6,056 research outputs found
Generative CVaR Portfolio Optimization with Attention-Powered Dynamic Factor Learning
The dynamic portfolio construction problem requires dynamic modeling of the
joint distribution of multivariate stock returns. To achieve this, we propose a
dynamic generative factor model which uses random variable transformation as an
implicit way of distribution modeling and relies on the Attention-GRU network
for dynamic learning and forecasting. The proposed model captures the dynamic
dependence among multivariate stock returns, especially focusing on the
tail-side properties. We also propose a two-step iterative algorithm to train
the model and then predict the time-varying model parameters, including the
time-invariant tail parameters. At each investment date, we can easily simulate
new samples from the learned generative model, and we further perform CVaR
portfolio optimization with the simulated samples to form a dynamic portfolio
strategy. The numerical experiment on stock data shows that our model leads to
wiser investments that promise higher reward-risk ratios and present lower tail
risks
Generative Learning of Heterogeneous Tail Dependence
We propose a multivariate generative model to capture the complex dependence
structure often encountered in business and financial data. Our model features
heterogeneous and asymmetric tail dependence between all pairs of individual
dimensions while also allowing heterogeneity and asymmetry in the tails of the
marginals. A significant merit of our model structure is that it is not prone
to error propagation in the parameter estimation process, hence very scalable,
as the dimensions of datasets grow large. However, the likelihood methods are
infeasible for parameter estimation in our case due to the lack of a
closed-form density function. Instead, we devise a novel moment learning
algorithm to learn the parameters. To demonstrate the effectiveness of the
model and its estimator, we test them on simulated as well as real-world
datasets. Results show that this framework gives better finite-sample
performance compared to the copula-based benchmarks as well as recent similar
models
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