Girard (p. 9-22)

Cooper (p. 23-32)

Kunz-Osborne (p. 33-41)

Coulmas-Law (p.42-46)

Stasio (p. 47-56)

Albert-Valette-Florence (p.57-63)

Zhang-Rauch (p. 64-70)

Alam-Yasin (p. 71-78)

Mattare-Monahan-Shah (p. 79-94)

Nonis-Hudson-Hunt (p. 95-106)

JOURNAL OF ACCOUNTING AND FINANCE

The correct model of a liquid financial market is one of the most important matters for the management of

all financial market activities including for example a stock or bond porfolio or asset pricing. Clear random walk models, which consider a market price/yield development of liquid financial markets,are to be a random walk within the meaning of a symmetric normal (Gaussian) distribution, and are very useful in accurately explaining many financial market effects. If we study financial markets more closely, we recognize that such development can be partly causal and a clear random walk is only a special case of it. A Dynamic Financial Market Model considers feedback processes of financial markets which cause dependence in the probability of the next price/yield step direction and also expects a mix of random processes as a final result. Both effects cause Gaussian (normal) observations in probability distributions of financial instruments and this is why the model is also able to explain for example effects like thin or fat tails and other deformations in the probability distribution. The S&P500 index or Euro Bond futures probability distribution, on daily basis, are good examples of the diversion from normality. Basic principles of the Dynamic Financial Market Model also help explain some of the S&P 500 index, Dow Jones Industrial Average index, Euro Bond futures or EUR/USD currency development return distribution, which is a departure from a Gaussian curve.