By Taisei Kaizoji (auth.), Arnab Chatterjee, Bikas K Chakrabarti (eds.)
This e-book reports the most recent econophysics researches at the fluctuations in inventory, currency and different markets. The statistical modeling of markets, utilizing a number of agent-based online game theoretical techniques, and their scaling research were discussed.
The major researchers in those fields have suggested on their contemporary paintings and in addition reviewed the modern literature. a few old views in addition to a few reviews and debates on contemporary matters in econophysics study have additionally been included.
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Extra info for Econophysics of Stock and other Markets: Proceedings of the Econophys-Kolkata II
44 V. Kulkarni and N. 3 ‘Last’ eigenmode and the variability index The eigenstates of C deviating from RMT predictions bring out the collective response of the market to perturbations. Collective motion of all the assets in the portfolio is significantly high, or the stocks are highly correlated in regimes marked by occasional or persisting bursts of activity. The degree of such synchronization is indicated by the eigenvector corresponding to the largest eigenvalue, through the evolution of its structure and components, seen in a.
Note that, both of the above-mentioned studies looked at low-resolution data, namely, the daily closing time series. In this study, we have looked at the high-frequency transaction by transaction stock price data, as well as taken a fresh look at the low-frequency daily data. We conclude that, far from being different, the distribution of price fluctuations in Indian markets is quantitatively almost identical to that of developed markets. However, the distributions for trading volume and number of trades seem to be market-specific, with the Indian data being consistent with a log-normal distribution for both of these quantities.
Hence, when θ = 0, we have the minimum risk portfolio, and when θ = 1, we have the maximum return (maximum risk) portfolio. The higher the value of θ, the higher the expected portfolio return rP,θ and, consequently, the higher the risk the investor is willing to absorb. We define a single measure, the weighted portfolio layer as wi lev(vit ), lP (t, θ) = (4) i∈P(t,θ) N where i=1 wi = 1 and further, as a starting point, the constraint wi ≥ 0 for all i, which is equivalent to assuming that there is no short-selling.
Econophysics of Stock and other Markets: Proceedings of the Econophys-Kolkata II by Taisei Kaizoji (auth.), Arnab Chatterjee, Bikas K Chakrabarti (eds.)