Modeling the conditional distribution of financial returns with asymmetric tails
提出一种允许左右尾部指数不同且波动率时变的条件密度模型，发现全球股指收益中尾部不对称普遍存在，且可能被误认为偏度；该模型在资产配置和风险管理中优于传统偏度模型。
Summary This paper proposes a conditional density model that allows for differing left/right tail indices and time‐varying volatility based on the dynamic conditional score (DCS) approach. The asymptotic properties of the maximum likelihood estimates are presented under verifiable conditions together with simulations showing effective estimation with practical sample sizes. It is shown that tail asymmetry is prevalent in global equity index returns and can be mistaken for skewness through the center of the distribution. The importance of tail asymmetry for asset allocation and risk premia is demonstrated in‐sample. Application to portfolio construction out‐of‐sample is then considered, with a representative investor willing to pay economically and statistically significant management fees to use the new model instead of traditional skewed models to determine their asset allocation.