-
Notifications
You must be signed in to change notification settings - Fork 0
/
Stock_market_model_network_poles_sims_gama_infty_shocks_insert.m
161 lines (128 loc) · 4.78 KB
/
Stock_market_model_network_poles_sims_gama_infty_shocks_insert.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
%Simulation of application with fluctutating price and types.
%Case of gamma --> infty. Last updated: Sep 20, 2023.
%Written by M. Hatcher (m.c.hatcher@soton.ac.uk)
%Calibration
xbar = 0; %Supply per person
r = 0.04; phi = 0.5; deltta = 1/phi; %dbar = 0.5; %star poles example %gama = 2.5;
pf = ( dbar - xbar/deltta )/ r; %Steady state fundamental price
n = 3; %no. of agents
%--------------------------------
%Initial matrices (for storage)
%--------------------------------
U_net = NaN(n,n); sum_U = NaN(n,1); rat = sum_U;
Beliefs = NaN(n,T); X = Beliefs; U = Beliefs; U_tild = U; g= U;
p = NaN(T,1); dev = p; p_crit = p; gap = p; gbar = p; cap_gain = p; check = p;
dev_lag = dev; gbar_lag = dev;
%-----------------
%Dividend shocks
%-----------------
shock0 = random(pd_t,1,1);
shocks = random(pd_t,T,1);
%----------------------------
%Initial types and network
%----------------------------
g_init = zeros(1,n);
eps = 0.001;
%eps = -0.01;
g_init(1,1) = 1; g_init(1,2) = 1+r; g_init(1,3) = (1+r+eps)^2;
A = [1 0 0; 1 1 1; 0 0 1];
%----------------
%Initial values
%----------------
gbar_init = sum(g_init)/n;
p0 = pf + 1; %1
plag2 = pf + ((1+r)/gbar_init)^2*(p0-pf);
plag1 = ( dbar + (1-sum(g_init)/n)*pf + sum(g_init)/n*plag2 - xbar/deltta ) /(1+r);
plag1_crit = sum(g_init)/n*(xbar/deltta)/((1+r)^2 - sum(g_init)/n);
gap_lag1 = (plag1 - pf) - plag1_crit;
g0 = g_init';
p0_check = ( dbar + (1-sum(g0)/n)*pf + sum(g0)/n*plag1 - xbar/deltta ) /(1+r);
p0_crit = sum(g0)/n*(xbar/deltta + shock0)/((1+r)^2- sum(g0)/n);
ptild0 = p0-pf; gap0 = (p0-pf) - p0_crit;
%----------------------------------------------
%Computation of demands and fitness (period 0)
%----------------------------------------------
Beliefs_lag1 = (1-g_init)*pf + g_init*plag2;
Xlag = deltta*(Beliefs_lag1 + dbar - (1+r)*plag1);
Beliefs0 = (1-g0)*pf + g0*plag1;
X0 = deltta*(Beliefs0 + dbar - (1+r)*p0);
U0 = (p0 + dbar + shock0 - (1+r)*plag1)*Xlag;
cap_gain0 = p0 + dbar + shock0 - (1+r)*plag1;
cap_gain_lag1 = plag1 + dbar - (1+r)*plag2;
dev0 = p0 - pf; gap0 = p0 - pf - p0_crit;
gbar0 = mean(g0);
%------------------
% Simulation
%------------------
for t=1:T
if t==1
for i=1:n
%For gamma --> infty
g_init_adj = g0;
A_vec = A(i,:)';
g_init_adj(A_vec==0) = NaN;
%Find maximal agents
if cap_gain0*dev0 > 0
[row,col] = find(g_init_adj==max(g_init_adj));
elseif cap_gain0*dev0 < 0
[row,col] = find(g_init_adj==min(g_init_adj));
end
nstar = length(row); %Number of maximal agents
V = zeros(1,n);
for j = row
V(j) = 1/nstar;
end
g(i,1) = V*g0;
end
p(1) = ( dbar + (1-sum(g(1:n,1))/n)*pf + sum(g(1:n,1))/n*p0 - xbar/deltta ) /(1+r);
dev(1) = p(1) - pf;
p_crit(1) = sum(g(1:n,1))/n*( (xbar/deltta) + shocks(1) ) /((1+r)^2 - sum(g(1:n,1))/n);
gap(1) = dev(1) - p_crit(1);
%Computation of demands and fitness
Beliefs(1:n,1) = (1-g(1:n,1))*pf + g(1:n,1)*p0;
X(1:n,1) = deltta*(Beliefs(1:n,1) + dbar - (1+r)*p(1));
U(1:n,1) = (p(1) + dbar + shocks(1) - (1+r)*p0)*X0;
cap_gain(1) = p(1) + dbar + shocks(1) - (1+r)*p0;
gbar(1) = sum(g(1:n,1))/n;
elseif t>=2
%----------------------
% Dates t>=2
%----------------------
for i=1:n
if t==2
g_adj = g0;
elseif t > 2
g_adj = g(1:n,t-2);
end
A_vec = A(i,:)';
g_adj(A_vec==0) = NaN;
%Find maximal agents
if cap_gain(t-1)*dev(t-1) > 0
[row,col] = find(g_adj==max(g_adj));
elseif cap_gain(t-1)*dev(t-1) < 0
[row,col] = find(g_adj==min(g_adj));
end
nstar = length(row); %Number of maximal agents
V = zeros(1,n);
for j = row
V(j) = 1/nstar;
end
g(i,t) = V*g(1:n,t-1);
end
p(t) = ( dbar + (1-sum(g(1:n,t))/n)*pf + sum(g(1:n,t))/n*p(t-1) - xbar/deltta ) /(1+r);
dev(t) = p(t) - pf;
dev_lag(t) = dev(t-1);
p_crit(t) = sum(g(1:n,t))/n*( (xbar/deltta) + shocks(t) )/((1+r)^2 - sum(g(1:n,t))/n);
gap(t) = dev(t) - p_crit(t);
%Computation of indvidual demands and fitness
Beliefs(1:n,t) = (1-g(1:n,t))*pf + g(1:n,t)*p(t-1);
X(1:n,t) = deltta*( Beliefs(1:n,t) + dbar - (1+r)*p(t) );
Xweighted = (1/n)*X(1:n,t);
U(1:n,t) = (p(t) + dbar + shocks(t) - (1+r)*p(t-1))*X(1:n,t-1);
gbar(t) = sum(g(1:n,t))/n;
gbar_lag(t) = gbar(t-1);
cap_gain(t) = p(t) + dbar + shocks(t) - (1+r)*p(t-1);
check(t) = sum(Xweighted)-xbar; %Market clearing
end
end
gbar_end = gbar(end);