Option price contains information on the distribution of the underlying asset. Under insufficient condition we employ the maximum entropy principle to estimate the probability density of the asset price. The problem is equivalent to finding the Lagrange multipliers of a linear functional defined by entropy and payoff functions. Buchen and Kelly proved that the maximum entropy distribution recovered from observed option prices is quite similar with the original asset distribution. In this article we apply a similar method to recover the probability density function of an asset from given option prices for binary options and European options.

This paper describes the application of the Principle of Maximum Entropy to the estimation of the distribution of an underlying asset from a set of option prices. The resulting distribution is least committal with respect to unknown or missing information and is hence the least prejudiced. The maximum entropy distribution is the only information about the asset that can be inferred from the price data alone. An extension to the Principle of Minimum Cross-Entropy allows the inclusion of prior knowledge of the asset distribution. We show that the maximum entropy distribution is able to accurately fit a known density, given simulated option prices at different strikes.

The Black-Scholes option pricing model has been the foundation of option pricing analysis. Yet as well known as the model itself, its empirical deficiencies are also well documented. Option prices generated by the Black-Scholes formula are often found to systematically differ from observed prices. The patterns of mispricing are generally believed to result from violations of one or more assumptions underlying the Black-Scholes option pricing model, such as the natural logarithm of the underlying stock price following a normal distribution with a variance that increases exactly linearly with time. This dissertation concerns an evaluation of the Principle of Maximum Entropy as a method for recovering a probability density function from stock index option prices. Theoretically, the resulting probability density is "the least prejudiced estimate since it is maximally noncommittal with respect to missing or unknown information." Empirically, this dissertation demonstrates that entropy valuation gives much stronger performance than does the Black-Scholes model in pricing stock index options on the S & P 500 and on the Dow Jones Industrial Average.

This dissertation studies density estimation and portfolio selection problems using the maximum entropy (ME) principle. Since an entropy measure turns out to be a distance measure between two distributions, it can be used to estimate unknown density function. Entropy can be also interpreted as a measure of the degree of diversification and thus provides an useful way to construct optimal portfolio weights. In this dissertation three subjects are studied extensively. First, we propose ME autoregressive conditional heteroskedasticity model with demonstrating how we can extract informative functional from the data in the form of moment function. Second, the portfolio selection problem is considered using ME principle. We propose to use cross entropy measure as the objective function (to minimize) with side conditions coming from the mean and variance-covariance matrix of the resampled asset returns. Finally, using ME principle, we provided characterization of some well-known income distributions and flexible parametric income distributions which satisfy certain stylized facts of personal income data. Empirical results showed that maximum entropy principle is quite useful for analyzing economic and financial data.

The two-volume set LNCS 10671 and 10672 constitutes the thoroughly refereed proceedings of the 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, held in Las Palmas de Gran Canaria, Spain, in February 2017. The 117 full papers presented were carefully reviewed and selected from 160 submissions. The papers are organized in topical sections on: pioneers and landmarks in the development of information and communication technologies; systems theory, socio-economic systems and applications; theory and applications of metaheuristic algorithms; stochastic models and applications to natural, social and technical systems; model-based system design, verification and simulation; applications of signal processing technology; algebraic and combinatorial methods in signal and pattern analysis; computer vision, deep learning and applications; computer and systems based methods and electronics technologies in medicine; intelligent transportation systems and smart mobility.

This paper suggests a new method of implementing the principle of maximum entropy to retrieve the risk neutral density of future stock, or any other asset, returns from European call and put prices. Instead of options prices used by previous studies the method maximizes the entropy measure subject to values of the risk neutral moments. These moments can be retrieved from market option prices in a first step, at each point of time. Compared to other existing methods of retrieving the risk neutral density based on the principle of maximum entropy, the benefits of the method that the paper suggests is the use of all the available information provided by the market more sufficiently. To evaluate the performance of the suggested method, the paper compares the new method proposed to other risk neutral density estimation techniques based on a number of simulation and empirical exercises.