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计算javascript中的隐含波动率

[英]Calculate Implied volatility in javascript

I am trying to calculate implied volatility using javascript , I have following code我正在尝试使用 javascript 计算隐含波动率,我有以下代码

function pdf_stdgauss(x) {
    return Math.exp(-x * x / 2.0) / Math.sqrt(2.0 * Math.PI);
}

function cdf_stdgauss(x) {
    var t = 1.0 / (1.0 + 0.2316419 * (x < 0 ? -x : x));
    var b1 = 0.319381530;
    var b2 = -0.356563782;
    var b3 = 1.781477937;
    var b4 = -1.821255978;
    var b5 = 1.330274429;
    var a = t * (b1 + t * (b2 + t * (b3 + t * (b4 + t * b5))));
    return (x < 0 ? a * pdf_stdgauss(x) : (1.0 - a * pdf_stdgauss(x)));
}



function ecp(s, x, rfi, dvd, sigma, t) {
    var sst = sigma * Math.sqrt(t);
    var d1 = (Math.log(s / x) + (rfi - dvd + sigma * sigma / 2.0) * t) / sst;
    var d2 = d1 - sst;
    var Nd1 = cdf_stdgauss(d1);
    var Nd2 = cdf_stdgauss(d2);
    var pd1 = pdf_stdgauss(d1);
    var pd2 = pdf_stdgauss(d2);
    var erfi = Math.exp(-rfi * t);
    var edvd = Math.exp(-dvd * t);
    var c = s * edvd * Nd1 - x * erfi * Nd2;
    var p = c + x * erfi - s * edvd;
    var cdelta = edvd * Nd1;
    var pdelta = cdelta - edvd;
    var gamma = edvd * pd1 / (s * sst);
    var ctheta = dvd * s * edvd * Nd1 - rfi * x * erfi * Nd2 - 0.5 * sigma * sigma * s * s * gamma;
    var ptheta = ctheta + rfi * x * erfi - dvd * s * edvd;
    var vega = s * edvd * pd1 * Math.sqrt(t);
    var crho = x * erfi * Nd2 * t;
    var prho = x * erfi * (Nd2 - 1.0) * t;
    var cdvd = -s * edvd * Nd1 * t;
    var pdvd = s * edvd * (1.0 - Nd1) * t;
    return [c, cdelta, gamma, ctheta, vega, crho, cdvd, p, pdelta, gamma, ptheta, vega, prho, pdvd];
}

function implied_volatility(i, p, s, x, rfi, dvd, t) {
    var cv = function(sigma) {
        var sst = sigma * Math.sqrt(t);
        var d1 = (Math.log(s / x) + (rfi - dvd + sigma * sigma / 2.0) * t) / sst;
        var d2 = d1 - sst;
        var Nd1 = cdf_stdgauss(d1);
        var Nd2 = cdf_stdgauss(d2);



        if (i == 7) {
            Nd1 = Nd1 - 1.0;
            Nd2 = Nd2 - 1.0;
        }
        return s * Math.exp(-dvd * t) * Nd1 - x * Math.exp(-rfi * t) * Nd2 - p;
    };
    var cvp = function(sigma) {
        var sst = sigma * Math.sqrt(t);
        var d1 = (Math.log(s / x) + (rfi - dvd + sigma * sigma / 2.0) * t) / sst;
        return s * Math.exp(-dvd * t) * pdf_stdgauss(d1) * Math.sqrt(t);
    };
    return newt_root(0.2, cv, cvp, 0.000001);
}

function newt_root(x, f, fp, tol) {
    var x0;
    for (x0 = x; Math.abs(f(x0)) > tol; x0 -= f(x0) / fp(x0));
    return x0;
}

var dayselect = 23;
var monthselect = 1;
var yearselect = 2020;


function calculate_time2expire() {
    var today = new Date();
    var eday = parseInt(dayselect);
    var emonth = parseInt(monthselect);
    var edate = new Date(yearselect, emonth - 1, eday);
    var days = Math.ceil((edate.getTime() - today.getTime()) / 86400000);
    return days / 365.0;
}

It is working most of the strike prices, but sometimes I get Infinity or - Infinity as output.它适用于大部分执行价格,但有时我会得到 Infinity 或 - Infinity 作为输出。

When I run当我跑

var ceiv = 100.0* implied_volatility(0, 624.65, 12352.35, 11750, 0.069, 0, 0.03287671232876712) var ceiv = 100.0* 隐含波动率(0、624.65、12352.35、11750、0.069、0、0.03287671232876712)

It is returning infinity它正在返回无限

But others strike prices are giving correct IV , For example If i run但是其他人的执行价格给出了正确的 IV ,例如如果我跑

var ceiv = 100.0* implied_volatility(0, 1521.75,31590, 30100, 0.069, 0, 0.0136986301369863)

It gives 19.08它给出了 19.08

Here is parameter这里是参数

implied_volatility(callput, optionprice,spotprice, strikeprice, riskfreeinterest/100, dividend, daytoexpireinyear)

Your result is basically 0.1 * 0.5^100, which is from calling estimate = (estimate - low) / 2 + low 100 times.你的结果基本上是 0.1 * 0.5^100,这是来自调用estimate = (estimate - low) / 2 + low 100 次。 Your initial estimation is 0.1 resulting in a very w in blackScholes and this will cause the probability returned from stdNormCDF having w as a part of the input to always be 1. So, the price will not change even during the iterations when estimation is becoming even smaller.您的初始估计是 0.1,导致在 blackScholes 中非常 w,这将导致从stdNormCDF返回的将 w 作为输入的一部分的概率始终为 1。因此,即使在估计变得均匀的迭代期间,价格也不会改变较小。

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