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Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models
Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models
2015/8/5
A solution method and an estimation method for nonlinear rational expectations
models are presented in this paper. The solution method can be used in forecasting and
policy applications and can hand...
Maximum likelihood approach for several stochastic volatility models
Maximum likelihood approach several stochastic volatility models Computational Finance
2012/4/28
Volatility measures the amplitude of price fluctuations. Despite it is one of the most important quantities in finance, volatility is not directly observable. Here we apply a maximum likelihood method...
Maximum entropy distribution of stock price fluctuations
Maximum entropy distribution stock price fluctuations
2011/7/4
The principle of absence of arbitrage opportunities allows obtaining the distribution of
stock price fluctuations by maximizing its information entropy. This leads to a physical
description of the u...
Optimal Portfolio Diversification Using Maximum Entropy Principle
Diversification Entropy measure Portfolio selection Shrinkage rule Simulation methods
2011/4/2
Markowitz’s mean-variance (MV) efficient portfolio selection is one of the most widely used approaches in solving portfolio diversification problem. However, contrary to the notion of diversification,...
A Family of Maximum Entropy Densities Matching Call Option Prices
Entropy Information Theory I-Divergence Asset Distribution Option Pricing
2011/3/23
We investigate the position of the Buchen-Kelly density in a family of entropy maximising densities which all match European call option prices for a given maturity observed in the market. Using the L...
Zipf's law states that the number of firms with size greater than S is inversely proportional to S. Most explanations start with Gibrat's rule of proportional growth but require additional constraints...
Selling a stock at the ultimate maximum
Geometric Brownian motion optimal prediction optimal stopping ultimate maximum parabolic free-boundary problem smooth fit
2010/11/2
Assuming that the stock price Z = (Zt)0≤t≤T follows a geometric Brownian motion with drift μ 2 R and volatility > 0, and letting Mt = max0≤s≤t Zs for t 2 [0,T].
Maximum penalized quasi-likelihood estimation of the diffusion function
Maximum penalized quasi-likelihood estimation diffusion function
2010/10/21
We develop a maximum penalized quasi-likelihood estimator for estimating in a nonparametric way the diffusion function of a diffusion process, as an alternative to more traditional kernel-based estima...
A maximum principle for forward-backward stochastic Volterra integral equations and applications in finance
Forward-backward stochastic Volterra integral equations Adapted
2010/10/20
This paper formulates and studies a stochastic maximum principle for forward-backward stochastic Volterra integral equations (FBSVIEs in short), while the control area is assumed to be convex. Then a ...
Maximum Entropy Distributions Inferred from Option Portfolios on an Asset
Entropy Information Theory I-Divergence Asset Distribution Option Pricing Volatility Smile
2010/10/29
We obtain the Maximum Entropy distribution for an asset from call and digital option prices. A
rigorous mathematical proof of its existence and exponential form is given, which can also be applied to...
A general "bang-bang" principle for predicting the maximum of a random walk
Bernoulli random walk Brownian motion optimal prediction ultimate maximum stopping time convex function
2010/11/2
Let (Bt)0tT be either a Bernoulli random walk or a Brownian motion with drift, and let Mt := max{Bs : 0 s t}, 0 t T. This paper solves the general optimal prediction problem sup 0 T E[f(MT...
From Physics to Economics: An Econometric Example Using Maximum Relative Entropy
econophyisics econometrics entropy maxent bayes
2010/10/29
Econophysics, is based on the premise that some ideas and methods from physics can be applied to economic situations. We intend to show in this paper how a physics concept such as entropy can be appli...
Maximum entropy autoregressive conditional heteroskedasticity model
Maximum entropy density ARCH models Excess kurtosis Asymmetry Peakedness of distribution Stock returns data
2011/4/2
In many applications, it has been found that the autoregressive conditional heteroskedasticity (ARCH) model under the conditional normal or Student’s t distributions are not general enough to account ...
Finite-sample Properties of Maximum Likelihood and Whittle Estimators in EGARCH and FIEGARCH Models
EGARCH fractionally integrated EGARCH maximum likelihood estimator
2010/9/7
EGARCH models for conditionally heteroscedastic time series have attracted a steadily increasing degree of attention in financial econometrics and related fields. These models are able to represent so...
Spatial data modelling and maximum entropy theory
spatial data classification distribution function error distribution and maximum entropy approach
2014/3/20
Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution fun...