We study the volatility of the S&P500 stock index from 1984 to 1996 and find that the volatility distribution can be very well described by a log-normal function. Further, using detrended fluctuation analysis we show that the volatility is power-law correlated with Hurst exponent α ≌ 0.9.

A method for constructing the model or virtual space of economic events when economic objects can be considered as material ones is suggested. We describe change of share rates in time at stock markets as the potential difference of attracted bodies in time in this virtual space. Each share of...

Truncated Levy distributions of “SP500” stock index fluctuations (Mantegna, Stanley, Nature 376 (1995) 46) are obtained in the formerly introduced model (Romanovsky, Physica A 265 (1999) 264) for stock market. A one-body random kinematics in this space corresponds to the one-shares...

The paper offers an alternative approach to analyzing stock market time series data. The purpose is to develop descriptive, more intuitive, and closer to reality analogs of the behavior of US stock market prices, as indexed by the S&P500 stock price index covering the period October 2003 to...

The modelling of financial markets presents a problem which is both theoretically challenging and practically important. The theoretical aspects concern the issue of market efficiency which may even have political implications, whilst the practical side of the problem has clear relevance to...

Purpose – The purpose of this paper is to empirically test dominant theories and assumptions in behavioral finance, using data from the Standard & Poor's 500 index. Design/methodology/approach – The empirical analysis has three parts: to test the assumption of risk aversion; to examine the...

Purpose – The purpose of this paper is to propose a new method for estimating continuous‐time stochastic volatility (SV) models for the S&P 500 stock index process using intraday high‐frequency observations of both the S&P 500 index and the Chicago Board Options Exchange (CBOE) implied (or...

We present tail risk analysis of cryptocurrencies (Bitcoin, Ethereum and Litecoin), non-fungible tokens, stocks (FTSE 100 and S&P 500) and Gold from November 12, 2017 to March 31, 2022 using conditional model-based Value-at-Risk (VaR). We explored which model specification and distributional...