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Relating Heston Characteristic Functions to Forward Variance Curves

Article Quant Q&A · Author: Rutger Versteegden

Summary

The document explores how the standard Heston characteristic function might be expressed using an affine forward variance curve. It lays out a transform representation involving a Riccati equation and a convolution with the initial forward variance term, then attempts to recover the familiar Heston expression when that curve follows an exponential mean-reverting shape. The derivation raises questions about which time index of the initial curve enters the convolution and whether the initial variance is identified with the curve’s value at time zero.

It also derives the conditional expected variance under mean reversion by applying the product rule to an exponentially scaled variance process. This gives the expected form of the forward variance curve under the stated dynamics. The text is exploratory rather than a verified tutorial: it presents the author’s own uncertainty, and the equations contain notation and indexing ambiguities. Readers should check the model definitions and derivation before relying on the claimed equivalence or applying it in pricing.

Key ideas

  • The Heston characteristic function can be written using an affine transform and a Riccati equation.
  • The author attempts to connect the transform to a convolution over an initial forward variance curve.
  • For a mean-reverting variance process, conditional expected variance decays toward its long-run level.
  • The derivation questions how initial variance and forward variance are indexed.
  • The presented equivalence is tentative and should be independently checked.

Tags

Full text
# How to use the Forward Variance Curve in AFV models


# How to use the Forward Variance Curve in AFV models












We know that the normal Heston model is given by $$ \varphi(u;T) = E\bigl[e^{i u\ln S_T}\bigr] = \exp\!\Bigl( i\,u\,\ln S_0 \;+\;D(T)\,V_0 \;+\;C(T) \Bigr). $$ Where $$ D(t) = \psi_2(t) = \frac{b - d}{\sigma^2} \;\frac{1 - e^{-d\,t}}{1 - g\,e^{-d\,t}}, \\[6pt] C(t) = \int_{0}^{t}\kappa\,\theta\,\psi_2(s)\,ds = \frac{\kappa\,\theta}{\sigma^2} \Bigl[(b - d)\,t \;-\;2\ln\!\frac{1 - g\,e^{-d\,t}}{1 - g}\Bigr], $$

We also know that we can write it in Forward Variance form with the following dynamics $$ dS_t = \sqrt{\xi_t(t)}\,S_t\,dW_t^{1,Q}, \\ d\xi_t(T) = \sigma\,e^{-\kappa_Q\,(T-t)}\,\sqrt{\xi_t(t)}\;dW_t^{2,Q}.\\ d\bigl\langle W^{1,Q},\,W^{2,Q}\bigr\rangle_t = \rho\,dt. $$ With $$ \xi_t(T) = E[\nu_T|F_t] = e^{-\kappa_Q (T-t)}\,\xi_t(t) + \theta_Q\bigl(1-e^{-\kappa_Q (T-t)}\bigr) $$ (Note: am I making a mistake here? Should it actually be $\xi_t(t)$? See derivation #1 below)

Then, we can let the CGF be $$ \varphi(u;T) =E\bigl[e^{iu\ln S_T}\bigr] =\exp\!\Bigl(iu\ln S_0\;+\;\int_0^T F\bigl(\psi_1(s),\psi_2(s)\bigr)\, g_0(T-s)\,\mathrm{d}s\Bigr). $$ With $$\psi_1(s)=iu,\qquad \psi_2(s)=\int_0^sF\bigl(iu,\psi_2(r)\bigr)\,\mathrm{d}r $$ and $F(\psi_1,\psi_2) =\tfrac12(\psi_1^2-\psi_1) +(\rho\sigma\,\psi_1-\kappa)\,\psi_2 +\tfrac{\sigma^2}{2}\,\psi_2^2. $

Now, let $g_0(T-s) =e^{-\kappa(T-s)}V_0+\theta\bigl(1-e^{-\kappa(T-s)}\bigr) $. We now have $V_0 = \xi_0(0)$, correct?

Writing \begin{equation} \psi_2(T)=\int_0^Te^{-\kappa(T-s)}\,F\bigl(iu,\psi_2(s)\bigr)\,ds, \end{equation} And then noting \begin{equation} \begin{aligned} \psi_{2}'(s) &= -\kappa\,\psi_{2}(s) + F(s)\\ \quad\Longrightarrow\quad \int_{0}^{T}F(s)\,ds &= \psi_{2}(T)-\psi_{2}(0) + \kappa\int_{0}^{T}\psi_{2}(s)\,ds = \psi_{2}(T) + \kappa\!\int_{0}^{T}\psi_{2}(s)\,ds, \end{aligned} \end{equation}

one finds that we have \begin{equation} \int_{0}^{T} F(s)\,g_{0}(T - s)\,\mathrm{d}s =V_0\!\int_{0}^{T} e^{-\kappa (T - s)}\,F(s)\,\mathrm{d}s \;+\;\theta\!\int_{0}^{T} F(s)\,\mathrm{d}s \;-\;\theta\!\int_{0}^{T} e^{-\kappa (T - s)}\,F(s)\,\mathrm{d}s. \end{equation} So we get \begin{equation} \int_{0}^{T}F(s)\,g_{0}(T-s)\,ds=V_0\,\psi_{2}(T) + \kappa\,\theta\!\int_{0}^{T}\psi_{2}(s)\,ds. \end{equation}

Hence \begin{equation} \varphi(u;T) =\exp\!\Bigl(iu\ln S_0 +V_0\,\psi_2(T) +\kappa\,\theta\!\int_0^T\psi_2(s)\,ds\Bigr), \end{equation} Then setting $\psi_2(T)=D(T)$ and $\kappa\theta\!\int_0^T\psi_2=C(T)$, is exactly the standard Heston CF with $K\equiv1$.

However, I now don't understand the purpose of the forward variance curve anymore. Say this looks like

Should I simply replace $V_0$ = $\xi_0(0)$ with the value of the forward variance curve at $Maturity = 0$, always? That doesn't make a whole lot of sense? In case i was wrong, and it is not $V_0 =\xi_0(0)$ but $V_0 = \xi_0(T)$, then how do we solve the convolution below?

$$ \int_{0}^{T}F(s)\,g_{0}(T-s)\,\mathrm{d}s =\int_{0}^{T}F(s)\bigl[e^{-\kappa (T-s)}\,\xi_0(T-s? ) +\theta\bigl(1-e^{-\kappa (T-s)}\bigr)\bigr]\,\mathrm{d}s, $$ We clearly can't easily pull out $xi_0(T-s)$ anymore?

Derivation #1 is as follows:

Let $\xi_0(t) = E\big[\nu_T | F_0\big]$. We note first that going from $d\nu_t$ to $\nu_t$ involves using the Ito product rule, given by $d(XY) = YdX + XdY + dYdX$. We work backwards from the solution, by taking $X_s = e^{\kappa_Q s}$ and $Y=\nu_s$ Then, we have $d(X_sY_s) = d(e^{\kappa_Q s}\nu_s) = e^{\kappa_Q s}d\nu_s +\nu_s\kappa_Q e^{\kappa_Q s}ds$ \begin{equation} \begin{aligned} & d\nu_s + \kappa_Q\nu_sds = \kappa_Q\theta_Qds + \sigma\sqrt{\nu_t}\,dW^{2,Q}_s\\ & e^{\kappa_Q s}( d\nu_s + \kappa_Q\nu_sds) = e^{\kappa_Q s}(\kappa_Q\theta_Qds + \sigma\sqrt{\nu_t}\,dW^{2,Q}_s)\\ & d(e^{\kappa_Q s}\nu_s) = e^{\kappa_Q s}(\kappa_Q\theta_Qds + \sigma\sqrt{\nu_t}\,dW^{2,Q}_s)\\ \end{aligned} \end{equation} Then integrating this, where we note $ \int_0^T e^{\kappa_Q s}ds = \frac{1}{\kappa_Q}\bigl(e^{\kappa_Q T}-1\bigr)$ we get \begin{equation} \begin{aligned} & e^{\kappa_Q T}\,\nu_T - \nu_0 = \kappa_Q\,\theta_Q\int_0^T e^{\kappa_Q s}\,ds + \sigma\int_0^T e^{\kappa_Q s}\sqrt{\nu_s}\,dW_s^{2,Q}.\\ &\nu_T = e^{-\kappa_Q T}\,\nu_0 + \theta_Q\bigl(1-e^{-\kappa_Q T}\bigr) + \sigma e^{-\kappa_Q T}\!\int_0^T e^{\kappa_Q s}\sqrt{\nu_s}\,dW_s^{2,Q}.\\ % &\nu_T \end{aligned} \end{equation}

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.