Credit risk – Return puzzle: Evidence from India

Although asset pricing theories predict a positive relation between risk and returns, empirical findings on credit risk-return relationship are mixed. And, observed negative relation between the two in this regard is referred to as credit risk-return puzzle. Using credit rating as a measure of credit risk, we have investigated into the existence of this puzzle in India during July 2011 to March 2019. We have used information for the companies listed on the National Stock Exchange for this purpose. Our results validate the presence of this puzzle in the Indian stock market. Moreover, credit risk is observed to be a systematic risk, which has not been captured in the traditional asset pricing models. We have also observed partial evidence favoring both behavioural and rational pricing explanations—the two widely acknowledged explanations in the literature behind this puzzle. On our further query in this connection, we have not seen any significant change in the puzzle due to the recent enactment of the Insolvency and Bankruptcy Code.     

Analysts’ Forecast Dispersion and Stock Returns: A Quantile Regression Approach

Prior research has not provided conclusive evidence on the association between analysts’ forecast dispersion and subsequent stock returns. Since inferences from prior studies may be confounded by research design choices, we use the quantile regression (QR) approach and assess the hidden non-monotonic relations between dispersion and stock returns within a broader sample. The empirical results show that dispersion is negatively associated with subsequent stock returns when the latter is in lower quantiles. In contrast, when the stock returns are in high quantiles, dispersion is positively associated with subsequent stock returns. Moreover, the association between dispersion and stock returns is trivial when the mid-range return quantiles are concerned. These non-uniform connections between dispersion and stock returns reflect the different status of overpricing correction process. Our findings help to reconcile the mixed results reported by prior research concerning the relation between analysts’ forecast dispersion and subsequent stock returns.     

Conditional value-at-risk: Aspects of modeling and estimation

This paper considers flexible conditional (regression) measures of market risk. Value-at-Risk modeling is cast in terms of the quantile regression function – the inverse of the conditional distribution function. A basic specification analysis relates its functional forms to the benchmark models of returns and asset pricing. We stress important aspects of measuring the extremal and intermediate conditional risk. An empirical application characterizes the key economic determinants of various levels of conditional risk. Received: September 30, 1999/Revised version: November 20, 2000     

Idiosyncratic risk matters! A regime switching approach

The evidence on the inter-temporal relation between idiosyncratic risk and future stock returns is conflicting and confusing. We shed new light on the issue using a more flexible econometric approach based on [Hamilton, J.D. 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57, 357–384.] regime switching model that accommodates the parameter instability of the forecasting relation between returns and financial variables. We find strong evidence suggesting that idiosyncratic risk is related to future stock market returns only in the low variance regime.     

Revisiting the risk-return relation in the Chinese stock market: Decomposition of risk premium and volatility feedback effect

The empirical results of the risk-return relationship are mixed for both mature and merging markets. In this paper, we develop a new volatility model to revisit the risk-return relation of the aggregate stock market index by extending the Realized GARCH model of Hansen et al. (2012) with the Wang and Yang (2013) framework, in which the overall risk-return relation is decomposed into a risk premium and a volatility feedback effect. An empirical analysis of three major Chinese stock indices reveals positive risk premium and negative volatility feedback effect, and those findings are stable across different markets and sub-samples. However, their relative magnitudes differ between markets and varies through time.     

基于门限分位点回归的条件VaR风险度量

门限分位点回归模型是线性分位点回归模型的改进,是一种更加客观实际的非线性估计方法。利用该模型实证分析了单只股票(民生银行)的条件VaR。选择一种流动性风险指标作为条件,经过分析发现,由门限分位点回归模型得到结果能够更好地描述市场,也能更好地预测市场风险。     

Recursive equilibrium in endogenous growth models with incomplete markets

This paper analyzes the existence of recursive equilibria in a class of convex growth models with incomplete markets. Households have identical CRRA-preferences, production displays constant returns to scale with respect to physical and human capital, and all markets are competitive. There are aggregate productivity shocks that affect aggregate returns to physical and human capital investment (stock returns and wages), and there are idiosyncratic shocks to human capital (idiosyncratic depreciation shocks) that only affect individual human capital returns. Aggregate and idiosyncratic shocks follow a joint Markov process. Conditional on the aggregate state, idiosyncratic shocks are independently distributed over time and identically distributed across households. Finally, households have the opportunity to trade assets in zero net supply with payoffs that depend on the aggregate shock, but markets are incomplete in the sense that there are no assets with payoffs depending on idiosyncratic shocks. It is shown that there exists a recursive equilibrium for which equilibrium prices (returns) only depend on the exogenous aggregate shock variable (the wealth distribution is not a relevant state variable). Moreover, the allocation associated with this recursive equilibrium is identical to the equilibrium allocation of an economy in which households live in autarky and face both aggregate and idiosyncratic risk.I would like to thank for helpful comments Peter Howitt, Bob Lucas, Michael Magill, Tomo Nakajima, Herakles Polemarchakis, Martine Quinzii, Kevin Reffett, an anonymous referee, and seminar participants at various universities and conferences.     

An empirical analysis of the downside risk-return trade-off at daily frequency

This paper considers the downside-risk aversion of investors as an explanation for the risk-return trade-off. We test empirically this hypothesis using intraday data along with the recent measure of downside-risk called realized semivariance developed in Barndorff-Nielsen et al. (2010). The empirical analysis over the period 1996–2008 provides evidence of a significant relation between semivariance and excess returns at the daily frequency. To gain better understanding of the relation between returns and downside-risk, we investigate the statistical relation between a new measure of conditional asymmetry, namely the ratio of the downside realized semivariance over the variance, and obtain a revealing pattern using a rolling window framework able to link asymmetry in the distribution to future returns. In particular, the asymmetry measure becomes significant when the past realized variance is not significant any more thereby providing insights about a possible change in the behavior of investors.     

Disagreement and the risk-return relation

Disagreement is an important behavioral factor in financial market, and this paper investigates the impact of disagreement on the risk-return relation. We construct disagreement of crowded trades (DCT) index to measure disagreement, and discover that DCT has a significant impact on the risk-return relation. Furthermore, DCT change has a time-varying effect on the risk-return relation. When DCT change is negative, the risk-return relation is significant and negative; when DCT change is positive, the risk-return relation is significantly positive. When we use different conditional variance models and different market portfolios, such results are still robust. Moreover, our empirical results have important practical implications for asset allocation decisions.     

Global idiosyncratic risk moments

We investigate a global cross-sectional relation between idiosyncratic risk moments and expected stock returns by suggesting three global idiosyncratic volatility, skewness, and kurtosis risk factors. We also suggest two global small minus big and high minus low risk proxies for estimating return residuals of the test assets from a global asset pricing model. To perform robustness checks, we suggest other four global risk factors of momentum, leverage, bid-ask spread, and liquidity. We find a significant negative relation between stock portfolio returns and the global moments, and the cross section of stock returns reflects a significant negative price of risk for global idiosyncratic skewness (?0.13%) and idiosyncratic volatility (?1.85%) and a positive and significant price of risk for global idiosyncratic kurtosis. We find that our suggested risk factors are key drivers of risk premia in stock market and are robust to various checks. These factors also can forecast the gross domestic product growth over the sample period.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

Under Performers and Over Achievers: A Quantile Regression Analysis of Growth

Numerous papers have searched for empirical linkages between long run economic growth and a myriad of economic, socio-political and environmental factors. Most of these studies use ordinary least-squares regression or panel regression analysis on a sample of countries and therefore consider the behaviour of growth around the mean of the conditional distribution. We extend the literature by using quantile regression to analyse long-term growth at a variety of points in the conditional distribution. By using this approach, we identify the determinants of growth for under performing countries relative to those for over achieving countries.     

Does economic policy uncertainty matter for commodity market in China? Evidence from quantile regression

ABSTRACT

This paper investigates the effect of economic policy uncertainty (EPU) on China’s agricultural and metal commodity futures returns across quantiles. We address this issue using the panel quantile regression approach, which allows for a more complete analysis of various conditions in the commodity market (i.e. bearish, normal, and bullish markets). Our empirical results reveal that domestic EPU shocks have a significantly negative effect on agricultural futures returns in bearish markets and a significantly positive effect on metal futures returns in bullish markets. The impacts of both domestic and U.S. EPU shocks on commodity markets are heterogeneous across quantiles and are sector specific. Additionally, by isolating positive and negative EPU shocks, the regression and test results indicate an asymmetric response of commodity futures prices in bullish markets. Moreover, our findings indicate that the metal futures market has a higher financialisation level than the agricultural futures market. The findings can be utilized by policymakers and investors.     

Does international oil volatility have directional predictability for stock returns? Evidence from BRICS countries based on cross-quantilogram analysis

While numerous studies have investigated the relationship between oil volatility and stock returns, it is surprising that little research has examined the quantile dependence and directional predictability from oil volatility to stock returns in BRICS (Brazil, Russia, India, China, and South Africa) countries. We address this issue by using the cross-quantilogram model proposed by Han et al. (2016). The empirical results show that, overall, oil volatility has a directional predictability for the stock returns in BRICS countries. When the oil volatility is in a low quantile (lower than its 0.1 quantiles), it is less likely to show either a large loss or a large gain in the stock market. In contrast, there is an increased likelihood of either large loss or a large gain in the stock market when the oil volatility is in a high quantile (higher than its 0.9 quantiles). The directional predictability from the oil volatility to stock returns depends on the net position of oil imports and exports of these BRICS countries in the oil market. The net oil exporters (Russia and Brazil) are less likely to have large gains and large losses in the stock market than are the net oil importers (India, China, and South Africa) when the oil volatility is in a low quantile. The net oil exporters are more likely to have large gains and large losses than are the net oil importers when the oil volatility is in a high quantile. The results are robust to change in the variable of oil volatility and the sample interval.     

The impacts of investor sentiment and price uncertainty on the effects of open-market share repurchase announcements

This study probes into relationship between investor sentiment and cumulative abnormal returns (CAR) of share repurchase announcements, and it treats market return as threshold variable. By threshold regression model, it tries to find the effect of market situations on relation between investor sentiment and CAR. According to empirical result, in share market of Taiwan, investor sentiment can explain CAR. When share market is extremely pessimistic (market return lower than ?16.0053%), relation between investor sentiment and CAR will change to some degree. In addition, relation between price risk of announcement company and CAR will disappear with the extremely pessimistic situation of market.     

The use of quantile regressions in estimating gender wage differentials: a case study of the Philippines

Quantile regressions are used to analyse the size, components, and nature of the gender-earnings differentials as well as the returns to education by gender in the Philippines, over the entire conditional distribution of earnings, using data from the 1999 APIS survey. This approach allows more focused conclusions about the nature of gender-earnings differentials. Incorporating the quantile regression approach to the Oaxaca–Blinder decomposition allows the localization of gender discrimination to particular quantiles of the earnings distribution and permits more targeted policy intervention.     

Temporal aggregation and the strength of the association between securities' risk and return

In this letter we examine the effect of time aggregation on the correlation between securities' average returns and their corresponding level of systematic risk as specified by the Capital Asset Pricing Model (CAPM). We demonstrate that the R-square of the empirical Security Market Line is highly sensitive to the length of securities' return interval. Consequently, extreme care should be displayed in interpreting statistical results obtained from securities' risk-return studies.     

Currency returns and downside risk: Debt,volatility, and the gap from benchmark values

The paper considers competing portfolio-balance specifications of currency returns, including one based on expected utility theory and another on prospect theory. The prospect theory specification relates downside risk to the gap between the exchange rate and its benchmark value. The empirical analysis uses survey data on exchange rate expectations to test directly the models’ predictions concerning ex ante excess returns. It also relies on the cointegrated VAR framework, which is well suited for testing competing models and dealing with unit roots. Like earlier studies, we find little support for the expected utility theory model in three major currency markets. By contrast, the prospect theory model’s predictions are largely borne out in the data, including those about sign reversals. We find the strongest support for a hybrid model that incorporates the risk factors of both models.     

Earnings distribution of Cuban immigrants in the USA: evidence from quantile regression with sample selection

This article analyses the conditional earnings distribution for Cuban immigrants in the USA considering Buchinsky sample selection in a quantile regression model. The test proposed by Huber and Melly to test the independence between error terms and regressors (conditional on the selection probability) is also considered. This is the first attempt in the migration literature to use quantile regression with sample selection. The data used come from the US American Community Survey. The results show that the hypothesis of conditional independence is not rejected, and increments in earnings associated with the usual socioeconomic characteristics in labour studies vary between the cohorts considered. The main conclusions are that a decline in returns from education may be a sign that a high level of education no longer provides a competitive advantage and that being a black person is associated with substantially lower earnings regardless of the individuals’ position in the earnings distribution. This may explain why, historically, comparatively fewer black Cubans have made the decision to emigrate to the USA because of a lack of economic incentives.     

The momentum effect on Chinese real estate stocks: Evidence from firm performance levels

This paper investigates the momentum effects under different firm performance levels for Chinese real estate stocks using quantile regression with a dummy variable estimator. This paper finds that regardless of the momentum horizon, the momentum effects are positive under high-performing individual stocks, but they are negative under low-performing individual stocks. While prior literature only finds that this asymmetric phenomenon appears under different market states, and the findings on different horizons are inconsistent. Furthermore, this paper finds that the positive (negative) momentum effect under high (low) firm performance levels is stronger than that under bullish (bearish) markets. This implies that superior (inferior) fundamental business performance and bullish (bearish) markets can cause the stock prices to go up (down); however, the effect of the former is stronger than that of the latter. Moreover, this paper finds that the relation between future returns and past turnover ratios is positively correlated under high-performing stocks, but negatively correlated under low-performing stocks. Based on the above findings, this paper regards past turnover ratios as a leading indicator of stock returns and suggests two profitable investment portfolios which are superior to the average returns of real estate stocks.