Quantitative Analysis
Parallel Processing
Numerical Analysis
C++ Multithreading
Python for Excel
Python Utilities
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I. Basic math.
II. Pricing and Hedging.
III. Explicit techniques.
IV. Data Analysis.
1. Time Series.
A. Time series forecasting.
B. Updating a linear forecast.
C. Kalman filter I.
a. Kalman filter computation at t=1.
b. Kalman filter computation for general t.
c. Calibration of parameters with Kalman filter.
D. Kalman filter II.
E. Simultaneous equations.
2. Classical statistics.
3. Bayesian statistics.
V. Implementation tools.
VI. Basic Math II.
VII. Implementation tools II.
VIII. Bibliography
Notation. Index. Contents.

Calibration of parameters with Kalman filter.

uppose that the functions $A_{t},H_{t},...$ depend on vector of unknown parameters $\theta$ . Recall that all the processes are Gaussian. According to the previous section if we have a list of observations MATH then we can form a likelihood function MATH where the $p$ is the density of the normal random variable MATH With the likelihood function known we can take a classical position and maximize it ( Maximal likelihood ) or take a Bayesian position ( Bayesian statistics ) and do MCMC.

Notation. Index. Contents.

Copyright 2007