Seeking Skewness

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Using detailed disaggregated Swedish household administrative data on portfolio holdings and labor income, this paper investigates retail investors’ behavior of seeking skewness in their portfolio choice. I develop a model of rational portfolio choice in which investors optimally hold portfolios with a (positively) skewed return distribution to hedge against (negatively) skewed labor income risk. I find empirical support for the model’s predictions. I find that investors trade off their portfolio’s Sharpe ratio against higher skewness, which explains the suboptimal Sharpe ratio found in previous studies. I also find that skewness seeking is more pronounced for investors with (i) higher overall risk in their labor income, (ii) higher downside risk in their labor income, and (iii) less wealth. Further, I find that investors hold more assets that provide insurance against the time-varying downside risk in their labor income.

Countercyclical Income Risk and Portfolio Choices: Evidence from Sweden
with Sylvain Catherine and Paolo Sodini

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Using Swedish administrative panel data, we show that workers facing higher left-tail
income risk when equity markets perform poorly are less likely to participate in the stock market and, conditional on participation, have lower equity shares. We call this measure of income risk “cyclical skewness” and show that it is a better predictor of equity holdings than other income risk measures such as variance, covariance, and countercyclical volatility. In line with theory, our findings are stronger at the beginning of the life-cycle, are not significant for individuals with substantial financial wealth, and are stronger when we focus on permanent income shocks. Finally, within their risky portfolio, workers put less weight on securities generating negative returns when their own income risk increases.

Multifractal Volatility with Shot-Noise Component
with Laurent Calvet and Jiawen Xu

Empirical evidence shows that volatility jumps upwards due to shocks to the economy, then gradually decreases as the uncertainty resolved. This jump and decay pattern in volatility happens at different frequencies depending on the frequency nature of the information. This paper offers a volatility model based on the Markov Switching Multifractal (MSM) that captures both the shot-noise effect and the multifractal properties in volatility process. The model introduces an asymmetric component into the multifractal framework, and captures, in a parsimonious way, the jump and decay pattern in different frequencies. Shot-Noise Multifractal (SNM) model matches well empirical data in terms of the number and the size of jumps in the volatility process. Moreover, regime-switching in different frequencies captures dynamics in different frequencies and generates fatter tails and clustering. Multifractal approach with close form likelihood function simplifies to a large extent, the treatment of high-frequency data. The SNM model outperforms the best MSM both in- and out-of-sample.