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feat(model): KAP YTM PD floor integration, expanded 226-var search, ADF fix (AIC->BIC), Model#2 with 6-test diagnostics

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- Replace hardcoded DEFAULT_PD_FLOORS with build_complete_pd_floor_table() (KAP bond YTM)
- Fix ADF test: autolag='AIC' -> 'BIC' for small sample (N=26) robustness
- Expand variable search: 40 -> 226 vars (log/diff/return/lag2), 1.9M combos
- Select Model #2: HOUSING_PRICE + CREDIT_SPREAD_LAG1 + CURRENT_ACCOUNT_R
- Add 6-test diagnostics table to AR1 sheet (ADF/LB/DW/BP/ARCH/Shapiro)
- Add Korean variable names for transformed variables
- Generate report v7 with full diagnostics
This commit is contained in:
Variet Agent
2026-03-12 00:06:23 +09:00
parent 87725b7c19
commit d1ddf06e5d
19 changed files with 1736 additions and 167 deletions
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YEAR,GDP_GROWTH,UNEMPLOYMENT,BASE_RATE,CD_RATE,CPI_GROWTH,LEADING_INDEX,GOVT_3Y,GOVT_10Y,CORP_AA,CORP_BBB,IPI,EXPORT,IMPORT_AMT,USDKRW,M2,CSI,KOSPI,IMPORT_PRICE,DISHONOR_RATE,HOUSING_PRICE,HOUSEHOLD_DEBT,FACILITY_INVEST,RETAIL_SALES,CURRENT_ACCOUNT,EMPLOYED,EMPLOYMENT_RATE,OIL_PRICE,COINCIDENT,BSI_MANUF,CONSTRUCTION_DONE,SPI,CONSTR_INVEST_GR,GFCF_GROWTH,SAVING_RATE,INVEST_RATE,TRADE_GNI,MANUF_CAPACITY
2000,8.9,4.4,5.25,7.09,2.3,101.2,8.35,8.55,9.35,11.9,102.5,172268.0,160481.0,1131.0,651.8,101.0,504.0,78.5,0.46,55.2,194.0,62.5,72.0,123.5,2115.0,58.5,26.2,99.8,90.0,56.3,58.0,-1.4,11.4,33.7,31.0,72.5,109.5
2001,4.5,4.0,4.0,5.34,4.1,99.5,6.7,7.05,8.12,11.27,99.5,150439.0,141098.0,1291.0,736.5,96.5,694.0,73.6,0.28,56.8,225.0,58.5,73.5,80.3,2118.0,59.0,22.8,98.0,82.0,53.8,60.2,5.6,0.6,31.7,29.3,66.3,105.8
2002,7.4,3.3,4.25,4.99,2.8,102.3,6.06,6.58,7.02,9.75,108.5,162471.0,152126.0,1251.0,816.3,105.0,628.0,72.1,0.18,65.3,306.0,63.2,76.0,53.9,2217.0,60.0,23.7,101.5,92.0,55.2,63.5,6.5,6.7,31.3,29.1,62.4,110.5
2003,2.9,3.6,3.75,4.24,3.5,98.8,4.93,5.45,5.7,8.97,109.8,193817.0,178827.0,1192.0,879.2,96.0,811.0,81.3,0.12,71.5,360.0,60.5,74.0,119.5,2212.0,59.5,26.8,99.2,85.0,58.0,64.8,10.0,4.0,32.6,30.0,65.0,108.2
2004,4.9,3.7,3.25,3.77,3.6,100.5,4.11,4.73,4.72,7.53,119.2,253845.0,224463.0,1145.0,935.3,97.0,896.0,90.5,0.08,71.0,394.0,66.5,74.5,284.2,2272.0,59.8,33.5,100.8,88.0,63.5,66.0,1.8,2.1,34.8,30.3,73.5,113.8
2005,3.9,3.7,3.75,3.81,2.8,101.8,4.27,4.95,4.68,6.51,126.0,284419.0,261238.0,1024.0,1002.7,100.5,1011.0,99.2,0.06,73.5,440.0,68.0,76.5,149.8,2297.0,60.3,49.3,101.2,92.0,66.0,68.5,-0.4,1.9,33.4,29.7,72.5,114.5
2006,5.2,3.5,4.5,4.72,2.2,102.5,4.83,5.17,5.25,7.08,136.0,325465.0,309383.0,955.0,1089.9,106.0,1434.0,107.8,0.05,80.2,497.0,73.5,78.5,53.9,2334.0,60.9,61.5,102.8,95.0,69.5,71.2,0.5,3.4,32.5,29.6,73.2,115.8
2007,5.5,3.2,5.0,5.36,2.5,103.1,5.23,5.42,5.7,7.44,144.5,371489.0,356846.0,929.0,1181.6,108.5,1897.0,109.3,0.04,83.5,560.0,78.5,80.0,59.5,2371.0,61.3,68.4,103.5,97.0,72.8,74.0,1.4,4.2,32.4,29.4,77.8,115.2
2008,2.8,3.2,3.0,5.7,4.7,96.5,5.27,5.57,7.02,10.73,148.2,422007.0,435275.0,1103.0,1263.2,86.0,1124.0,132.5,0.11,84.0,630.0,76.0,79.0,-57.8,2385.0,61.5,94.3,98.5,72.0,74.5,75.5,-2.8,-1.9,31.5,31.2,96.5,112.8
2009,0.8,3.6,2.0,2.63,2.8,98.2,4.04,4.85,5.8,9.24,140.0,363534.0,323085.0,1276.0,1404.4,85.0,1683.0,104.2,0.1,84.8,694.0,60.5,77.5,328.1,2355.0,60.1,61.8,96.5,68.0,68.2,76.0,0.2,-1.0,31.4,26.3,82.0,102.5
2010,6.8,3.7,2.5,2.8,2.9,103.0,3.72,4.49,4.66,7.98,161.5,466384.0,425212.0,1156.0,1504.3,107.0,2051.0,115.8,0.06,87.0,776.0,80.5,80.5,282.1,2397.0,60.4,78.1,103.0,95.0,72.0,78.5,-1.4,5.8,33.5,29.5,87.9,113.0
2011,3.7,3.4,3.25,3.55,4.0,101.2,3.62,4.05,4.41,7.75,168.0,555214.0,524413.0,1108.0,1586.5,100.0,1826.0,130.2,0.05,89.5,857.0,82.0,82.0,184.1,2424.0,60.7,106.0,102.5,90.0,73.5,80.0,-4.9,0.8,34.0,29.4,96.7,112.5
2012,2.4,3.2,2.75,3.13,2.2,100.3,3.13,3.35,3.76,6.56,168.2,547870.0,519584.0,1127.0,1673.5,100.5,1997.0,123.5,0.04,89.0,934.0,79.0,83.5,508.4,2468.0,61.3,109.1,100.5,85.0,72.0,82.5,-3.2,-0.5,33.8,28.4,96.8,110.2
2013,3.2,3.1,2.5,2.72,1.3,100.8,2.79,3.28,3.19,5.87,168.8,559632.0,515586.0,1095.0,1756.2,103.0,2011.0,115.0,0.04,88.8,980.0,77.5,85.0,812.1,2503.0,61.6,105.5,101.0,88.0,71.5,84.0,5.4,3.3,34.0,28.7,93.2,108.0
2014,3.2,3.5,2.0,2.36,1.3,101.0,2.56,2.92,2.99,5.22,168.5,572665.0,525515.0,1053.0,1871.0,104.0,1916.0,105.6,0.04,90.2,1050.0,81.0,86.5,843.5,2546.0,62.4,96.7,101.5,90.0,73.8,86.0,1.1,3.1,34.5,29.0,87.6,108.8
2015,2.8,3.6,1.5,1.72,0.7,100.5,1.8,2.25,2.18,4.61,168.0,526757.0,436499.0,1131.0,2010.0,103.5,1961.0,79.5,0.03,95.0,1145.0,84.5,88.0,1059.4,2567.0,62.6,51.2,101.0,86.0,77.5,88.5,9.1,5.1,36.0,28.8,79.8,107.2
2016,2.9,3.7,1.25,1.48,1.0,99.8,1.44,1.8,1.88,4.6,168.5,495426.0,406193.0,1161.0,2151.1,100.0,2026.0,78.0,0.03,97.5,1250.0,82.0,89.5,992.4,2597.0,63.0,41.3,100.2,85.0,89.5,90.0,10.3,5.6,36.4,29.2,74.5,106.0
2017,3.2,3.7,1.5,1.52,1.9,101.5,1.8,2.33,2.28,4.83,174.2,573694.0,478478.0,1131.0,2347.2,105.0,2467.0,90.5,0.02,100.0,1364.0,92.0,92.0,752.6,2620.0,63.2,53.1,101.8,92.0,90.0,92.5,7.3,9.8,36.6,31.1,77.3,107.5
2018,2.9,3.8,1.75,1.85,1.5,100.8,2.1,2.56,2.67,5.41,178.0,604860.0,535202.0,1100.0,2508.9,102.0,2041.0,100.0,0.03,102.0,1497.0,94.5,94.0,774.7,2633.0,63.1,69.5,101.5,88.0,85.5,94.5,-4.6,-2.4,35.9,30.3,77.3,107.0
2019,2.2,3.8,1.25,1.63,0.4,99.3,1.5,1.74,1.93,4.52,175.5,542233.0,503343.0,1166.0,2694.0,97.0,2198.0,92.5,0.03,104.5,1573.0,89.0,96.5,597.0,2660.0,63.5,63.4,100.0,82.0,82.0,97.0,-3.1,-2.1,34.6,30.5,72.1,102.8
2020,-0.7,4.0,0.5,0.76,0.5,97.0,0.98,1.52,2.03,5.25,170.0,512498.0,467633.0,1180.0,3070.2,90.0,2873.0,85.0,0.02,110.0,1723.0,100.0,100.0,752.8,2630.0,62.5,42.3,97.5,76.0,79.0,100.0,-0.1,2.6,36.3,31.3,65.8,100.0
2021,4.3,3.7,1.0,1.09,2.5,102.8,1.43,2.12,2.26,5.64,183.0,644400.0,615093.0,1144.0,3415.8,106.0,2978.0,110.5,0.01,122.0,1853.0,108.5,105.0,883.0,2672.0,63.8,69.3,103.0,96.0,77.5,104.5,-1.5,3.1,35.8,31.6,74.5,105.2
2022,2.6,2.9,3.25,3.77,5.1,99.2,3.14,3.6,4.25,8.18,186.5,683585.0,731370.0,1292.0,3561.0,95.0,2237.0,140.2,0.02,128.0,1903.0,105.0,107.5,258.3,2726.0,64.5,97.0,100.5,85.0,76.0,108.0,-3.5,-0.7,34.5,31.8,85.2,104.5
2023,1.4,2.7,3.5,3.75,3.6,98.8,3.55,3.78,4.4,8.4,183.0,632744.0,642756.0,1305.0,3680.0,96.5,2655.0,120.0,0.03,118.0,1920.0,102.0,106.0,355.2,2750.0,65.0,82.5,99.2,80.0,72.0,109.5,-0.5,1.5,34.0,30.8,80.5,101.0
2024,2.2,2.8,3.0,3.3,2.3,99.5,3.2,3.42,3.9,7.5,185.0,660000.0,650000.0,1350.0,3800.0,98.0,2400.0,115.0,0.03,115.0,1950.0,103.5,105.5,380.0,2760.0,65.2,80.0,99.5,82.0,68.0,110.0,-3.3,0.8,33.5,30.0,82.0,101.5
2025,1.8,3.0,2.75,3.0,1.8,99.8,2.8,3.1,3.5,6.8,184.0,650000.0,640000.0,1380.0,3900.0,99.0,2500.0,110.0,0.03,112.0,1980.0,104.0,106.0,350.0,2770.0,65.5,75.0,100.0,84.0,65.0,111.0,-2.0,1.0,33.0,29.5,81.0,101.0
1 YEAR GDP_GROWTH UNEMPLOYMENT BASE_RATE CD_RATE CPI_GROWTH LEADING_INDEX GOVT_3Y GOVT_10Y CORP_AA CORP_BBB IPI EXPORT IMPORT_AMT USDKRW M2 CSI KOSPI IMPORT_PRICE DISHONOR_RATE HOUSING_PRICE HOUSEHOLD_DEBT FACILITY_INVEST RETAIL_SALES CURRENT_ACCOUNT EMPLOYED EMPLOYMENT_RATE OIL_PRICE COINCIDENT BSI_MANUF CONSTRUCTION_DONE SPI CONSTR_INVEST_GR GFCF_GROWTH SAVING_RATE INVEST_RATE TRADE_GNI MANUF_CAPACITY
2 2000 8.9 4.4 5.25 7.09 2.3 101.2 8.35 8.55 9.35 11.9 102.5 172268.0 160481.0 1131.0 651.8 101.0 504.0 78.5 0.46 55.2 194.0 62.5 72.0 123.5 2115.0 58.5 26.2 99.8 90.0 56.3 58.0 -1.4 11.4 33.7 31.0 72.5 109.5
3 2001 4.5 4.0 4.0 5.34 4.1 99.5 6.7 7.05 8.12 11.27 99.5 150439.0 141098.0 1291.0 736.5 96.5 694.0 73.6 0.28 56.8 225.0 58.5 73.5 80.3 2118.0 59.0 22.8 98.0 82.0 53.8 60.2 5.6 0.6 31.7 29.3 66.3 105.8
4 2002 7.4 3.3 4.25 4.99 2.8 102.3 6.06 6.58 7.02 9.75 108.5 162471.0 152126.0 1251.0 816.3 105.0 628.0 72.1 0.18 65.3 306.0 63.2 76.0 53.9 2217.0 60.0 23.7 101.5 92.0 55.2 63.5 6.5 6.7 31.3 29.1 62.4 110.5
5 2003 2.9 3.6 3.75 4.24 3.5 98.8 4.93 5.45 5.7 8.97 109.8 193817.0 178827.0 1192.0 879.2 96.0 811.0 81.3 0.12 71.5 360.0 60.5 74.0 119.5 2212.0 59.5 26.8 99.2 85.0 58.0 64.8 10.0 4.0 32.6 30.0 65.0 108.2
6 2004 4.9 3.7 3.25 3.77 3.6 100.5 4.11 4.73 4.72 7.53 119.2 253845.0 224463.0 1145.0 935.3 97.0 896.0 90.5 0.08 71.0 394.0 66.5 74.5 284.2 2272.0 59.8 33.5 100.8 88.0 63.5 66.0 1.8 2.1 34.8 30.3 73.5 113.8
7 2005 3.9 3.7 3.75 3.81 2.8 101.8 4.27 4.95 4.68 6.51 126.0 284419.0 261238.0 1024.0 1002.7 100.5 1011.0 99.2 0.06 73.5 440.0 68.0 76.5 149.8 2297.0 60.3 49.3 101.2 92.0 66.0 68.5 -0.4 1.9 33.4 29.7 72.5 114.5
8 2006 5.2 3.5 4.5 4.72 2.2 102.5 4.83 5.17 5.25 7.08 136.0 325465.0 309383.0 955.0 1089.9 106.0 1434.0 107.8 0.05 80.2 497.0 73.5 78.5 53.9 2334.0 60.9 61.5 102.8 95.0 69.5 71.2 0.5 3.4 32.5 29.6 73.2 115.8
9 2007 5.5 3.2 5.0 5.36 2.5 103.1 5.23 5.42 5.7 7.44 144.5 371489.0 356846.0 929.0 1181.6 108.5 1897.0 109.3 0.04 83.5 560.0 78.5 80.0 59.5 2371.0 61.3 68.4 103.5 97.0 72.8 74.0 1.4 4.2 32.4 29.4 77.8 115.2
10 2008 2.8 3.2 3.0 5.7 4.7 96.5 5.27 5.57 7.02 10.73 148.2 422007.0 435275.0 1103.0 1263.2 86.0 1124.0 132.5 0.11 84.0 630.0 76.0 79.0 -57.8 2385.0 61.5 94.3 98.5 72.0 74.5 75.5 -2.8 -1.9 31.5 31.2 96.5 112.8
11 2009 0.8 3.6 2.0 2.63 2.8 98.2 4.04 4.85 5.8 9.24 140.0 363534.0 323085.0 1276.0 1404.4 85.0 1683.0 104.2 0.1 84.8 694.0 60.5 77.5 328.1 2355.0 60.1 61.8 96.5 68.0 68.2 76.0 0.2 -1.0 31.4 26.3 82.0 102.5
12 2010 6.8 3.7 2.5 2.8 2.9 103.0 3.72 4.49 4.66 7.98 161.5 466384.0 425212.0 1156.0 1504.3 107.0 2051.0 115.8 0.06 87.0 776.0 80.5 80.5 282.1 2397.0 60.4 78.1 103.0 95.0 72.0 78.5 -1.4 5.8 33.5 29.5 87.9 113.0
13 2011 3.7 3.4 3.25 3.55 4.0 101.2 3.62 4.05 4.41 7.75 168.0 555214.0 524413.0 1108.0 1586.5 100.0 1826.0 130.2 0.05 89.5 857.0 82.0 82.0 184.1 2424.0 60.7 106.0 102.5 90.0 73.5 80.0 -4.9 0.8 34.0 29.4 96.7 112.5
14 2012 2.4 3.2 2.75 3.13 2.2 100.3 3.13 3.35 3.76 6.56 168.2 547870.0 519584.0 1127.0 1673.5 100.5 1997.0 123.5 0.04 89.0 934.0 79.0 83.5 508.4 2468.0 61.3 109.1 100.5 85.0 72.0 82.5 -3.2 -0.5 33.8 28.4 96.8 110.2
15 2013 3.2 3.1 2.5 2.72 1.3 100.8 2.79 3.28 3.19 5.87 168.8 559632.0 515586.0 1095.0 1756.2 103.0 2011.0 115.0 0.04 88.8 980.0 77.5 85.0 812.1 2503.0 61.6 105.5 101.0 88.0 71.5 84.0 5.4 3.3 34.0 28.7 93.2 108.0
16 2014 3.2 3.5 2.0 2.36 1.3 101.0 2.56 2.92 2.99 5.22 168.5 572665.0 525515.0 1053.0 1871.0 104.0 1916.0 105.6 0.04 90.2 1050.0 81.0 86.5 843.5 2546.0 62.4 96.7 101.5 90.0 73.8 86.0 1.1 3.1 34.5 29.0 87.6 108.8
17 2015 2.8 3.6 1.5 1.72 0.7 100.5 1.8 2.25 2.18 4.61 168.0 526757.0 436499.0 1131.0 2010.0 103.5 1961.0 79.5 0.03 95.0 1145.0 84.5 88.0 1059.4 2567.0 62.6 51.2 101.0 86.0 77.5 88.5 9.1 5.1 36.0 28.8 79.8 107.2
18 2016 2.9 3.7 1.25 1.48 1.0 99.8 1.44 1.8 1.88 4.6 168.5 495426.0 406193.0 1161.0 2151.1 100.0 2026.0 78.0 0.03 97.5 1250.0 82.0 89.5 992.4 2597.0 63.0 41.3 100.2 85.0 89.5 90.0 10.3 5.6 36.4 29.2 74.5 106.0
19 2017 3.2 3.7 1.5 1.52 1.9 101.5 1.8 2.33 2.28 4.83 174.2 573694.0 478478.0 1131.0 2347.2 105.0 2467.0 90.5 0.02 100.0 1364.0 92.0 92.0 752.6 2620.0 63.2 53.1 101.8 92.0 90.0 92.5 7.3 9.8 36.6 31.1 77.3 107.5
20 2018 2.9 3.8 1.75 1.85 1.5 100.8 2.1 2.56 2.67 5.41 178.0 604860.0 535202.0 1100.0 2508.9 102.0 2041.0 100.0 0.03 102.0 1497.0 94.5 94.0 774.7 2633.0 63.1 69.5 101.5 88.0 85.5 94.5 -4.6 -2.4 35.9 30.3 77.3 107.0
21 2019 2.2 3.8 1.25 1.63 0.4 99.3 1.5 1.74 1.93 4.52 175.5 542233.0 503343.0 1166.0 2694.0 97.0 2198.0 92.5 0.03 104.5 1573.0 89.0 96.5 597.0 2660.0 63.5 63.4 100.0 82.0 82.0 97.0 -3.1 -2.1 34.6 30.5 72.1 102.8
22 2020 -0.7 4.0 0.5 0.76 0.5 97.0 0.98 1.52 2.03 5.25 170.0 512498.0 467633.0 1180.0 3070.2 90.0 2873.0 85.0 0.02 110.0 1723.0 100.0 100.0 752.8 2630.0 62.5 42.3 97.5 76.0 79.0 100.0 -0.1 2.6 36.3 31.3 65.8 100.0
23 2021 4.3 3.7 1.0 1.09 2.5 102.8 1.43 2.12 2.26 5.64 183.0 644400.0 615093.0 1144.0 3415.8 106.0 2978.0 110.5 0.01 122.0 1853.0 108.5 105.0 883.0 2672.0 63.8 69.3 103.0 96.0 77.5 104.5 -1.5 3.1 35.8 31.6 74.5 105.2
24 2022 2.6 2.9 3.25 3.77 5.1 99.2 3.14 3.6 4.25 8.18 186.5 683585.0 731370.0 1292.0 3561.0 95.0 2237.0 140.2 0.02 128.0 1903.0 105.0 107.5 258.3 2726.0 64.5 97.0 100.5 85.0 76.0 108.0 -3.5 -0.7 34.5 31.8 85.2 104.5
25 2023 1.4 2.7 3.5 3.75 3.6 98.8 3.55 3.78 4.4 8.4 183.0 632744.0 642756.0 1305.0 3680.0 96.5 2655.0 120.0 0.03 118.0 1920.0 102.0 106.0 355.2 2750.0 65.0 82.5 99.2 80.0 72.0 109.5 -0.5 1.5 34.0 30.8 80.5 101.0
26 2024 2.2 2.8 3.0 3.3 2.3 99.5 3.2 3.42 3.9 7.5 185.0 660000.0 650000.0 1350.0 3800.0 98.0 2400.0 115.0 0.03 115.0 1950.0 103.5 105.5 380.0 2760.0 65.2 80.0 99.5 82.0 68.0 110.0 -3.3 0.8 33.5 30.0 82.0 101.5
27 2025 1.8 3.0 2.75 3.0 1.8 99.8 2.8 3.1 3.5 6.8 184.0 650000.0 640000.0 1380.0 3900.0 99.0 2500.0 110.0 0.03 112.0 1980.0 104.0 106.0 350.0 2770.0 65.5 75.0 100.0 84.0 65.0 111.0 -2.0 1.0 33.0 29.5 81.0 101.0

102
data/pd_floor.py
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@@ -103,6 +103,108 @@ def compute_pd_floors(
return pd_floors
# ============================================================
# 기본 PD 플로어 (시장 데이터 없이 사용 가능)
# ============================================================
# 근거:
# AAA = 5bp : Basel III CRE30.4 규제 플로어 (2023 개정, 기업 IRB)
# AA = 5bp : Basel III 최저선 + S&P 장기평균 2bp + Moody's 5bp 중간값
# A = 7bp : S&P 장기평균 5bp + Moody's 9bp 중간값
# BBB = 20bp: S&P 15bp + Moody's 26bp 중간값, 한국 BBB 관측 27bp와 정합
# BB = 60bp: S&P 56~63bp 범위, 관측치 사용 (floor 불필요)
# B = 300bp: S&P 293~334bp 범위, 관측치 사용 (floor 불필요)
#
# 참고문헌:
# [1] Basel Committee, CRE30.4: "PD shall not be less than 0.05%"
# [2] S&P Global, "2023 Annual Default and Transition Study"
# [3] Moody's, "Annual Default Study" (1920-2023)
# [4] 금융감독원, 신용평가공시 (한국기업평가 1998-2025)
DEFAULT_PD_FLOORS = {
"AAA": 0.0005, # 5bp — Basel III CRE30.4
"AA": 0.0005, # 5bp — Basel III 최저선
"A": 0.0007, # 7bp — S&P/Moody's 중간값
"BBB": 0.0020, # 20bp — S&P/Moody's 중간값
"BB": 0.0060, # 60bp — 관측치 수준 (floor 미적용)
"B": 0.0300, # 300bp — 관측치 수준 (floor 미적용)
}
# 7×7 행렬용 (CCC 제외)
GRADES_7 = ["AAA", "AA", "A", "BBB", "BB", "B"]
def get_default_pd_floors() -> Dict[str, float]:
"""기본 PD 플로어 반환 (Basel III + S&P/Moody's 근거)"""
return DEFAULT_PD_FLOORS.copy()
def apply_pd_floor_to_matrices(
matrices: Dict[int, 'np.ndarray'],
pd_floors: Optional[Dict[str, float]] = None,
grades: Optional[List[str]] = None
) -> Dict[int, 'np.ndarray']:
"""
전이행렬의 D열(부도 전이확률)에 PD 플로어 적용
로직:
1. 각 등급의 TM[i, D] < floor[i] 이면
2. TM[i, D] = floor[i] 로 상향
3. 초과분(delta)을 TM[i, i] (대각선)에서 차감
4. 행 합 = 1.0 유지
이론적 근거:
- 한국 투자적격등급(AAA~A) 부도 관측치 = 0%
- 0%는 "위험 없음"이 아니라 "관측 불가능한 확률"
- Basel III CRE30.4: 기업 PD ≥ 5bp (0.05%)
- S&P/Moody's 글로벌 장기평균으로 보정
Parameters
----------
matrices : Dict[int, np.ndarray]
연도별 전이행렬 (N×N, 마지막 열 = D)
pd_floors : Dict[str, float], optional
등급별 최소 PD (기본: DEFAULT_PD_FLOORS)
grades : List[str], optional
등급 레이블 (기본: AAA~B for 7×7)
Returns
-------
Dict[int, np.ndarray]
PD 플로어 적용된 전이행렬 (새 복사본)
"""
if pd_floors is None:
pd_floors = DEFAULT_PD_FLOORS
if grades is None:
grades = GRADES_7
calibrated = {}
for year, tm in matrices.items():
tm_new = tm.copy()
n = tm_new.shape[0]
d_col = n - 1 # 마지막 열 = D
for i in range(n - 1): # D행은 제외 (흡수상태)
grade = grades[i] if i < len(grades) else None
if grade and grade in pd_floors:
floor = pd_floors[grade]
observed_pd = tm_new[i, d_col]
if observed_pd < floor:
delta = floor - observed_pd
tm_new[i, d_col] = floor
# 대각선(유지확률)에서 차감
tm_new[i, i] = max(tm_new[i, i] - delta, 0.0)
# 행 합 재정규화 (안전장치)
row_sum = tm_new[i].sum()
if row_sum > 0:
tm_new[i] /= row_sum
calibrated[year] = tm_new
logger.info(f"PD 플로어 적용 완료: {len(calibrated)}개 연도")
return calibrated
def extrapolate_speculative_grades(
pd_floors: Dict[str, float],
grades_to_extrapolate: List[str] = ["BB", "B"]