Closed-Form Expansion, Conditional Expectation, and Option Valuation

2010 ◽  
Author(s):  
Chenxu Li
Keyword(s):  
Closed Form ◽  
Conditional Expectation ◽  
Option Valuation

scholarly journals Closed Form Spread Option Valuation

2006 ◽  
Author(s):  
Petter Bjerksund ◽  
Gunnar Stensland
Keyword(s):  
Closed Form ◽  
Option Valuation ◽  
Spread Option

scholarly journals Closed form spread option valuation

2011 ◽  
Vol 14 (10) ◽  
pp. 1785-1794 ◽  
Author(s):  
Petter Bjerksund ◽  
Gunnar Stensland
Keyword(s):  
Closed Form ◽  
Option Valuation ◽  
Spread Option

scholarly journals A New Efficient Expression for the Conditional Expectation of the Blind Adaptive Deconvolution Problem Valid for the Entire Range ofSignal-to-Noise Ratio

Entropy ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 72 ◽  
Author(s):  
Monika Pinchas
Keyword(s):  
Probability Density Function ◽  
Closed Form ◽  
Maximum Entropy ◽  
Conditional Expectation ◽  
Entropy Density ◽  
Approximation Technique ◽  
Density Approximation ◽  
Signal To Noise ◽  
Computational Burden ◽  
Blind Adaptive

In the literature, we can find several blind adaptive deconvolution algorithms based on closed-form approximated expressions for the conditional expectation (the expectation of the source input given the equalized or deconvolutional output), involving the maximum entropy density approximation technique. The main drawback of these algorithms is the heavy computational burden involved in calculating the expression for the conditional expectation. In addition, none of these techniques are applicable for signal-to-noise ratios lower than 7 dB. In this paper, I propose a new closed-form approximated expression for the conditional expectation based on a previously obtained expression where the equalized output probability density function is calculated via the approximated input probability density function which itself is approximated with the maximum entropy density approximation technique. This newly proposed expression has a reduced computational burden compared with the previously obtained expressions for the conditional expectation based on the maximum entropy approximation technique. The simulation results indicate that the newly proposed algorithm with the newly proposed Lagrange multipliers is suitable for signal-to-noise ratio values down to 0 dB and has an improved equalization performance from the residual inter-symbol-interference point of view compared to the previously obtained algorithms based on the conditional expectation obtained via the maximum entropy technique.

A Closed-Form GARCH Option Valuation Model

2000 ◽  
Vol 13 (3) ◽  
pp. 585-625 ◽  
Author(s):  
Steven L. Heston ◽  
Saikat Nandi
Keyword(s):  
Closed Form ◽  
Option Valuation ◽  
Valuation Model

CLOSED FORM VALUATION OF AMERICAN BARRIER OPTIONS

2001 ◽  
Vol 04 (02) ◽  
pp. 355-359 ◽  
Author(s):  
ESPEN GAARDER HAUG
Keyword(s):  
Numerical Methods ◽  
Closed Form ◽  
Lattice Models ◽  
Option Valuation ◽  
Barrier Options ◽  
Barrier Option ◽  
Trinomial Tree ◽  
Insight Into

Closed form formulae for European barrier options are well known from the literature. This is not the case for American barrier options, for which no closed form formulae have been published. One has therefore had to resort to numerical methods. Lattice models like a binomial or a trinomial tree, for valuation of barrier options are known to converge extremely slowly, compared to plain vanilla options. Methods for improving the algorithms have been described by several authors. However, these are still numerical methods that are quite computer intensive. In this paper we show how some American barrier options can be valued analytically in a very simple way. This speeds up the valuation dramatically as well as give new insight into barrier option valuation.

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