Market volatility modeling for short time window

The gain or loss of an investment can be defined by the movement of the market. This movement can be estimated by the difference between the magnitudes of two stock prices in distinct periods and this difference can be used to calculate the volatility of the markets. The volatility characterizes the sensitivity of a market change in the world economy. Traditionally, the probability density function (pdf) of the movement of the markets is analyzed by using power laws. The contributions of this work is two-fold: (i) an analysis of the volatility dynamic of the world market indexes is performed by using a two-year window time data. In this case, the experiments show that the pdf of the volatility is better fitted by exponential function than power laws, in all range of pdf; (ii) after that, we investigate a relationship between the volatility of the markets and the coefficient of the exponential function based on the Maxwell–Boltzmann ideal gas theory. The results show an inverse relationship between the volatility and the coefficient of the exponential function. This information can be used, for example, to predict the future behavior of the markets or to cluster the markets in order to analyze economic patterns.

[1]  A. Carbone,et al.  Cross-correlation of long-range correlated series , 2008, 0804.2064.

[2]  Victor M. Yakovenko,et al.  Exponential distribution of financial returns at mesoscopic time lags: a new stylized fact , 2004 .

[3]  C. Granger,et al.  The Random Character of Stock Market Prices. , 1965 .

[4]  H. Eugene Stanley,et al.  Scale-Dependent Price Fluctuations for the Indian Stock Market , 2004 .

[5]  F. Reif,et al.  Fundamentals of Statistical and Thermal Physics , 1965 .

[6]  B. Mandelbrot New Methods in Statistical Economics , 1963, Journal of Political Economy.

[7]  F. Diebold,et al.  The distribution of realized stock return volatility , 2001 .

[8]  Victor M. Yakovenko,et al.  Statistical mechanics of money , 2000 .

[9]  S. Kotz,et al.  Statistical Size Distributions in Economics and Actuarial Sciences , 2003 .

[10]  R. Fletcher Practical Methods of Optimization , 1988 .

[11]  P. Cizeau,et al.  Statistical properties of the volatility of price fluctuations. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[13]  L. Amaral,et al.  Can statistical physics contribute to the science of economics , 1996 .

[14]  D. Sornette,et al.  Stretched exponential distributions in nature and economy: “fat tails” with characteristic scales , 1998, cond-mat/9801293.

[15]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .

[16]  A. Lo,et al.  A Non-Random Walk Down Wall Street , 1999 .

[17]  Bikas K. Chakrabarti,et al.  Ideal-gas-like market models with savings: Quenched and annealed cases , 2006, physics/0607258.

[18]  V. Plerou,et al.  Econophysics: financial time series from a statistical physics point of view , 2000 .

[19]  Kazuko Yamasaki,et al.  Scaling and memory in volatility return intervals in financial markets. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[20]  B. Malkiel A Random Walk Down Wall Street , 1973 .

[21]  H. E. Stanley,et al.  Common scaling behavior in finance and macroeconomics , 2010 .

[22]  Tiago Alessandro Espínola Ferreira,et al.  A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks , 2008, Neural Processing Letters.

[23]  P. Gopikrishnan,et al.  Inverse cubic law for the distribution of stock price variations , 1998, cond-mat/9803374.

[24]  Stochastic volatility of financial markets as the fluctuating rate of trading: An empirical study , 2006, physics/0608299.

[25]  Michel L. Goldstein,et al.  Problems with fitting to the power-law distribution , 2004, cond-mat/0402322.

[26]  B. Mandelbrot The Variation of Certain Speculative Prices , 1963 .

[27]  John R. Wolberg,et al.  Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments , 2005 .

[28]  H. Stanley,et al.  Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. , 2007, Physical review letters.

[29]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[30]  V. Yakovenko,et al.  Evidence for the exponential distribution of income in the USA , 2001 .

[31]  Jan A Snyman,et al.  Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms , 2005 .

[32]  E. Fama The Behavior of Stock-Market Prices , 1965 .

[33]  J. Bouchaud,et al.  Scaling in Stock Market Data: Stable Laws and Beyond , 1997, cond-mat/9705087.

[34]  Temporal evolution of the "thermal" and "superthermal" income classes in the USA during 1983-2001 , 2005, cond-mat/0406385.

[35]  Boris Podobnik,et al.  Quantitative relations between risk, return and firm size , 2009 .

[36]  Chung-Kang Peng,et al.  Econophysics : can statistical physics contribute to the science of economics? , 1999 .

[37]  R. Mantegna,et al.  Scaling behaviour in the dynamics of an economic index , 1995, Nature.

[38]  V. Plerou,et al.  Scaling of the distribution of fluctuations of financial market indices. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[39]  H. Stanley,et al.  Cross-correlations between volume change and price change , 2009, Proceedings of the National Academy of Sciences.

[40]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[41]  Gemunu H. Gunaratne,et al.  An Empirical Model for Volatility of Returns and Option Pricing , 2002, ArXiv.

[42]  Victor M. Yakovenko,et al.  Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States , 2001, cond-mat/0103544.

[43]  F. Longin The Asymptotic Distribution of Extreme Stock Market Returns , 1996 .

[44]  Rosario N. Mantegna,et al.  An Introduction to Econophysics: Contents , 1999 .

[45]  Harry Eugene Stanley,et al.  Return Intervals Approach to Financial Fluctuations , 2009, Complex.

[46]  E. Eberlein,et al.  New Insights into Smile, Mispricing, and Value at Risk: The Hyperbolic Model , 1998 .

[47]  V. Plerou,et al.  A unified econophysics explanation for the power-law exponents of stock market activity , 2007 .

[48]  R. Gorvett Why Stock Markets Crash: Critical Events in Complex Financial Systems , 2005 .

[49]  Celia Anteneodo,et al.  Power-law distributions in economics: a nonextensive statistical approach (Invited Paper) , 2005, SPIE International Symposium on Fluctuations and Noise.

[50]  Hubert Fromlet Behavioral Finance-Theory and Practical Application , 2001 .