skew-lognormal-cascade-distribution.org

Skew Lognormal Cascade Distribution: Tail risk is omnipresent

Skew Lognormal Cascade Distribution. Tail risk is omnipresent. Our world is more volatile than we want to admit. Let's navigate the world of Fat Tails [ wiki. Aka Heavy Tails [ wiki. This website is set up to present the Skew Lognormal Cascade Distribution. Proposed by Stephen Lihn in 2008; and subsequently Poisson Subordinated Distribution. More and more evidences tell us that tail risk is omnipresent (it is everywhere, universal). This distribution exhibits fat-tail. Asymmetry tunable by a skew. The pl...

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Skew Lognormal Cascade Distribution: Tail risk is omnipresent | skew-lognormal-cascade-distribution.org Reviews

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Skew Lognormal Cascade Distribution. Tail risk is omnipresent. Our world is more volatile than we want to admit. Let's navigate the world of Fat Tails [ wiki. Aka Heavy Tails [ wiki. This website is set up to present the Skew Lognormal Cascade Distribution. Proposed by Stephen Lihn in 2008; and subsequently Poisson Subordinated Distribution. More and more evidences tell us that tail risk is omnipresent (it is everywhere, universal). This distribution exhibits fat-tail. Asymmetry tunable by a skew. The pl...

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Skew Lognormal Cascade Distribution: Tail risk is omnipresent | skew-lognormal-cascade-distribution.org Reviews

https://skew-lognormal-cascade-distribution.org

Skew Lognormal Cascade Distribution. Tail risk is omnipresent. Our world is more volatile than we want to admit. Let's navigate the world of Fat Tails [ wiki. Aka Heavy Tails [ wiki. This website is set up to present the Skew Lognormal Cascade Distribution. Proposed by Stephen Lihn in 2008; and subsequently Poisson Subordinated Distribution. More and more evidences tell us that tail risk is omnipresent (it is everywhere, universal). This distribution exhibits fat-tail. Asymmetry tunable by a skew. The pl...

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skew-lognormal-cascade-distribution.org

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Applications of Skew Lognormal Cascade Distribution

http://www.skew-lognormal-cascade-distribution.org/apps

Applications of Skew Lognormal Cascade Distribution. Our Financial System is Full of Fat Tails (aka Heavy Tails). This page presents the most well known financial time series fitted by the skew lognormal cascade distribution. The fat tails are so clear under this microscope. Here are what I got so far. I tried to sort them by kurtosis (or eta more or less). Fat Tails Sorted By Kurtosis. Note: Kurtosis scales up with total duration, while scales down with sampling interval.). To preview the charts). Russe...

2

Double Pareto

http://www.skew-lognormal-cascade-distribution.org/pareto

There is a fundamental link between Lognormal Cascade Distribution and Double Pareto Distribution. But I have not been able to make a good sense out of them. Double Pareto is not only a stable solution of Komogorov diffusion equation, it is also a extreme case of the ground state of a quantum well (when the width of well is zero). So a Double Pareto distribution (or Lognormal Cascade) implies the underlying population is in a confined state, which brings me to think about Financial Cycles.

3

Lihn's law: The N^3/2 power law

http://www.skew-lognormal-cascade-distribution.org/Lihn's_law.htm

Lihn's law: The N 3/2 power law (copy from the deleted page on Wikipedia). Lihn's N 3/2 power law. States that the total value of a financial market in equilibrium is proportional to the 3/2 power of the number of constituents (e.g. stocks) in the market ( n. This scaling law was first formulated by Stephen H. Lihn in 2009. There are strong statistical reasons for the n. In order to survive as a viable market. The statistical result based on these assumptions is the n. The first pillar of the n. Law is p...

4

Study of Financial Cycles

http://www.skew-lognormal-cascade-distribution.org/cycle

Study of Financial Cycles. It has occurred to me that financial time series are far from simple random walks (even with fat tails). Financial markets exhibit cycles. My focus is on stocks, bonds, and inflation (gold). Two cycles are particularly interesting:. Short cycles that run for 2-5 years. The word "quadrennial" may approximately describe it. There appears to be two major long-run cycles - 30 years and 60 years. Bonds Ran Out of Steam. A Case For Gold. How high can gold price be?

5

Demo of Skew Lognormal Cascade Distribution

http://www.skew-lognormal-cascade-distribution.org/cgi-bin/gd-skew.pl

Demo of Skew Lognormal Cascade Distribution: Have fun playing! Eta (η) =. Range: 0.00 to 9.99). Definition, see this paper. Beta (β) =. Range: -9.99 to 9.99). Range: 0.00 to 9.99). If you don't know where to start, try η = 0.5, β = -0.3, g. 01 That is approximately the parameters for the US stock market. The tail gets fatter rapidly when η 0.5. The drop at the very last bit of the right tail is. Due to lack of data beyond the range. It does NOT reflect. The real tail behavior.

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Heavy-tailed distribution - Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Heavy-tailed_distribution

From Wikipedia, the free encyclopedia. Whose tails are not exponentially bounded:. That is, they have heavier tails than the exponential distribution. In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be heavy. There are three important subclasses of heavy-tailed distributions: the fat-tailed distributions. And the subexponential distributions. Definition of heavy-tailed distribution. The distribution of a r...

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Skew Lognormal Cascade Distribution: Tail risk is omnipresent

Skew Lognormal Cascade Distribution. Tail risk is omnipresent. Our world is more volatile than we want to admit. Let's navigate the world of Fat Tails [ wiki. Aka Heavy Tails [ wiki. This website is set up to present the Skew Lognormal Cascade Distribution. Proposed by Stephen Lihn in 2008; and subsequently Poisson Subordinated Distribution. More and more evidences tell us that tail risk is omnipresent (it is everywhere, universal). This distribution exhibits fat-tail. Asymmetry tunable by a skew. The pl...

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