{"id":"W2148031765","doi":"10.5539/ijef.v4n11p183","title":"Estimation of Exchange Rate Volatility via GARCH Model: Case Study Sudan (1978 – 2009)","year":2012,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economics; Exchange rate; Volatility (finance); Econometrics; Autoregressive conditional heteroskedasticity; Monetary economics; Leverage effect; Leverage (statistics); Depreciation (economics); Stock exchange; Financial economics; Finance; Statistics; Mathematics; Microeconomics","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":[0.001670385,0.0001411808,0.000432468,0.0002606378,0.00006331755,0.00004263828,0.0002251909,0.00007514564,0.0000193712],"category_scores_gemma":[0.000107783,0.0001608352,0.00011863,0.00006519994,0.00005428512,0.0008051638,0.0000798706,0.0001747503,0.000005802774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001010421,"about_ca_system_score_gemma":0.00003876429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004401647,"about_ca_topic_score_gemma":0.00008972372,"domain_scores_codex":[0.9983725,0.00002078805,0.001182254,0.0001894321,0.00003822622,0.0001967754],"domain_scores_gemma":[0.9984313,0.00007005785,0.0010992,0.0001683143,0.0001669758,0.00006410979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006714158,0.002337555,0.3858919,0.00008157099,0.0004892589,0.000144391,0.01371846,0.3393456,0.00002364056,0.1341892,0.0002377878,0.1228693],"study_design_scores_gemma":[0.0008190124,0.0001399943,0.01439002,0.00001746024,0.00001207686,0.0002206461,0.0001275649,0.96198,0.0000448702,0.02089655,0.001191299,0.0001604804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703369,0.002319783,0.02589846,0.0001297743,0.0007416323,0.000134309,0.0001232865,0.000002900402,0.0003129236],"genre_scores_gemma":[0.9953144,0.001568156,0.002790976,0.00004367641,0.000201525,0.000004322009,0.000003313077,0.00001260384,0.00006109066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6226345,"threshold_uncertainty_score":0.655867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05695029611530092,"score_gpt":0.2824985554354132,"score_spread":0.2255482593201123,"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."}}