{"id":"W4297158746","doi":"10.3390/jrfm15100427","title":"Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cryptocurrency; Portfolio; Portfolio optimization; Kurtosis; Econometrics; Expected shortfall; Skewness; Spectral risk measure; Value at risk; Computer science; Risk measure; Volatility (finance); Economics; Actuarial science; Risk management; Financial economics; Statistics; Mathematics; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003149808,0.001086089,0.00128767,0.0009330244,0.000301305,0.001966743,0.0007431329,0.0009685812,0.0008080947],"category_scores_gemma":[0.008393333,0.0005094971,0.0007043781,0.0007738129,0.0005982253,0.001719315,0.0009797384,0.001145117,0.0001220763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119443,"about_ca_system_score_gemma":0.001378392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002550044,"about_ca_topic_score_gemma":0.001508682,"domain_scores_codex":[0.9992121,0.0002948555,0.00004807948,0.0001415089,0.0002108249,0.00009256018],"domain_scores_gemma":[0.9960755,0.002804802,0.0004221481,0.0001489829,0.0003865046,0.0001621104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004908056,0.00004850954,0.001742462,0.00004308163,0.00006273776,0.00006126802,0.00001749736,0.9732978,0.0007390631,0.006966779,0.0002834253,0.01668825],"study_design_scores_gemma":[0.000004559158,0.00002498292,0.0002745388,0.000005380425,0.00001038319,0.000009684081,0.000004572246,0.9966788,0.0002312415,0.002665944,0.00008590899,0.00000399396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1805591,0.001252627,0.8146239,0.0004571026,0.00004716417,0.00009774189,0.0001114489,0.0001777684,0.002673164],"genre_scores_gemma":[0.9365737,0.0006026101,0.06103975,0.00006374055,0.00003850694,0.00009804279,0.0001565892,0.00004236838,0.001384619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003149808,"threshold_uncertainty_score":0.01665795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04558299711136072,"score_gpt":0.231553443598492,"score_spread":0.1859704464871313,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}