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Calibration

Calibration#

Early pointers

Calibrating ABC#

For calibration we use [Mer14]. Lets consider the Heston model as a test case

from quantflow.sp.heston import Heston

pr = Heston.create(vol=0.6, kappa=1.3, sigma=0.8, rho=-0.6)
pr.variance_process.is_positive

True

The Heston model is a classical example where the calibration of parameters requires to deal with the estimation of an unobserved random variable, the stochastic variance. The model can be discretized as follow:

(70)#\[\begin{align} d \nu_t &= \kappa\left(\theta -\nu_t\right) dt + \sigma \sqrt{\nu_t} d z_t \\ d s_t &= -\frac{\nu_t}{2}dt + \sqrt{\nu_t} d w_t \\ {\mathbb E}\left[d w_t d z_t\right] &= \rho dt \end{align}\]

noting that

(71)#\[\begin{equation} d z_t = \rho d w_t + \sqrt{1-\rho^2} d b_t \end{equation}\]

which leads to

(72)#\[\begin{align} d \nu_t &= \kappa\left(\theta -\nu_t\right) dt + \sigma \sqrt{\nu_t} \rho d w_t + \sigma \sqrt{\nu_t} \sqrt{1-\rho^2} d b_t \\ d s_t &= -\frac{\nu_t}{2}dt + \sqrt{\nu_t} d w_t \\ \end{align}\]

and finally

(73)#\[\begin{align} d \nu_t &= \kappa\left(\theta -\nu_t\right) dt + \sigma \rho \frac{\nu_t}{2} dt + \sigma \sqrt{\nu_t} \sqrt{1-\rho^2} d b_t + \sigma \rho d s_t\\ d s_t &= -\frac{\nu_t}{2}dt + \sqrt{\nu_t} d w_t \\ \end{align}\]

Our problem is to find the best estimate of \(\nu_t\) given by ths equation based on the observations \(s_t\).

The Heston model is a dynamic model which can be represented by a state-space form: \(X_t\) is the state while \(Z_t\) is the observable

(74)#\[\begin{align} X_{t+1} &= f\left(X_t, \Theta\right) + B^x_t\\ Z_t &= h\left(X_t, \Theta\right) + B^z_t \\ B^x_t &= {\cal N}\left(0, Q_t\right) \\ B^z_t &= {\cal N}\left(0, R_t\right) \\ \end{align}\]

\(f\) is the state transition equation while \(h\) is the measurement equation.

the state equation is given by

(75)#\[\begin{align} X_{t+1} &= \left[\begin{matrix}\kappa\left(\theta\right) dt \\ 0\end{matrix}\right] + \end{align}\]
[p for p in pr.variance_process.parameters]

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[2], line 1
----> 1 [p for p in pr.variance_process.parameters]

File ~/.cache/pypoetry/virtualenvs/quantflow-lUXkzsy2-py3.12/lib/python3.12/site-packages/pydantic/main.py:767, in BaseModel.__getattr__(self, item)
    764     return super().__getattribute__(item)  # Raises AttributeError if appropriate
    765 else:
    766     # this is the current error
--> 767     raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}')

AttributeError: 'CIR' object has no attribute 'parameters'

Calibration against historical timeseries#

We calibrate the Heston model agais historical time series, in this case the measurement is the log change for a given frequency.

(76)#\[\begin{align} F_t &= \left[\begin{matrix}1 - \kappa\theta dt \\ 0\end{matrix}\right] \\ Q_t &= \left[\begin{matrix}1 - \kappa\theta dt \\ 0\end{matrix}\right] \\ z_t &= d s_t \end{align}\]

The observation vector is given by

(77)#\[\begin{align} x_t &= \left[\begin{matrix}\nu_t && w_t && z_t\end{matrix}\right]^T \\ \bar{x}_t = {\mathbb E}\left[x_t\right] &= \left[\begin{matrix}\nu_t && 0 && 0\end{matrix}\right]^T \end{align}\]
from quantflow.data.fmp import FMP
frequency = "1min"
async with FMP() as cli:
    df = await cli.prices("ETHUSD", frequency)
df = df.sort_values("date").reset_index(drop=True)
df

import plotly.express as px
fig = px.line(df, x="date", y="close", markers=True)
fig.show()

import numpy as np
from quantflow.utils.volatility import parkinson_estimator, GarchEstimator
df["returns"] = np.log(df["close"]) - np.log(df["open"])
df["pk"] = parkinson_estimator(df["high"], df["low"])
ds = df.dropna()
dt = cli.historical_frequencies_annulaized()[frequency]
fig = px.line(ds["returns"], markers=True)
fig.show()

import plotly.express as px
from quantflow.utils.bins import pdf
df = pdf(ds["returns"], num=20)
fig = px.bar(df, x="x", y="f")
fig.show()

g1 = GarchEstimator.returns(ds["returns"], dt)
g2 = GarchEstimator.pk(ds["returns"], ds["pk"], dt)

import pandas as pd
yf = pd.DataFrame(dict(returns=g2.y2, pk=g2.p))
fig = px.line(yf, markers=True)
fig.show()

r1 = g1.fit()
r1

r2 = g2.fit()
r2

sig2 = pd.DataFrame(dict(returns=np.sqrt(g2.filter(r1["params"])), pk=np.sqrt(g2.filter(r2["params"]))))
fig = px.line(sig2, markers=False, title="Stochastic volatility")
fig.show()

class HestonCalibration:
    
    def __init__(self, dt: float, initial_std = 0.5):
        self.dt = dt
        self.kappa = 1
        self.theta = initial_std*initial_std
        self.sigma = 0.2
        self.x0 = np.array((self.theta, 0))
    
    def prediction(self, x):
        return np.array((x[0] + self.kappa*(self.theta - x[0])*self.dt, -0.5*x[0]*self.dt))
    
    def state_jacobian(self):
        """THe Jacobian of the state equation"""
        return np.array(((1-self.kappa*self.dt, 0),(-0.5*self.dt, 0)))

c = HestonCalibration(dt)
c.x0

c.prediction(c.x0)

c.state_jacobian()
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