We propose parametric tests for serial correlation in levels and squares that exploit the non-normality of financial returns. Our are robust to distributional misspecification. Furthermore, our mean predictability can be robustified against time-varying volatility. Local power analyses confirm their gains over existing methods, while Monte Carlo exercises assess finite sample reliability. apply...

We consider a mean-variance portfolio selection problem in financial market with contagion risk. The risky assets follow jump-diffusion model, which jumps are driven by multivariate Hawkes process mutual-excitation effect. feature of the captures risk sense that each price jump an asset increases likelihood future not only same but also other assets. apply stochastic maximum principle, backward...

Many problems in quantitative finance involve both predictive forecasting and decision-based optimization. Traditionally, models are optimized with unique prediction-based objectives constraints, therefore unaware of how those predictions will ultimately be used the context their final We present a stochastic optimization framework for integrating regression based mean-variance portfolio settin...

The paper investigates quadratic hedging in a general semimartingale market that does not necessarily contain risk-free asset. An equivalence result for with and without numeraire change is established. This permits direct computation of the optimal strategy choosing reference asset and/or performing change. New explicit expressions strategies are obtained, featuring use oblique projections pro...

To verify the effectiveness of STU and STS for measures of muscle strength, power and balance, we computed withinsubject standardized mean differences as SMDw = (mean pre-test value mean post-test value)/standard deviation pre-test value, and between-subject standardized mean differences as SMDb = (mean post-test value in intervention group mean post-test value in control group)/pooled variance...