The intuition behind the Taylor expansion

In quantitative finance, where complex models are the norm, Taylor's expansion offers a way to approximate these models with simpler forms. This technique is vital for quick decision-making in trading, sensitivity analysis, pricing exotic instruments, and in numerical methods.

Imagine you're hiking on a mountain, and at some point on your hike, you want to predict the altitude for the next few steps. If the mountain is perfectly flat where you are, you'd assume the altitude remains the same. If it's sloping upwards, you'd expect the altitude to increase as you move forward, and if there's a curve or a bend, your prediction would take that into account as well.

The Taylor expansion essentially does the same thing but for functions, using derivatives:

-0th order: The first, or zeroth, term of the Taylor series is just the value of the function at that point. This is like saying, "Based on where I'm standing, I estimate the altitude around me to be the same as right here."

-1st order: The first-order term involves the first derivative (slope). This accounts for whether the function is increasing or decreasing at that point. It's like noticing that the mountain trail is sloping upwards, so you expect to climb as you move forward.

-2nd order: The second-order term considers the second derivative, which gives us an idea about the curvature or concavity of the function. This is akin to observing how the trail bends or curves as it slopes.

-Higher orders: As you include higher and higher order terms, you account for more intricate bends, wiggles, and behaviors of the function.

The Taylor expansion is all about locally approximating a function using polynomials (which are easier to work with), starting from the most basic approximation (just a flat horizontal line) and adding complexity (slopes, curves, etc.) as we incorporate higher-order terms.

The higher the order of the term, the more subtle and detailed the behavior it captures.

Let's take the function f(x) = e^(-x^2), which represents the bell curve or Gaussian function. This function is fundamental in probability theory and statistics, but it's notoriously difficult to integrate. 

Let's approximate the function near x = 0 using the Taylor expansion.

1. Zeroth Order :
f(0) = e^0 = 1

2. First Derivative:
f'(x) = -2x e^(-x^2)

At x=0, f'(0) = 0. This indicates that there's neither growth nor decline at x = 0, as expected for the peak of a bell curve.

3. Second Derivative:

f''(x) = (4x^2 - 2) e^(-x^2)
At x=0, f''(0) = -2. A negative second derivative indicates that the function is concave down at x = 0.

From the Taylor expansion, the approximate function near x=0 is:

f(x) approx 1 - x^2

This is a parabola and gives a simplified representation of our bell curve near x=0.

The higher you go in derivatives, the more refined and detailed information you get about the function's behavior close to that point.

#QuantitativeFinance #TaylorExpansion

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