IMES Discussion Paper Series 98-J
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1 IMES DISCUSSION PAPER SERIES Discuss ssion Paper No. 98-J-1 INSTITUTE FOR MONETARY AND ECONOMIC STUDIES BANK OF JAPAN
2 IMES Discussion Paper Series 98-J tokiko.shimizu@boj.or.jp
3
4 k profile 1
5 1.2. I R i å R i 2
6 Glosten and Milgrom 1985 Glosten and Milgrom Gennottee and Leland
7 2.2. t I R i t+1 t+2 t+3 t+2 t+3 t t+2 R it R it+1 R it+2 R it+3 S DR i 4 S 1
8 t I F it F it t f it x t Fit = fit ( xt ) S 0 t+1 I x t t+1 f it S 0 = dx F = f ( x ) = f ( x + dx) it + 1 it + 1 t + 1 it t R s å å å R = R = df = s i i fi x dx I f 1 f 2 it + it + t+2 F 2 = f 2( x + dx) it+ it+ t n
9 æ n df ö i dx = G ç å èi= x 1 ø S 1 S1 = dx+ dx = dx R d å( + 2 ) å fit+ 2 ( xt dx) fit ( xt ) R = F - F d it it ( ) = + - æ i ( ) fx dx dx fi = ç + + è t dt ö å ø (1) t (1) fi t dt 6
10 R d t x a t a t = a constant x b t db = b / x b constant x c t dc = c dx t t t t b x t 100 x x t = 100 s x = 02. S0 = 5s = -65. x x t + 1 =
11 F = f ( x ) = 100 x = = it it t t F = f ( x ) = 100 x = = 9350 it + 1 it + 1 t + 1 t + 1 R s R = å R = ådf = 1950 s i i F2 = f2 ( x ) = 100 x = = t t t t ( ) ( ) F = f ( x ) = b/ x x = / = t+ 1 2t+ 1 t+ 1 t+ 1 t+ 1 df 2 F3 = f3 ( x ) = 100 x = = t t t t ( ) { } x t + 1 F = f ( x ) = c dx x = (- 6. 5) = t+ 1 3t+ 1 t+ 1 t+ 1 df 3 x t + 1 =+ 21 = n dx æ ö = G dfi x ç å èi= 1 ø = k( ) =-11. 5k k=0.2 S1 = dx+ dx = =-88. 8
12 R d å( + 2 ) R = F - F d it it æ å Fit + 2ç1 - è x t t + 2 ö ø = =
13 t 10
14 JGB JGB JGB ± ± ± 11
15 71.67% 76.67% 85.42% 12
16 13
17 % t JGB 14
18 JGB 15
19 JGB JGB JGB JGB 16
20 DP = as b DP a s b p p : JGB : / : JGB : VaR (t) * * t VaR t-1 t 17
21
22
23
24 21
25 Factor x Factor y y x ( ) X = x, y t t t t f ( X ) t t Sensitivity data at t : f t ( Xt) f ( ) t Xt x t+1 f ( ) 1( X 1) ft+ 1 Xt+ 1 t+ t+ x f ( X ) t+ 1 t+ 1 y y 22
26 a) b) f f t ( X ) t t+1 x ( Xt) f ( ) t Xt+1 x f x ( X ) t+ 1 t+ 1 f t f t+1 x Network A sell xa Network B buy xb Network C sell xc 23
27 Camargo, F. A., Learning Algorithms in neural Networks, Working Paper, the DOC Laboratory, Computer Science Department, Columbia University, Glosten, L., and P. Milgrom, Bid, Ask, and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders, Journal of Financial Economics, 13, 1985, pp Gennottee, G., and H. Leland, Market Liquidity, Hedges and Crashes, American Economic Review, 80, 1990, pp O hara, M., Market Microstructure Theory, Blackwell Publishers. Shimizu, Tokiko, and Tsukasa Yamashita, Dynamic Micro and Macro Stress Simulation, Disucussion Paper Series, 96-E-4, IMES, Bank of Japan 24
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36 Table 1 Characteristics of the agents Key for trading Fortrend/contrarian Other characteristics Targeted profit (loss limit), Position limit Agent 1 Charts of market movements (chartist) Fortrend Positive correlation b/w P/L condition and trading volume 3 billion yen/half year, delta limit:± 100 billion Agent 2 Charts of market movements (chartist) Contrarian Frequent writer of options 3 billion yen/half year, delta limit:± 100 billion Agent 3 Fundamental events (fundamentalist) Trading horizon is longer than the others (more an investor-type of trader than a dealer) 2 billion yen/half year, delta limit:± 70 billion Table 2 Steps for inputs adjustments Inputs Agent 1 Agent 2 Agent 3 A: Market data (change on a trading day) B: A + accumulated P/L C: B + recent market movements + news D: C + market trend over a longer horizon (Agent 3) 71.67% 82.08% 80.00% (Figure 5) (Figure 6) 188
37 Table 3 The magnitude of largest daily change Upward change Downward change 3-year swap rate 3.01s -3.43s 5-year swap rate 2.96s -2.59s 10-year swap rate 3.46s -2.54s Table 4 Stress scenarios 3y 5y 10y Period t 3.33% 3.98% 4.49% Scenario % 4.22% 4.72% Scenario % 3.78% 4.33% Scenario % 3.98% 4.49% Scenario % 3.98% 4.49% Scenario % 4.22% 4.49% Scenario % 3.78% 4.49% Scenario % 3.98% 4.72% Scenario % 3.98% 4.33% 189
38 Table 5 Changes of each agent s delta under scenarios (in billions of yen) Agent 1 Agent 2 Agent 3 Total D/S Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario Standard deviation 12 Table 6 Risk profiles of each agent (in billions of yen) Agent 1 Agent 2 Agent 3 Total Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario VaR(t) * * Agent 3 s VaR figure is measured at period t-1, since its position at period t is square. 190
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