Redistribution,inequality, and efficiency with credit constraints: Implications for South Africa

We develop a model that jointly determines the distribution of income and the aggregate macrodynamics. We identify multiple channels through which alternative public policies such as transfers, consumption and income taxes, and public investment will affect the inequality-efficiency trade-off. Income tax and transfers have both a direct income and an indirect substitution effect; a consumption tax has only the latter. We present extensive numerical simulations motivated by the South African National Development Plan 2030, the objective of which is to reduce soaring inequality and increase per capita GDP. Our results illustrate how the judicious combination of social grants and a consumption tax may help achieve these targets. The simulations also suggest that the sharp decline in the private-public capital ratio, coupled with a high degree of complementarity between public and private capital may help explain the persistence of market inequality in South Africa during the last two decades.     

金融时间序列指标判别框架：以特质波动率为例

基于拐点集合判别的TBUD方法主要思路是分析拐点集合间的关系,并在高维空间进行划分,从而搭建判别模型,并将分析框架应用在特质波动率等若干指标上,利用实证数据得到结论。应用 TBUD判别框架可以发现,特质波动率等指标无法对拐点集合进行清晰划分,因而并不具有预测能力。     

SKEWNESS‐AWARE ASSET ALLOCATION: A NEW THEORETICAL FRAMEWORK AND EMPIRICAL EVIDENCE

This paper presents a new measure of skewness, skewness‐aware deviation, that can be linked to prospective satisficing risk measures and tail risk measures such as Value‐at‐Risk. We show that this measure of skewness arises naturally also when one thinks of maximizing the certainty equivalent for an investor with a negative exponential utility function, thus bringing together the mean‐risk, expected utility, and prospective satisficing measures frameworks for an important class of investor preferences. We generalize the idea of variance and covariance in the new skewness‐aware asset pricing and allocation framework. We show via computational experiments that the proposed approach results in improved and intuitively appealing asset allocation when returns follow real‐world or simulated skewed distributions. We also suggest a skewness‐aware equivalent of the classical Capital Asset Pricing Model beta, and study its consistency with the observed behavior of the stocks traded at the NYSE between 1963 and 2006.     

The information content of option-implied information for volatility forecasting with investor sentiment

This study explores the effect of investor sentiment on the volatility forecasting power of option-implied information. We find that the risk-neutral skewness has the explanatory power regarding future volatility only during high sentiment periods. Furthermore, the implied volatility has varying volatility forecasting ability depending on the level of investor sentiment. Our findings suggest that the effectiveness of volatility forecasting models based on option-implied information varies over time with the level of investor sentiment. We confirm the important role of investor sentiment in volatility forecasting models exploiting option-implied information with strong evidence from in-sample and out-of-sample analyses. We also present improvements in the accuracy of volatility forecasts from volatility forecasting models derived by incorporating investor sentiment in these models.     

Conditional Properties of Hedge Funds: Evidence from Daily Returns*

Using daily returns on a set of hedge fund indices, we study (i) the properties of the indices' conditional density functions and (ii) the presence of asymmetries in conditional correlations between hedge fund indices and other investments and between hedge fund indices themselves. We use the SNP approach to obtain estimates of conditional densities of hedge fund returns and then proceed to examine their properties. In general, a nonparametric GARCH(1,1) model appears to provide the best fit for all strategies. We find that the conditional third and fourth moments are significantly affected by changes in the current volatility of returns on hedge fund indices. We examine changes in the conditional probability of tail events and report significant changes in the probability of extreme events when the conditioning information changes. These results have important implications for models of hedge fund risk that rely on probability of tail events. We formally test for the presence of asymmetries in conditional correlations to determine if there is contagion between hedge funds and other investments and between various hedge fund indices in extreme down markets versus extreme up markets. We generally do not find strong evidence in support of asymmetric correlations.     

Expected stock returns,common idiosyncratic volatility and average idiosyncratic correlation

Motivated by Herskovic et al. (2016), we examine the role of the average idiosyncratic correlation (ICOR) in two types of markets: an emerging market and a developed market. Examining daily stock data from the Chinese stock market for the period 1995 to 2020 and from the US for the period 1926 to 2019, we adopt high-dimensional principal component analysis (PCA) and thresholding methods to re-estimate ICOR. We find that ICOR plays an important role in explaining the expected stock returns, as the common idiosyncratic volatility (CIV) does in Herskovic et al. (2016). ICOR has been neglected in the literature due to large estimation error in the idiosyncratic covariance matrix and our analysis provides evidence that ICOR is nonnegligible in both markets when we control for several common market factors. We show that the average idiosyncratic covariance, which is the numerator of ICOR, exhibits the same pattern as CIV. Furthermore, our regression analyses of expected stock returns in response to ICOR change in both markets show that, in contrast to the negative result for CIV, the stocks’ high risk exposure to ICOR change comes with a higher risk premium, perhaps because of the synchronized but disproportionate changes in the monthly idiosyncratic covariance and idiosyncratic volatility.     

Dissecting the idiosyncratic volatility anomaly

The idiosyncratic volatility (IVOL) anomaly, documented in Ang, et al. (2006), has garnered a great deal of attention in the literature. Yet questions remain regarding the robustness and pervasiveness of the IVOL anomaly, with a particular concern that the IVOL anomaly might simply be the manifestation of market microstructure effect. In this paper, we show that the IVOL anomaly is strong and pervasive after we exclude stocks most susceptible to market microstructure noise — such as microcap stocks, penny stocks, and stocks with strong short-term return reversal. These results are robust to equal-weighting or value-weighting stocks in the IVOL portfolios. Our findings suggest that rather than being the cause of the anomaly, market microstructure noise actually weakens the IVOL anomaly.     

Portfolio concentration and mutual fund performance

Mutual fund managers should choose to increase their portfolio concentration when their information set is valuable enough that the benefits of the expected increase in alpha more than offsets the costs of the expected increase in idiosyncratic volatility. Consistent with that idea, we find that fund performance improves after concentration increases. Because the expected costs of increased concentration vary between funds and over time, the required expected benefits before managers choose to increase concentration should also vary. Among other results, we show that the concentration-performance relation is stronger for funds with less institutional ownership and when investor sentiment is low.     

Public Insurance against Idiosyncratic and Aggregate Risk: The Case of Social Security and Progressive Income Taxation

In this article, I discuss the role a progressive income taxsystem and a redistributive pay as you go (PAYGO) social securitysystem can play in insuring and reallocating idiosyncratic aswell as aggregate risk. I also argue that the underlying sourceof market failures generating such a role for government interventionmay be crucial when determining the normative consequences ofsuch social insurance. (JEL Classifications: E62, H21, H24,H55).     

Poverty and vulnerability in the Punjab,Pakistan: A multilevel analysis

This study estimates the prevalence and extent of vulnerability to poverty in the Punjab province of Pakistan. A multilevel model is used to analyze survey data of about 90,000 households distributed across 150 towns/tehsils. Empirical estimates show that the vulnerability rate is higher than the rate of poverty, and poverty-induced vulnerability is higher than risk-induced vulnerability. Moreover, idiosyncratic-vulnerability is higher than covariate-vulnerability. Unlike previous studies that find poverty to be a rural phenomenon, this research shows that poverty and vulnerability are equally high in urban areas. A high level of urban vulnerability adds urgency to anti-poverty interventions given a rapid urbanization in Pakistan.