Money, goods and some statistics. Addition

    In the article “Money, goods and some statistics” I described a method of statistical analysis of long-term price dynamics and building a diversified product — a basket of goods for which the standard deviation of its relative price over a certain period of time is minimal.
    Let me remind you, at the beginning, the relative prices of goods are calculated, then their covariance matrix, and using the method of Lagrange multipliers the conditional minimum is calculated.

    In this article, I will try to explore a few more dependencies.
    Under cat graphics.

    First of all, I tried to build two DPs (diversified products).
    DP1 - fuel and metals (crude oil, natural gas, gold, platinum, silver, aluminum, copper, zinc, nickel, tin, lead, iron ore).
    DP2 - renewable resources (food: barley, corn, rice, sorghum, soy, wheat, beef, chicken, as well as logs, cotton, cocoa, coffee, sugar, tobacco and tea).

    Charts - prices of DP1 and DP2 in dollars (normalized to the average over time), as well as the relative price:

    As you can see, from 1960 to the present there has been a stable trend towards cheaper food and other renewable resources in relation to fuel and metals.

    Next, let's try to compare the cost of DP1 and the volume of the dollar mass - monetary aggregates M1 and M2.

    The graphs are normalized to the time average, the correlation coefficients are 85.18% and 89.67%, respectively.

    Further. DP1 and the real estate price index in the USA (taken here ).

    As can be seen from the graph, before the mortgage crisis in the United States and the global economic crisis provoked by it, real estate prices (relative to fuel and metals) rose, then fell to the level of the 70s.

    I also tried to build a DP from the shares of HiTech companies - Apple, Ford Motor, General Electric, Google, Microsoft, Intel and Yahoo.
    The data was taken from the Nasdaq, unfortunately, in just 10 years.

    On the upper graph, prices for DP1 and DP-HiTech, normalized to the average time.
    At the bottom - their ratio.

    The graph shows that at present there is a trend towards a relative increase in the prices of high-tech stocks.

    That's all for now.

    Scripts and data files here:

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