Showing 5 of total 5 results (show query)
geobosh
cvar:Compute Expected Shortfall and Value at Risk for Continuous Distributions
Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator or probability density function. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.
Maintained by Georgi N. Boshnakov. Last updated 2 years ago.
expected-shortfalllocations-scale-transformationsquantilequantile-functionsriskvalue-at-risk
6 stars 8.05 score 27 scripts 52 dependents
dppalomar
riskParityPortfolio:Design of Risk Parity Portfolios
Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>. F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. <doi:10.2139/ssrn.2297383>. T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Maintained by Daniel P. Palomar. Last updated 2 years ago.
optimizationportfolioriskrisk-paritycpp
108 stars 7.64 score 35 scripts 2 dependents
hneth
riskyr:Rendering Risk Literacy more Transparent
Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent.
Maintained by Hansjoerg Neth. Last updated 10 months ago.
2x2-matrixbayesian-inferencecontingency-tablerepresentationriskrisk-literacyvisualization
19 stars 7.18 score 80 scripts
ardiad
RiskPortfolios:Computation of Risk-Based Portfolios
Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Maintained by David Ardia. Last updated 4 years ago.
covarianceoptimizationportfolioportfolio-optimizationrisk
51 stars 5.05 score 44 scripts
feakster
QDiabetes:Type 2 Diabetes Risk Calculator
Calculate the risk of developing type 2 diabetes using risk prediction algorithms derived by 'ClinRisk'.
Maintained by Benjamin G. Feakins. Last updated 4 years ago.
clinriskdiabetesdiabetes-predictiondiabetes-riskdiabetes-risk-predictionprognosticqdiabetes-algorithmqtoolsrisk
7 stars 3.85 score 5 scripts