{"id":"W4406110248","doi":"10.3390/jrfm18010018","title":"Economic Resilience in Post-Pandemic India: Analysing Stock Volatility and Global Links Using VAR-DCC-GARCH and Wavelet Approach","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Volatility (finance); Autoregressive conditional heteroskedasticity; Stock (firearms); Wavelet; Pandemic; Econometrics; Economics; Financial economics; Coronavirus disease 2019 (COVID-19); Computer science; Geography; Artificial intelligence; Medicine","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":[0.001896815,0.0001640907,0.0005373169,0.0004255018,0.0001524909,0.0001090659,0.0001431201,0.0001608866,0.000006520384],"category_scores_gemma":[0.0001210399,0.0001753919,0.00007351237,0.0002829232,0.0001042273,0.0002352989,0.0002113889,0.0004150735,2.580595e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000241914,"about_ca_system_score_gemma":0.00005449114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005646472,"about_ca_topic_score_gemma":0.0002899646,"domain_scores_codex":[0.9984185,0.00005089293,0.0008671705,0.0003770347,0.00004624197,0.0002401954],"domain_scores_gemma":[0.9991614,0.00006344042,0.0004908502,0.0001715805,0.0000338032,0.00007893595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001310188,0.00005427967,0.9245074,0.00009079505,0.00003657942,0.000007031997,0.0001714839,0.0001018467,6.340733e-7,0.00974431,0.00001106698,0.06514353],"study_design_scores_gemma":[0.0007700353,0.00004416937,0.7997371,0.00003946662,0.00003769793,0.000008643245,0.0001136814,0.1720474,1.47717e-7,0.02650944,0.0005686974,0.0001235179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538893,0.002862244,0.0412071,0.00004789822,0.0001596316,0.000188672,0.00007606704,0.000003641617,0.001565507],"genre_scores_gemma":[0.992942,0.002746034,0.004161372,0.00006855545,0.00004239734,0.000001932481,0.000002038596,0.000005077995,0.00003053811],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1719456,"threshold_uncertainty_score":0.7152272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01255410967999918,"score_gpt":0.2421251642881701,"score_spread":0.2295710546081709,"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."}}