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/********************************************************************************** buildHWLog: constructs a lognormal Hull-White tree calibrated to the yield curve [in]: vector<double> yield_curve : vector of yield curve double a1 : level of mean reversion int N : number of time steps double T : time to maturity vector<double> yield : yield to maturity rates vector<double> volatility_curve : vector of yield volatilities int abs_ ag : ag to indicate whether volatilities are annualized [out]: void **********************************************************************************/ void HullWhiteLog::buildHWLog(int N, double T, double a1, vector<double> yield, vector<double> volatility_curve, int abs_ ag) { double R[20] = {0.0}; // zero yield rates double P[20] = {0.0}; // discount bond prices double Pu[20] = {0.0}; // discount bond price in up nodes double Pd[20] = {0.0}; // discount bond price in down nodes double r[20][20] = {0.0}; // short rate at node i, j double volR[20] = {0.0}; // volatility of short rate double vol, a, b, c, d1, e, f; // log HW parameters double pu[20][20] = {0.0}; // up probability at node i,j double pm[20][20] = {0.0}; // middle probability at node i,j double pd[20][20] = {0.0}; // down probability at node i,j double Qu[20][20] = {0.0}; // prices of Arrow-Debreu in up state double Qd[20][20] = {0.0}; // prices of Arrow-Debreu in down state double d[20][20] = {0.0}; // discount rate double theta[20] = {0.0}; // theta parameter double mu[20][20] = {0.0}; // mu parameter double tolerance = 0.0001; // error tolerance level double error = 0.0; // numerical search error double diff = 0.0; // drift difference r[i][j] mu[i][j]; double val = 0.0; // f(alpha) double val1 = 0.0; // rst derivative of f(alpha) double alpha[20] = {0.0}; // alpha (a) parameter double eta = 0.0; // eta parameter double topNode[20] = {0.0}; // stores top branch level double lowNode[20] = {0.0}; // stores low branch level int jmax = 0; // max upper branch level int jmin = 0; // min lower branch level int i, j, k; // pre-compute constants double dt = T/N; double dr = vol*sqrt(3*dt); double sdt = sqrt(dt); Like many open source projects, BZFlag was begun by a single person as a small idea that just took off. Chris Schoeneman started what became BZFlag as part of his graduate studies in computer graphics at Cornell University in 1993. The project started as a demo program to spin a 3D model with a mouse. When a friend suggested that Schoeneman make the demo into a game, he created tank models, added the ability to shoot the tanks, and made it so the game could be played against other players on a LAN. The game grew in popularity at Cornell and, over time, features were added such as flags, team bases, and Capture-the-flag style game play. With the addition of capturethe-flag, the game s name changed from bz to BZFlag. More than a dozen years later, BZFlag (http://BZFlag.org/) has a thriving community, with more than 3,000 registered users and 34,000 articles at the BZFlag forums (http:// my.BZFlag.org/bb). At any given time, there are dozens of public BZFlag servers running and waiting for you to join in. New worlds and new features are constantly being created and made available for BZFlag. The current BZFlag maintainer is Tim Riker. Many other contributors to the project are listed as well. These include Daniel L onard, Jeremiah CobraA1 Moss, Frank Siegert, Frank Chestal Thilo, Colin Bayer, David Trowbridge, Daryll Strauss, Dave Brosius, Sean Morrison, Alfredo Tupone, and Daniel Remenak. document.testForm.Cuisine.options[1].text = "Continental"; document.testForm.Cuisine.options[1].value = "Continental"; inline double Statistics::kurtosis() const { QL_REQUIRE(sampleNumber_>3, Stat::kurtosis() : sample number <=3, insuf cient ); double m = mean(); double v = variance(); if (v==0) return - 3.0*(sampleNumber_-1.0)*(sampleNumber_-1.0) / ((sampleNumber_2.0)*(sampleNumber_-3.0)); double kurt = sampleNumber_*sampleNumber_*(sampleNumber_+1.0) / ((sampleNumber_-1.0)*(sampleNumber_-2.0) * (sampleNumber_-3.0)*v*v) * (fourthPowerSum_ - 4.0*m*cubicSum_ + 6.0*m*m*quadraticSum_ -3.0*m*m*m*sum_)/sampleWeight_ -3.0*(sampleNumber_-1.0)* (sampleNumber_-1.0) / ((sampleNumber_-2.0)*(sampleNumber_-3.0));
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