Premium
Telling from Discrete Data Whether the Underlying Continuous‐Time Model Is a Diffusion
Author(s) -
AïtSahalia Yacine
Publication year - 2002
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/1540-6261.00489
Subject(s) - classification of discontinuities , jump , sampling (signal processing) , diffusion , discrete time and continuous time , statistical physics , sample (material) , jump diffusion , focus (optics) , econometrics , diffusion process , path (computing) , mathematics , path dependent , computer science , statistics , innovation diffusion , mathematical analysis , physics , knowledge management , filter (signal processing) , quantum mechanics , optics , computer vision , thermodynamics , programming language
Can discretely sampled financial data help us decide which continuous‐time models are sensible? Diffusion processes are characterized by the continuity of their sample paths. This cannot be verified from the discrete sample path: Even if the underlying path were continuous, data sampled at discrete times will always appear as a succession of jumps. Instead, I rely on the transition density to determine whether the discontinuities observed are the result of the discreteness of sampling, or rather evidence of genuine jump dynamics for the underlying continuous‐time process. I then focus on the implications of this approach for option pricing models.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom