scholarly journals PORTFOLIO SELECTION PROBLEMS CONSISTENT WITH GIVEN PREFERENCE ORDERINGS

2013 ◽  
Vol 16 (05) ◽  
pp. 1350029 ◽  
Author(s):  
SERGIO ORTOBELLI LOZZA ◽  
HAIM SHALIT ◽  
FRANK J. FABOZZI
Keyword(s):  
Portfolio Selection ◽  
Optimization Problems ◽  
Risk Measures ◽  
Tracking Error ◽  
Selection Problems ◽  
Error Measures ◽  
Ex Post ◽  
The Us ◽  
Post Impact ◽  
Recent Financial Crisis

This paper theoretically and empirically investigates the connection between portfolio theory and ordering theory. In particular, we examine three different portfolio problems and the respective orderings used to rank investors' choices: (1) risk orderings, (2) variability orderings, and (3) tracking-error orderings. For each problem, we discuss the properties of the risk measures, variability measures, and tracking-error measures, as well as their consistency with investor choices. Finally, for each problem, we propose an empirical application of several admissible portfolio optimization problems using the US stock market. The proposed empirical analysis permits us to evaluate the ex-post impact of the optimal choices, thereby deriving completely different investors' preference orderings during the recent financial crisis.

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

scholarly journals Robustness in the Optimization of Risk Measures

2021 ◽  
Author(s):  
Paul Embrechts ◽  
Alexander Schied ◽  
Ruodu Wang
Keyword(s):  
Value At Risk ◽  
Optimization Problem ◽  
Financial Regulation ◽  
Optimization Problems ◽  
Risk Measures ◽  
Risk Measurement ◽  
Loss Functions ◽  
Insurance Regulation ◽  
Convex Risk Measures ◽  
Comparative Advantages

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.

scholarly journals Tracking error: Ex ante versus ex post measures

2001 ◽  
Vol 2 (3) ◽  
pp. 241-246 ◽  
Author(s):  
SE Satchell ◽  
S Hwang
Keyword(s):  
Tracking Error ◽  
Ex Post ◽  
Ex Ante
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