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  2. Monte Carlo methods for option pricing - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_methods_for...

    For example, for bond options [3] the underlying is a bond, but the source of uncertainty is the annualized interest rate (i.e. the short rate). Here, for each randomly generated yield curve we observe a different resultant bond price on the option's exercise date; this bond price is then the input for the determination of the option's payoff.

  3. Stock correlation network - Wikipedia

    en.wikipedia.org/wiki/Stock_correlation_network

    The popular method for connecting two correlated stocks is the minimum spanning tree method. The other methods are, planar maximally filtered graph, and winner take all method. In all three methods, the procedure for finding correlation between stocks remains the same. Step 1: Select the desired time series data.

  4. Monte Carlo methods in finance - Wikipedia

    en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance

    For example, for bonds, and bond options, [13] under each possible evolution of interest rates we observe a different yield curve and a different resultant bond price. To determine the bond value, these bond prices are then averaged; to value the bond option, as for equity options, the corresponding exercise values are averaged and present valued.

  5. Financial correlation - Wikipedia

    en.wikipedia.org/wiki/Financial_correlation

    The binomial correlation approach of equation (5) is a limiting case of the Pearson correlation approach discussed in section 1. As a consequence, the significant shortcomings of the Pearson correlation approach for financial modeling apply also to the binomial correlation model. [citation needed]

  6. Markov chain Monte Carlo - Wikipedia

    en.wikipedia.org/wiki/Markov_chain_Monte_Carlo

    In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution.Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution.

  7. Surrogate model - Wikipedia

    en.wikipedia.org/wiki/Surrogate_model

    A surrogate model is an engineering method used when an outcome of interest cannot be easily measured or computed, so an approximate mathematical model of the outcome is used instead. Most engineering design problems require experiments and/or simulations to evaluate design objective and constraint functions as a function of design variables.

  8. Answer set programming - Wikipedia

    en.wikipedia.org/wiki/Answer_set_programming

    Answer set programming (ASP) is a form of declarative programming oriented towards difficult (primarily NP-hard) search problems. It is based on the stable model (answer set) semantics of logic programming .

  9. STELLA (programming language) - Wikipedia

    en.wikipedia.org/wiki/STELLA_(programming_language)

    STELLA (short for Systems Thinking, Experimental Learning Laboratory with Animation; also marketed as iThink) is a visual programming language for system dynamics modeling introduced by Barry Richmond in 1985. The program, distributed by isee systems (formerly High Performance Systems) allows users to run models created as graphical ...