the relative cost of capital, the yield curve and international nonbank assets. The empirical results also indicate that nationality remains important in interbank trading because of the advantages it imparts on the home country in dealing in its home currency, particularly if that currency is a vehicle currency.     

Investor type trading behavior and trade performance: Evidence from the Thai stock market

This paper examines the trading behavior and decomposes the trading performance of foreign, individual and institutional investors as well as proprietary traders in a dynamic emerging stock market, the Stock Exchange of Thailand. Foreign investors follow a positive feedback, momentum strategy and are good short term market timers but have poor security selection performance in poor markets, thus suggesting that they have a macro (market timing) but not a micro (security selection) informational advantage relative to local investors. Institutions and proprietary traders have poor security selection trading performance. Individuals display herding behavior and have fairly good security selection performance, but individual investors appear to compensate proprietary traders for the provision of short term liquidity by proprietary traders, so individuals' security selection gains are canceled out by market timing losses.