Optimal Systemic Risk Bailout: A PGO Approach Based on Neural Network

The bailout strategy is crucial to cushion the massive loss caused by systemic risk in the financial system. There is no closed-form formulation of the optimal bailout problem, making solving it difficult. In this paper, we regard the issue of the optimal bailout (capital injection) as a black-box optimization problem, where the black box is characterized as a fixed-point system that follows the E-N framework for measuring the systemic risk of the financial system. We propose the so-called ``Prediction-Gradient-Optimization'' (PGO) framework to solve it, where the ``Prediction'' means that the objective function without a closed-form is approximated and predicted by a neural network, the ``Gradient'' is calculated based on the former approximation, and the ``Optimization'' procedure is further implemented within a gradient projection algorithm to solve the problem. Comprehensive numerical simulations demonstrate that the proposed approach is promising for systemic risk management

Integrating Different Informations for Portfolio Selection

Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior. The proposed combined estimation adaptively harmonizes these two types of information based on the degree of market efficiency and responds quickly at turning points of the market. Both simulation experiments and a global empirical test confirm that the approach is a flexible and robust forecasting tool and is applicable to various capital markets with different degrees of efficiency

Portfolio Selection under Distributional Uncertainty: A Relative Robust CVaR in Portfolio Management

Robust optimization, one of the most popular topics in the field of optimization and control since the late 1990s, deals with an optimization problem involving uncertain parameters. In this paper, we consider the relative robust conditional value-at-risk portfolio selection problem where the underlying probability distribution of portfolio return is only known to belong to a certain set. Our approach not only takes into account the worst-case scenarios of the uncertain distribution, but also pays attention to the best possible decision with respect to each realization of the distribution. We also illustrate how to construct a robust portfolio with multiple experts (priors) by solving a sequence of linear programs or a second-order cone program

Chance Constrained Programs with Gaussian Mixture Models

10.1080/24725854.2021.2001608IISE Transactions54121117-113

Robust portfolio selection under downside risk measures

We investigate a robust version of the portfolio selection problem under a risk measure based on the lower-partial moment (LPM), where uncertainty exists in the underlying distribution. We demonstrate that the problem formulations for robust portfolio selection based on the worst-case LPMs of degree 0, 1 and 2 under various structures of uncertainty can be cast as mathematically tractable optimization problems, such as linear programs, second-order cone programs or semidefinite programs. We perform extensive numerical studies using real market data to reveal important properties of several aspects of robust portfolio selection. We can conclude from our results that robustness does not necessarily imply a conservative policy and is indeed indispensable and valuable in portfolio selection.Portfolio selection, Downside risk, Lower-partial moment, Robust optimization,

Portfolio selection under distributional uncertainty: A relative robust CVaR approach

Robust optimization, one of the most popular topics in the field of optimization and control since the late 1990s, deals with an optimization problem involving uncertain parameters. In this paper, we consider the relative robust conditional value-at-risk portfolio selection problem where the underlying probability distribution of portfolio return is only known to belong to a certain set. Our approach not only takes into account the worst-case scenarios of the uncertain distribution, but also pays attention to the best possible decision with respect to each realization of the distribution. We also illustrate how to construct a robust portfolio with multiple experts (priors) by solving a sequence of linear programs or a second-order cone program.Conditional value-at-risk Worst-case conditional value-at-risk Relative robust conditional value-at-risk Portfolio selection problem Linear programming

Y-type partial duplication of a vaginal ectopic ureter with ipsilateral hypoplastic pelvic kidney and bicornuate uterus

We present a case of vaginal ectopic ureter with ipsilateral partial duplication of the upper ureter (Y-type ureter), ipsilateral hypoplastic pelvic kidney and bicornuate uterus in a 20-year-old woman, who presented with mild urinary incontinence since infancy. Ultrasonography, computed tomography and intravenous pyelography examination showed a left kidney with no evidence of a right kidney. Cystourethroscopy showed absence of the right hemitrigone. Magnetic resonance (MR) urography demonstrated the presence of a bicornuate uterus, an ectopic dysplastic right kidney in the pelvic cavity, and a right ureter that terminated in the fornix vaginae. The patient underwent right nephroureterectomy, and urinary continence was restored completely. Although congenital malformations of the urinary tract are frequently associated with genital tract abnormalities, this is, to the best of our knowledge, the first report of the coexistence of these anomalies in an individual. Our report also highlights the importance of MR urography in the diagnosis of such rare and complex anomalies

Portfolio risk optimisation and diversification using swarm intelligence

The ongoing global economic turmoil has got the asset management industry look into new ways of financial risk management. Portfolio optimisation and risk budgeting are at the heart of most computational finance studies by academics and practitioners. In this paper, we introduce and analyse a method to construct an equity portfolio based on decomposition of marginal asset risk contribution of each stock in a given universe and then formulate a diversification problem for unsystematic risk as an optimisation problem. We have illustrated the performance of our method by comparing with another diversification technique, known as the Risk Parity portfolio, and then benchmark our results against the global major indices

High- vs Low-power Holmium Laser Lithotripsy: A Prospective, Randomized Study in Patients Undergoing Multitract Minipercutaneous Nephrolithotomy

OBJECTIVE To determine the efficacy and safety of high-power holmium: yttrium aluminum-garnet (Ho: YAG) laser lithotripsy for multitract modified minimally invasive percutaneous nephrolithotomy (MPCNL) in the treatment of patients with large staghorn renal calculi. METHODS A randomized, prospective study was conducted. Two-hundred seventy-three consecutive patients (291 renal units) with large staghorn renal calculi were randomized to undergo multitract MPCNL with 30-W low-power or 70-W high-power Ho: YAG laser lithotripsy. Both groups were compared in terms of perioperative findings and postoperative outcomes, including procedure time, stone-free rate, length of hospital stay, transfusion rates, renal function recovery, and other complications. RESULTS The average patient age was 49.2 years (range 22-73) and mean stone size was 5.54 +/- 0.7 cm. The 2 groups had some comparable perioperative findings and outcome, including tracts required per operated renal unit (n), blood loss, postoperative fever, postoperative hospital stay, stone-free rate, and improvement of operated renal function. The operation time in the high-power group was significantly shorter than that in the low-power group (129.20 +/- 17.2 vs 105.18 +/- 14.2, P <.01). CONCLUSION A combination of multitract MPCNL and high-power Ho: YAG laser lithotripsy can greatly decrease the operative time without increasing the intraoperative complications or delaying postoperative renal function recovery when compared with low-power Ho: YAG laser lithotripsy. UROLOGY 79: 293-297, 2012. (C) 2012 Elsevier Inc