Abstract
This chapter is an introduction for the methods and content of the book. After a brief description of the book’s contents, we give results in a simple setting: the underlying process X is a one-dimensional Lévy process which is either continuous, or has finitely many jumps in finite intervals. The process is discretized along a regular grid of mesh Δ n which eventually goes to 0, and we introduce two kinds of functionals of interest for this setting:
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1.
The “unnormalized functional” \(V^{n}(f,X)_{t}=\sum_{i=1}^{[t/\varDelta _{n}]}f(X_{i\varDelta _{n}}-X_{(i-1)\varDelta _{n}})\) for any given test function f, and where [t/Δ n ] denotes the integer part of t/Δ n .
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2.
The “normalized functional” \(V'^{n}(f,X)_{t}=\varDelta _{n}\sum_{i=1}^{[t/\varDelta _{n}]}f((X_{i\varDelta _{n}}-X_{(i-1)\varDelta _{n}})/\sqrt{\varDelta _{n}}\,)\), where the (inside) normalized factor \(\sqrt{\varDelta _{n}}\) is chosen so that when X is a Brownian motion the argument of f in each summand is a standard normal variable.
Then, with X as described above, we explain the sort of limiting behavior one may expect for these functionals: the convergence in probability towards a suitable limit, and the associated Central Limit Theorem. The simple setting allows one to give a heuristic explanation of the results, and of the conditions on the test function f which are necessary to obtain these results.
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© 2012 Springer-Verlag Berlin Heidelberg
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Jacod, J., Protter, P. (2012). Introduction. In: Discretization of Processes. Stochastic Modelling and Applied Probability, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24127-7_1
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DOI: https://doi.org/10.1007/978-3-642-24127-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24126-0
Online ISBN: 978-3-642-24127-7
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