#ifndef __FILE_EMMUSR_H_SEEN__ #define __FILE_EMMUSR_H_SEEN__ /* ---------------------------------------------------------------------------- Copyright (C) 2009. A. Ronald Gallant Post Office Box 659 Chapel Hill NC 27514-0659 USA This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. -----------------------------------------------------------------------------*/ //#define DEBUG_EMMUSR #undef DEBUG_EMMUSR //#define MONTHLY_FREQUENCY #undef MONTHLY_FREQUENCY //#define USE_ACCURATE_RBAR #undef USE_ACCURATE_RBAR /* Economy II Parameter Name Meaning Plausible theta[1] g_c Consumption growth 0.0184/12.0 theta[2] g_d Dividend growth 0.0184/12.0 theta[3] sig_c Sdev of consumption growth 0.0379/sqrt(12.0) theta[4] sig_d Sdev of dividend growth 0.12/sqrt(12.0) theta[5] omega Correlation of cg & dg 0.15 theta[6] gamma Risk aversion parameter 1.0 theta[7] rho Subjective discount factor pow(0.98,1.0/12.0) theta[8] lambda Intercept of lambda function 2.25 theta[9] k Slope of lambda function 10.0 but 3.0 for f plots theta[10] b_0 Scaling of prospect part 2.0/12.0 theta[11] eta Autoregressive parameter of z pow(0.9,1.0/12.0) theta[12] mudc Mean log dividend/consumption -3.3857 stats[1] br Bond returns 1.000743618 stats[2] bv Variance of bond returns 0.000024083 stats[3] sr Stock returns 1.002851 stats[4] sv Variance of stock returns 0.001771989 Parameter Name Reasonable support theta[1] g_c [-0.02, 0.02] [-0.24, 0.24] theta[2] g_c [-0.02, 0.02] [-0.24, 0.24] theta[3] sig_c [ 0.0, 1.0] [0.0, 3.46] theta[4] sig_d [ 0.0, 3.0] [0.0, 10.39] theta[5] omega [-1.0, 1.0] [-1.0, 1.0] theta[6] gamma [ 0.25, 10.0] [ 0.25, 10.0] theta[7] rho [ 0.9, 0.99999] [ 0.9, 0.99999] theta[8] lambda [ 0.0, 10.0] [ 0.0, 10.0] theta[9] k [ 0.0, 20.0] [ 0.0, 20.0] theta[10] b_0 [ 0.0, 10.0] [ 0.0, 120.0] theta[11] eta [ 0.25, 1.0] [ 0.25, 1.0] theta[12] phi_u [-10.0, 10.0] [-10.0, 10.0] */ #include "libsnp.h" #include "libsmm.h" #include "emm_base.h" #include "snp.h" namespace emm { class prospect_usrmod; typedef prospect_usrmod usrmod_type; const INTEGER n_state = 1; //Number of state variables const INTEGER n_parms = 12; //Number of sci mod parameters const INTEGER n_funcs = 4; //Number of sci mod functionals const INTEGER n_datum = 4; //Number of sci mod functionals class linear_function { private: REAL a; REAL b; REAL x0; public: void initialize(REAL intercept, REAL slope, REAL origin) { a = intercept; b = slope; x0 = origin; } REAL operator()(REAL x) { return a + b*(x - x0); } REAL intercept() { return a; } REAL slope() { return b; } REAL origin() { return x0; } }; class linear_interpolater { private: std::vector funcs; typedef std::vector::size_type lfst; REAL xmin; REAL xmax; lfst N; lfst hash(REAL x) { return lfst( REAL(N-2)*(x-xmin)/(xmax-xmin) ); } public: linear_interpolater() { scl::realmat grid(2,1) ; grid[1] = 0.0; grid[2] = 1.0; scl::realmat vals(2,1) ; vals[1] = 0.0; vals[2] = 1.0; update(grid,vals); } linear_interpolater(scl::realmat& x, scl::realmat& y) { update(x,y); }; void update(scl::realmat& x, scl::realmat& y) { INTEGER n = x.size(); N = lfst(n); funcs.clear(); funcs.reserve(N); if (n<2) scl::error("Error, linear_interpolater, x.size() < 2"); if (x.ncol() != 1 || y.ncol() != 1) scl::error("Error, linear_interpolater, x or y not a vector"); if (n != y.size()) scl::error("Error, linear_interpolater, x and y sizes differ"); scl::intvec rank = x.sort(); y = y(rank,""); xmin = x[1]; xmax = x[n]; linear_function f; for (INTEGER i=1; i= funcs[N-1].origin()) return funcs[N-1](x); lfst i = hash(x); if (x < funcs[i].origin()) while(x < funcs[--i].origin()); else if (x >= funcs[i+1].origin()) while(x >= funcs[++i+1].origin()); return funcs[i](x); } REAL get_xmin() {return xmin;} REAL get_xmax() {return xmax;} }; struct prospect_usrmod_variables { INTEGER n0; // for lags and to allow transients to dissipate INTEGER n; // simulation length INTEGER n1; // for leads INTEGER nt; // nt = n0 + n + n1 scl::realmat log_consumption_growth; scl::realmat log_dividend_growth; scl::realmat consumption_growth; // cg_t scl::realmat dividend_growth; // dg_t scl::realmat consumption; // C_t scl::realmat dividend; // D_t scl::realmat state_variable; // z_t scl::realmat marginal_rate_of_substitution; // need to derive a formula scl::realmat price_dividend_ratio; // PD_t scl::realmat gross_stock_return; // R_t scl::realmat gross_risk_free_rate; // Rf scl::realmat geometric_stock_return; scl::realmat geometric_risk_free_rate; prospect_usrmod_variables() { } prospect_usrmod_variables(INTEGER n_lag, INTEGER n_sim, INTEGER n_lead) : n0(n_lag), n(n_sim), n1(n_lead), nt(n0+n+n1), log_consumption_growth(nt,1,0.0), log_dividend_growth(nt,1,0.0), consumption_growth(nt,1,0.0), dividend_growth(nt,1,0.0), consumption(nt,1,0.0), dividend(nt,1,0.0), state_variable(nt,1,1.0), marginal_rate_of_substitution(nt,1,1.0), price_dividend_ratio(nt,1,0.0), gross_stock_return(nt,1,0.0), gross_risk_free_rate(nt,1,0.0), geometric_stock_return(nt,1,0.0), geometric_risk_free_rate(nt,1,0.0) { } std::vector get_prospect_usrmod_variables(scl::realmat& mv); }; struct prospect_usrmod_parameters { REAL g_c; // Consumption growth 0.001533 0.0184 REAL g_d; // Dividend growth 0.001533 0.0184 REAL sig_c; // Sdev of consumption growth 0.010941 0.0379 REAL sig_d; // Sdev of dividend growth 0.034641 0.12 REAL omega; // Correlation of cg & dg 0.15 0.15 REAL gamma; // Risk aversion parameter 1.0 1.0 REAL rho; // Subjective discount factor 0.998318 0.98 REAL lambda; // Intercept of lambda function 2.25 2.25 REAL k; // Slope of lambda function 10.0 10.0 REAL b_0; // Scaling of prospect part 0.166667 2.0 REAL eta; // Autoregressive parameter of z 0.991258 0.9 REAL mudc; // Mean log dividend/consumption -3.3857 -3.3857 INTEGER p; // Number of model parameters 12 12 prospect_usrmod_parameters() : p(n_parms) { scl::realmat theta(p,1); #if defined MONTHLY_FREQUENCY theta[1] = 0.0015333333333334; theta[2] = 0.0015333333333334; theta[3] = 0.0109407876011434; theta[4] = 0.0346410161513775; theta[5] = 0.15; theta[6] = 1.0; theta[7] = 0.99831785744726; theta[8] = 2.25; theta[9] = 10.0; theta[10] = 0.1666666666666667; theta[11] = 0.991258389045303; theta[12] = -3.3857; #else theta[1] = 0.0184; theta[2] = 0.0184; theta[3] = 0.0379; theta[4] = 0.12; theta[5] = 0.15; theta[6] = 1.0; theta[7] = 0.98; theta[8] = 2.25; theta[9] = 10.0; theta[10] = 2.0; theta[11] = 0.9; theta[12] = -3.3857; #endif set_theta(theta); } void set_theta(const scl::realmat& theta) { if (theta.nrow() != p || theta.ncol() != 1) scl::error("Error, prospect_usrmod, set_theta, bad dimension"); g_c = theta[1]; g_d = theta[2]; sig_c = theta[3]; sig_d = theta[4]; omega = theta[5]; gamma = theta[6]; rho = theta[7]; lambda = theta[8]; k = theta[9]; b_0 = theta[10]; eta = theta[11]; mudc = theta[12]; } void get_theta(scl::realmat& theta) { theta.resize(p,1); theta[1] = g_c; theta[2] = g_d; theta[3] = sig_c; theta[4] = sig_d; theta[5] = omega; theta[6] = gamma; theta[7] = rho; theta[8] = lambda; theta[9] = k; theta[10] = b_0; theta[11] = eta; theta[12] = mudc; } }; class prospect_usrmod : public libsmm::usrmod_base { private: prospect_usrmod_parameters mp; prospect_usrmod_variables mv; bool make_sim(INT_32BIT& seed, scl::realmat& sim); bool make_sim(INT_32BIT& seed, scl::realmat& sim, scl::realmat& func); linear_interpolater BHSpdval; std::ostream* debug; bool debug_switch; scl::realmat data; INT_32BIT bootstrap_seed; INT_32BIT simulation_seed; public: prospect_usrmod (const scl::realmat& dat, INTEGER len_mod_parm, INTEGER len_mod_func, const std::vector& pfvec, const std::vector& alvec, std::ostream& detail); INTEGER len_rho() { return n_parms; } INTEGER len_stats() { return n_funcs; } void get_rho(scl::realmat& parm) { mp.get_theta(parm); } void set_rho(const scl::realmat& parm) { mp.set_theta(parm); } bool support(const scl::realmat& parm); bool gen_sim(scl::realmat& sim); bool gen_sim(scl::realmat& sim, scl::realmat& func); bool gen_bootstrap(std::vector& bs); scl::den_val prior(const scl::realmat& parm, const scl::realmat& func); scl::realmat annualize_parms(const scl::realmat& theta); scl::realmat annualize_funcs(const scl::realmat& stats); void write_usrvar(const char* filename); }; } #endif