Statistics: Posted by Amin — 14 minutes ago

]]>

This is not the end of Space-Time Money, but the very beggining of it

Existing technology could revolutionize our view on money

Statistics: Posted by Collector — 29 minutes ago

]]>

]]>

I feel the above is a case of

Statistics: Posted by Cuchulainn — 55 minutes ago

]]>

Honest question to Alan, Paul, and any other Trump fans out there. Is there anything he could say or do that would change your opinion of him? Obviously, just killing off a hundred thousand Americans doesn’t move the needle. They were probably losers anyway.

Well, not really an honest question with the 'killing a hundred thousand" bs. But, to answer anyway -- sure, lots of actions could lose my support. Let's say he began the process to pack the Supreme Court by increasing it to 13. I'd vote against him for that. Or, let's say he wanted to get rid of the electoral college. I'd vote against him for that. Or, if he began massive deportations of non-criminal 'undocumented' (well in excess of what has occurred under previous admins). Or, if he engineered the elimination of the 'pre-existing condition' protection of Obamacare without an equivalent replacement. The last two were real risks prior to him assuming office. Seem unlikely now.

Here's one for you. Say by Jan 2021 we have a covid vaccine and Biden is president. He proposes making it a federal felony if you don't take the vaccine by the end of 2021. Would you support that?

Statistics: Posted by Alan — Today, 2:11 am

]]>

]]>

There is more to life than ODEs but my question is why these reports do not discuss these problems as dynamical systems ("ODEs++") so that other questions can be answered beyond numerical ODEs. I wonder why no mention is made of the Lyapunov, Poincare-Bendixson theory etc. What happens to the ODEs when [$]t \rightarrow \infty[$] (sounds like a logical question). A Sapir-Whorfe problem ("give hammer then all is a nail")

They do. Many epidemiologists use network models of contagion / directed percolations. The critical behaviour is at the threshold between the active and the absorbing regime (cf R0).Funny, in the other forum I saw some Jan Dash posting papers on Regge field theory (from high energy physics, any CERN people here to give more insight?) as "generalisation" of a Brownian motion. RFT, Brownian motions and the network model of epidemic/directed percolation mentioned above are in the same universality class - every model with an underlying stochastic Markov process qualifies to this class (every reaction-diffusion system: contagion, reacting chemical substances, 2nd order phase transitions like ferromagnetic - note percolation threshold vs critical, and so on. Scientists are copycats...

Anyway, let's assume a naive model the contagion as an isotropic percolation, i.e. Bethe lattice:

A classic result is that large clusters and long-range connectivity arise when the probability of "jumping" to the next node is Pc = 1/(z-1) (*), where z is the coordination number. In our model the latter corresponds to the number of regular contacts made by an infectious (it's a static model - contacts don't change).

Assume that for Covid the probability of "jumping", i.e. infecting the contact, is 15% per each meeting. This probability is due to the Poisson process with intensity given by the equation 0.15 = 1-exp(-lambda*1), which yields lambda = -ln(0.85).

Say the disease period is 21 days and you meet all your contacts once a day. Then the probability that you infect each contact accumulates to P(21 days) = 1-exp(ln(0.85)*21) = 1 - 0.85^21. For this probability to be the percolation/epidemic outbreak threshold, it takes z susceptible contacts given by Eq (*), which is 2.0340679919043606448571830240222 persons.

Don't tell Boris!

Statistics: Posted by katastrofa — Yesterday, 10:29 pm

]]>

]]>

Statistics: Posted by Cuchulainn — Yesterday, 9:10 pm

]]>

www.youtube.com/watch?v=oNiuDuEVllc

kats, do you recognise this town?

www.youtube.com/watch?v=ItVEhL-T7qQ

Statistics: Posted by Cuchulainn — Yesterday, 8:54 pm

]]>

Thank you for your comments Cuch.

I long to have a look at this piece of research.

Sincerely,

tag

tag,I long to have a look at this piece of research.

Sincerely,

tag

I reckon 2-3 weeks and then it will hopefully be public domain

Statistics: Posted by Cuchulainn — Yesterday, 8:45 pm

]]>

where W is the standard Brownian motion, mu is the drift, sigma the volatility, alpha the strength of mean reversion (these parameters are estimated from data).

This commodity is the underlying asset of the Forward contract with price at delivery F(S,T), where S is the spot price at time 0 and T is the expiry time.

Such a contract is equivalent to having an option with , where K=F(S,T) is the strike price.

Letting be the time to expiry, the formula for the price of the forward contract is

and the value of the equivalent option is

Knowing this information, how to run (I'm using matlab but other languages are fine too) a Monte Carlo simulation to price this particular option? Usually, these are the steps to follow

- generate a large number of random price paths for the underlying asset
- for each path compute the payoff of the option
- compute the average of all payoffs
- discount the average to today

I wrote the (matlab) code (download below) which computes what I think (can you confirm?) is the exact solution for option pricing (V_exact in the attached code) and then computes the approximating solution by means of Monte Carlo simulation (V_Monte_Carlo in the code). The MC simulation uses the first spot price (real data) and the estimated parameters to 'randomly' compute the next spot prices. To compute the spot prices, instead of using the equation for dS that I wrote above it's easier to use this one which is obtained from dS by letting X=log S:

where and

These are the steps for the Monte Carlo simulation:

- The first price F of the forward contract - and so of the option V too - is set to 0 since there is no cost to enter a forward contract. Compute the first value of X by taking the logarithm of the first spot price
- compute the next value of X using the formula (2)
- compute the next value of the spot price by taking the exponential of X
- compute the next price F of the forward contract using formula (1)
- compute the next price V of the option using
- compute the average V_Monte_Carlo of the option prices
- repeat steps 2-6 until all values are computed

p.s. if you prefer I can write the code here

- testMC.rar

Statistics: Posted by Rabelais — Yesterday, 8:17 pm

]]>

I was looking at FAQQF2 on some online book store, with the plan of maybe buying a copy, and realized it was released in 2009 (yeah... everything moves very slowly here in France). Is there a chance the author publishes an updated version some day, maybe reflecting the changes in the finance industry, jobs, practices, etc... during the past decade, or something?

Thank you,

tag.

Statistics: Posted by tagoma — Yesterday, 8:12 pm

]]>

Sincerely,

tag

Statistics: Posted by tagoma — Yesterday, 7:42 pm

]]>

There's not much sand on that beach.If you're number 7 billion you get lunch with Joe Biden

Something unusual is happening in La Perla, a poor barrio clinging to a steep hillside between Old San Juan and the sea where the video for the pop hitDespacitowas filmed.

"The gringos are coming!"

Outsiders were afraid to venture in before, but since Luis Fonsi and Daddy Yankee's mega hit, tourists from all over the world are descending on the narrow streets that wind among La Perla's brightly coloured houses.

"Despacito?" they inquire.

And the barrio's residents obligingly point out the locations where the video was filmed: the rocks facing the sea where Fonsi sings the refrain, the sea wall where ex-Miss Universe Zuleyka Rivera strolls, the little plaza where men play dominoes — the tables and chairs just as they were in the video

"ex-Miss Universe"! I had no idea.

Just thought she was a random gorgeous Latina ...

Statistics: Posted by Alan — Yesterday, 5:48 pm

]]>