Yakup Eser Arısoy

Do Stock Market Investors Really Care About the Skewness of Long-Ahead Returns?

We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.

Does Aggregate Uncertainty Explain Size and Value Anomalies

This paper examines the impact of aggregate uncertainty on return dynamics of size and book-to-market ratio sorted portfolios. Using VVIX as a proxy for aggregate uncertainty, and controlling for market risk, volatility risk, correlation risk and the variance risk premium, we document significant portfolio return exposures to aggregate uncertainty. In particular, portfolios that contain small and value stocks have significant and negative uncertainty betas, whereas portfolios of large and growth stocks exhibit positive and significant uncertainty betas. Using a quasi-natural experimental setting around the financial crisis, we confirm the differential sensitivity of small versus big and value versus growth portfolios to aggregate uncertainty. We posit that due to their negative uncertainty betas, uncertainty-averse investors demand extra compensation to hold small and value stocks. Our results offer an uncertainty-based explanation to size and value anomalies.

Do Stock Markets Price Expected Stock Skewness? New Evidence from Quantile Regression Skewness Forecasts

We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.

Aggregate Volatility Expectations and Threshold CAPM

We propose a volatility-based capital asset pricing model (V-CAPM) in which asset betas change discretely with respect to changes in investors’ expectations regarding near-term aggregate volatility. Using a novel measure to proxy uncertainty about expected changes in aggregate volatility, i.e. monthly range of the VIX index (RVIX), we find that portfolio betas change significantly when uncertainty about aggregate volatility expectations is beyond a certain threshold level. Due to changes in their market betas, small and value stocks are perceived as riskier than their big and growth counterparts in bad times, when uncertainty about aggregate volatility expectations is high. The proposed model yields a positive and significant market risk premium during periods when investors do not expect significant uncertainty in near-term aggregate volatility. Our findings support a volatility-based time-varying risk explanation.

Do Stock Markets Price Expected Stock Skewness? New Evidence from Quantile Regression based Skewness Forecasts

We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.We use density forecasts derived from recursively estimated quantile regressions to calculate a forecast of the physical skewness of an assets future return distribution. The forecast is unbiased and efficient, and it can easily be adapted to forecast the skewness of returns calculated over any conceivable return interval. Using Neubergers (2012) realized physical skewness, we show that our quantile regression skewness forecast outperforms other variables proposed in the literature. Despite this, it does not condition the cross-section of future stock returns, neither independently nor when combined with other forecasts. Overall, we cast doubt on whether stock markets price expected stock skewness.