Omega Performance Measure and Portfolio Insurance

2010 ◽  
Author(s):  
Philippe Bertrand ◽  
Jean-Luc Prigent
Keyword(s):  
Performance Measure ◽  
Portfolio Insurance

MULTIPLIER OPTIMIZATION FOR CONSTANT PROPORTION PORTFOLIO INSURANCE (CPPI) STRATEGY

2020 ◽  
Vol 23 (02) ◽  
pp. 2050011
Author(s):  
OLGA BIEDOVA ◽  
VICTORIA STEBLOVSKAYA
Keyword(s):  
Numerical Optimization ◽  
Performance Measure ◽  
Portfolio Insurance ◽  
Admissible Range ◽  
Constant Proportion ◽  
Black Scholes ◽  
Gap Risk ◽  
Constant Proportion Portfolio Insurance ◽  
Omega Ratio ◽  
Optimal Multiplier

Constant proportion portfolio insurance (CPPI) strategy is a very popular investment solution which provides an investor with a capital protection as well as allows for an equity market participation. In this paper, we propose a two-step approach to the numerical optimization of the CPPI main parameter, multiplier. First, we identify an admissible range of the multiplier values by controlling the shortfall probability (chosen as a measure of the gap risk). Second, within the admissible range, we choose the optimal multiplier value with respect to the omega ratio (chosen as a performance measure). We illustrate the performance of our optimization algorithm on simulated CPPI paths in the Black–Scholes environment with discrete trading as well as on the historical S&P500 data using the block-bootstrap simulations.

Omega performance measure and portfolio insurance

2011 ◽  
Vol 35 (7) ◽  
pp. 1811-1823 ◽  
Author(s):  
Philippe Bertrand ◽  
Jean-luc Prigent
Keyword(s):  
Performance Measure ◽  
Portfolio Insurance

Work-Performance Measure

2011 ◽  
Author(s):  
Yih-teen Lee ◽  
Alfred Stettler ◽  
John Antonakis
Keyword(s):  
Work Performance ◽  
Performance Measure

Hotel Performance Measure

2019 ◽  
Author(s):  
Erick Pusck Wilke ◽  
Benny Kramer Costa ◽  
Otávio Bandeira De Lamônica Freire ◽  
Manuel Portugal Ferreira
Keyword(s):  
Performance Measure ◽  
Hotel Performance

A Universal Performance Measure

CFA Digest ◽  
2003 ◽  
Vol 33 (1) ◽  
pp. 51-52
Author(s):  
Frank T. Magiera
Keyword(s):  
Performance Measure

Assessing the Costs of Portfolio Insurance

1987 ◽  
Vol 43 (3) ◽  
pp. 27-37 ◽  
Author(s):  
Richard J. Rendleman ◽  
Richard W. McEnally
Keyword(s):  
Portfolio Insurance

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

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