Diversification and risk-adjusted performance: A quantile regression approach

The effect of diversification on firm performance has been debated. We reexamine the effect using a sample of 44,248 observations of non-financial US firms for the 1997–2009 period employing the quantile regression approach. Our empirical results show that the effect of diversification on firm performance is not homogeneous across various quantile levels: the diversification discount (premium) shows up in firms with high (low) RoE quantiles. Further, we find that diversification affects firm risk as well. Therefore, we consider a risk-adjusted performance measure and find that both diversification discount and premium disappear, which is consistent with the risk-return trade-off principle.     

The structure and degree of dependence: A quantile regression approach

The copula function defines the degree of dependence and the structure of dependence. This paper proposes an alternative framework to decompose the dependence using quantile regression. We demonstrate that the methodology provides a detailed picture of dependence including asymmetric and non-linear relationships. In addition, changes in the degree or structure of dependence can be modeled and tested for each quantile of the distribution. The empirical part applies the framework to three different sets of financial time-series and demonstrates substantial differences in dependence patterns among asset classes and through time. The analysis of 54 global equity markets shows that detailed information about the structure of dependence is crucial to adequately assess the benefits of diversification in normal times and crisis times.     

Demand for international reserves in developing nations: A quantile regression approach

I estimate the determinants of the demand for international reserves using quantile regressions. Employing a dataset of 108 developing nations over the period of 1980-2007, I find considerable differences at different points of the conditional distribution of reserves. The estimates of elasticities that were found to be insignificant in ordinary least squares regressions become statistically significant at various quantiles of the distribution. Furthermore, majority of the explanatory variables have lower coefficients for the nations that are located on the right tail of the conditional reserve distribution. This finding shows that the level of reserves is as important as the other determinants of the demand for reserves.     

Testing the inflation rates in MENA countries: Evidence from quantile regression approach and seasonal unit root test

We investigate the dynamic behavior and seasonal property (with regime shift) of inflation in the Middle East and North Africa (MENA) countries. Our investigation uses the quantile regression approach developed by Koenker and Xiao (2004) and the newly developed seasonal unit root test of Narayan and Popp (2011) respectively. Our empirical results show that the inflation rates are not mean-reverting, and they show the asymmetries in their dynamic adjustment. Further, we find a seasonal unit root does not exist in the inflation rate for any country in this study. This finding implies that shocks do not have lasting effects on the inflation rate.     

An application of two-stage quantile regression to insurance ratemaking

Two-part models based on generalized linear models are widely used in insurance rate-making for predicting the expected loss. This paper explores an alternative method based on quantile regression which provides more information about the loss distribution and can be also used for insurance underwriting. Quantile regression allows estimating the aggregate claim cost quantiles of a policy given a number of covariates. To do so, a first stage is required, which involves fitting a logistic regression to estimate, for every policy, the probability of submitting at least one claim. The proposed methodology is illustrated using a portfolio of car insurance policies. This application shows that the results of the quantile regression are highly dependent on the claim probability estimates. The paper also examines an application of quantile regression to premium safety loading calculation, the so-called Quantile Premium Principle (QPP). We propose a premium calculation based on quantile regression which inherits the good properties of the quantiles. Using the same insurance portfolio data-set, we find that the QPP captures the riskiness of the policies better than the expected value premium principle.     

Value at risk estimation by quantile regression and kernel estimator

Risk management has attracted a great deal of attention, and Value at Risk (VaR) has emerged as a particularly popular and important measure for detecting the market risk of financial assets. The quantile regression method can generate VaR estimates without distributional assumptions; however, empirical evidence has shown the approach to be ineffective at evaluating the real level of downside risk in out-of-sample examination. This paper proposes a process in VaR estimation with methods of quantile regression and kernel estimator which applies the nonparametric technique with extreme quantile forecasts to realize a tail distribution and locate the VaR estimates. Empirical application of worldwide stock indices with 29 years of data is conducted and confirms the proposed approach outperforms others and provides highly reliable estimates.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

Predicting recovery rates using logistic quantile regression with bounded outcomes

Logistic quantile regression (LQR) is used for studying recovery rates. It is developed using monotone transformations. Using Moody’s Ultimate Recovery Database, we show that the recovery rates in different partitions of the estimation sample have different distributions, and thus for predicting recovery rates, an error-minimizing quantile point over each of those partitions is determined for LQR. Using an expanding rolling window approach, the empirical results confirm that LQR with the error-minimizing quantile point has better and more robust out-of-sample performance than its competing alternatives, in the sense of yielding more accurate predicted recovery rates. Thus, LQR is a useful alternative for studying recovery rates.     

A nonparametric quantity-of-quality approach to assessing financial asset return performance

This paper adapts two recent developments from the bibliometric literature to the problem of assessing the return performance of a financial asset. The result is a quantity-of-quality metric, which is both nonparametric and moment-free. As such, it offers a nonstandard perspective on the informational patterns in asset returns, and accordingly can complement traditional moment-based asset evaluation methods. The proposed approach is simple to apply, and while moment-free, captures intuitively important aspects of asset performance such as location, upside potential, downside risk, and volatility. It can also be expressed as a reward-to-risk ratio, which serves as a counterpart to the Sharpe ratio. Empirical and simulation results suggest that, relative to the Sharpe ratio, the proposed approach prefers assets with moderately higher means and standard deviations, and more favorable skewness.     

Modeling the bank regulator's closure option: A two-step logit regression approach

This article models the regulator's decision to close a bank as a call option. A two-equation model of bank failure that treats bank closings as an event timed by bank regulators is constructed and estimated for bank failures occurring from 1984 through 1989. The results of the regression experiment are consistent with the underlying theoretical model, as the majority of the regressors in the closure equation are significant with the correct sign.     

The Monday effect revisited: An alternative testing approach

This paper questions traditional approaches for testing the Monday effect of stock returns. We propose an alternative, multiple hypothesis testing approach based on the closure test principle which controls the multiple type I error. We consider the US, the UK and the German stock markets and test Monday related pairwise comparisons of daily expected stock returns, while the probability of committing any type I error is always kept smaller than a prespecified level α, for each combination of true null hypotheses. Overall, the new testing approach supports previous findings of a Monday effect for the 1970s and 1980s, in particular for the US and Germany, while it suggests that the Monday effect has vanished in the 1990s and 2000s in all three markets. The comparison of the closure test procedure, the traditional multiple t-test and the Bonferroni test, a classical multiple test procedure, shows that traditional testing may result in spurious significance while the Bonferroni test may sometimes be too conservative.     

Determinants of equity return correlations: a case study of the Amman Stock Exchange

This paper seeks to explain time-varying correlations among equity returns. The literature has shown that fundamental and economic factors can explain stock returns or the volatility of markets. Here, panel data analysis is employed to examine whether these factors can also explain the comovement of stock returns. Time-varying correlations among sectoral indexes are estimated using a restricted multivariate threshold GARCH model with dynamic conditional correlation controlling for the asymmetric effects of news and the influence of financial crises. The empirical results from this panel data analysis show that equity return correlations can be explained not only by macroeconomic variables but also by fundamentals within an industry.     

Stock returns and inflation revisited: An evaluation of the inflation illusion hypothesis

Several hypotheses have been proposed to explain the stock return–inflation relation. The Modigliani and Cohn’s inflation illusion hypothesis has received renewed attention. Another hypothesis is the two-regime hypothesis. We reexamine these hypotheses using long sample data of the US and international data. We find that the inflation illusion hypothesis can explain the post-war negative stock return–inflation relation, but it is not compatible with the pre-war positive relation. Using a structural VAR identification method, we show that there are two regimes with positive and negative stock return–inflation relations not only in each period of the US but also in every developed country we consider. This seems inconsistent with the inflation illusion hypothesis that predicts only a negative relation.     

A tale of three schools: insights on autocorrelations of short-horizon stock returns

This article reexamines the autocorrelation patterns of short-horizonstock returns. We document empirical results which imply thatthese autocorrelations have been overstated in the existingliterature. Based on several new insights, we provide supportfor a market efficiency-based explanation of the evidence. Ouranalysis suggests that institutional factors are the most likelysource of the autocorrelation patterns.     

Stock market liberalization and return volatility: Evidence from the emerging market of Sri Lanka

This study examines the impact of liberalization of the Sri Lankan stock market on return volatility. We specify GARCH and TGARCH models of volatility, and estimate them using 16 years of weekly returns for the period from 1985 to 2000. The results show that liberalization of the market to foreign investors significantly increased the return volatility in the Colombo Stock Exchange. Both conditional and unconditional volatility measures are the highest in the liberalization period. Negative return shocks lead to lower volatility suggesting that there is no leverage effect, and this appears to reflect the very low levels of leverage used by Sri Lankan companies.     

A fully parametric approach to return modelling and risk management of hedge funds

This paper examines the empirical properties of hedge fund returns and proposes a fully parametric model capable of adequately describing both univariate and multivariate return properties. The suggested model is based on the multivariate extension of the Normal Inverse Gaussian (NIG) distribution and will be shown to be capable of capturing the characteristic distributional features of hedge fund returns. Drawing on recent research in the area of Generalized Hyperbolic distributions and their calibration, we will elaborate on the application of the NIG-model for risk management purposes, and highlight the differences between the NIG and the Gaussian model.      

Stock market uncertainty and economic fundamentals: an entropy-based approach

This study investigates the effects of stock market uncertainty on economic fundamentals, represented by economic activities and systemic risk, in China. To capture the uncertainty in the Chinese stock market precisely, we use the entropy measure through symbolic time-series analysis. The empirical findings reveal strong spillover effects from stock market uncertainty to economic fundamentals. Specifically, an uncertainty shock generates (i) a short-term decline in industrial production, (ii) a rapid drop and rebound in the composite leading indicator, and (iii) an increase in systemic risk. To understand these findings, we suggest and validate the transmission channel through changes in consumption and investment.     

Stock return seasonalities and the tax-loss selling hypothesis: Analysis of the arguments and Australian evidence

A ‘tax-loss selling’ hypothesis has frequently been advanced to explain the ‘January effect’ reported in this issue by Keim. This paper concludes that U.S. tax laws do not unambiguously predict such an effect. Since Australia has similar tax laws but a July–June tax year, the hypothesis predicts a small-firm July premium. Australian returns show pronounced December–January and July–August seasonals, and a premium for the smallest-firm decile of about four percent per month across all months. This contrasts with the U.S. data in which the small-firm premium is concentrated in January. We conclude that the relation between the U.S. tax year and the January seasonal may be more correlation than causation.     

Contrarian and momentum profitability revisited: Evidence from the London Stock Exchange 1964–2005

We provide evidence relating to contrarian and momentum profits for the LSE, using 64 strategies for all 6531 stocks traded from 1964 to 2005. Thorough analysis demands controlling for key potential (contradictory) explanations of the strategies’ profitability which span psychological characteristics (e.g. overreaction/underreaction), excess risk, seasonality, size, and microstructure induced biases. Results provide a measurement of the miscalculations which occur when ignoring survivorship and microstructure biases. Contrarian/momentum profits cannot be explained by seasonality, size, or a single factor risk model. However, the Fama–French three factor model rationalises all contrarian profits. Important differences are found when examining a truncated sample period demonstrating the need to recognise that financial markets can change markedly through time.