{"id":"W4382583755","doi":"10.3390/jrfm16070312","title":"Estimating Value-at-Risk in the EURUSD Currency Cross from Implied Volatilities Using Machine Learning Methods and Quantile Regression","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Quantile regression; Value at risk; Artificial neural network; Boosting (machine learning); Currency; Volatility (finance); Quantile; Regression; Gradient boosting; Computer science; Artificial intelligence; Machine learning; Economics; Statistics; Risk management; Mathematics; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004623001,0.0001467719,0.000392111,0.0002849762,0.0004055607,0.0001316829,0.0001568553,0.00006466161,0.00002417618],"category_scores_gemma":[0.0007840716,0.0001166647,0.00008723389,0.0002979445,0.00005586222,0.0001739847,0.0002166137,0.0004202356,0.000001637129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004967547,"about_ca_system_score_gemma":0.000007608235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001174391,"about_ca_topic_score_gemma":0.00007768317,"domain_scores_codex":[0.9984965,0.0001832606,0.0007850472,0.0002437335,0.00007416862,0.0002172699],"domain_scores_gemma":[0.9984584,0.0004447555,0.0008756984,0.0001543053,0.00002284256,0.00004398287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008011268,0.00002539724,0.9202852,0.0000481153,0.0000160454,0.00001447129,0.002007058,0.0009353866,0.000002962641,0.003210026,0.00002536879,0.07334991],"study_design_scores_gemma":[0.0004057978,0.00003238559,0.4981926,0.00005763497,0.0000173026,0.000002456317,0.0001618263,0.4496024,7.653466e-7,0.04843559,0.003007848,0.00008344118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9469726,0.003436593,0.0485937,0.00003471941,0.0004119374,0.0001220698,0.00009511308,0.000008421577,0.0003248428],"genre_scores_gemma":[0.9660927,0.004193044,0.02950173,0.00002102903,0.000110497,0.000002878971,0.000007086732,0.00001254991,0.00005849456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.448667,"threshold_uncertainty_score":0.4757448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03328962511009013,"score_gpt":0.3154697436603803,"score_spread":0.2821801185502901,"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."}}