The VIX has a daily autocorrelation of −.04, a weekly autocorrelation of −.21, and a monthly autocorrelation of −.12.
Euan Sinclair, Volatility Trading
The VIX has a daily autocorrelation of −.04, a weekly autocorrelation of −.21, and a monthly autocorrelation of −.12.
Euan Sinclair, Volatility Trading
Tags:autocorrelation, econometrics, facts, finance, math, mathematics, maths, stat arb, statistics, VIX, vol arb, volatility
Posted in Uncategorized | Leave a Comment »
GARCH stands for Generalized Autoregressive Conditional Heteroskedasticity. To translate, skedasticity refers to the volatility or wiggle of a time series. Heteroskedastic means that the wiggle itself tends to wiggle. Conditional means the wiggle of the wiggle depends on something else. Autoregressive means that the wiggle of the wiggle depends on its own past wiggle. Generalized means that the wiggle of the wiggle can depend on its own past wiggle in all kinds of wiggledy ways.
Tags:algo trading, alo trading, autoregressive, Black-Scholes, econometrics, GARCH, hedge funds, heroes, heteroskedasticity, HFT, math, mathematics, maths, options pricing, quant trading, stat arb, time-series, vol arb, what does GARCH stand for?
Posted in Uncategorized | Leave a Comment »
μ dt + σ dWt, my #$$
The simplest model of a stock price movement is that the log of the price moves in a direction, plus some noisy drift (like adding a Gaussian W𝓽 at every timestep).
Agustin Silvani gives a counterexample: Federal Open Market Committee meetings precede a volatile market episode, meaning long-term changes in μ and short-term spikes in σ come after an FOMC announcement.
“Stop hunting” by dealers and other smart-money players can sometimes shake up the price pattern ONLY during a super-short period. This occurs most in the least regulated market, FX, because dealers will pull the rug out from under their retail clients’ feet. The dealers can see their clients’ stops and will just blatantly cheat the retail clients (according to Silvani), because they only need to retain a big client’s business. (Hence it’s more profitable to cheat the small fry — there will always be more.)
Not only does this violate the Black-Scholes model (a freak σ^7 comes in and then disappears), it also violates the Strongly Efficient Market Hypothesis, which says that market price—at any instant—reflects all available information and are the best measure of the “true” value of an asset. You would obviously get a more accurate valuation from taking a one-hour average than from relying on any instantaneous price, in the above graph.
According to Silvani’s story, the dealers are very efficient at bilking worse-informed or less-experienced participants, but don’t use this as a Prediction Market!
Tags:alo trading, Black-Scholes, education, efficient markets hypothesis, EMH, fair market value, Federal Open Market Committee, FMV, FOMC, hedge funds, interest rates, mark to market, math, mathematics, maths, options pricing, quant trading, stat arb, vol arb
Posted in Uncategorized | Leave a Comment »
This really puts my mind at ease as far as whether it’s an economically doomed strategy to pursue quantitative finance:
The Efficient Markets Hypothesis is neither necessary nor sufficient for the Random Walk Hypothesis.
Apparently the Cowles Commission was the first to discover a Gaussian pattern in stock market returns—and its members thought it was proof that financial markets are irrational. Later on, Paul Samuelson established the connection between EMH, greed, arbitrageurs, and random walks of the price of a publicly traded stock. (Benoit Mandelbrot figures into this story too.)
Anyway, Eugene Fama eventually coined the phrase “Prices fully reflect all available information” which is the story I got in Economics 101. But like most bite-sized wisdom, apparently it’s more complicated than that. (Phew, said the hedge fund investor.)
Tags:algo trading, Black-Scholes, books, Brownian motion, education, financial economics, hedge funds, HFT, long reads, martingale, math, options pricing, quant, quant trading, quantitative finance, random walk, random walk hypothesis, stat arb, stochastics, vol arb
Posted in Uncategorized | Leave a Comment »
No-arbitrage conditions are so often assumed in economics papers that they’ve come to seem magical. As I read more books by arbitrageurs, the obvious has become apparent: real people have the job of making financial markets equilibrate.
Given that companies are trying to raise funds for their projects by offering shares of the profits on public exchanges — which is the state of things* — it’s not at all obvious that various mathematical balancing conditions should come about. It takes hedge funds and stat arbs spending their days looking for profit opportunities to smooth these markets out.
For example, take high-frequency traders. They learn the nitty-gritty trading rules (aka market microstructure) and look for tiny opportunities to hold a security for just a little longer (intraday) and then sell it for more than transaction cost a wee bit later. Effectively they act as a short-term warehouse holding the security in between the person who wanted to dump it and the person looking to pick it up.
Knightridge Capital and Jesse Livingston both advised traders not to fight the market, but to go where it wants to go.
As with most ways of making money, to extract it over the long term you have to make the world more like people want it. (So I believe.)
* but not necessarily the way things would have to be done. For example I could just call people up and ask them if they wanted a share of my company; or I could auction shares on eBay; or I could post a message on Craigslist to get local people to meet up and talk together about various people buying various proportions of the company.
Tags:algo trading, algorithmic trading, alo trading, arbitrageurs, Black-Scholes, books, dark pools, direct market access, DMA, finance, hedge funds, HFT, high-frequency trading, long reads, markets, options pricing, quant trading, stat arb, tech, vol arb
Posted in Uncategorized | Leave a Comment »
Should I buy this?
Tags:algo trading, alo trading, Black-Scholes, books, education, hedge funds, heroes, HFT, Lawrence C Evans, long reads, math, mathematics, maths, options pricing, PDE's, quant trading, science, Sobolev spaces, stat arb, vol arb
Posted in Uncategorized | Leave a Comment »
“The advantage scientists bring into the [investing] game is less their mathematical and technical ability than their ability to think scientifically.”
—James Simons, founder of Renaissance Technologies
[Q]uants are forced to think deeply about many aspects of their strategy that are taken for granted by nonquant investors. Why does this happen? Computers are obviously powerful tools, but without absolutely precise instruction, they can achieve nothing…. You can’t tell a computer to “find cheap stocks”. You have to specify what “find” means, what “cheap” means, and what “stocks” are.
—Rishi K Narang
Tags:algo trading, algorithmic trading, alo trading, Black-Scholes, books, hedge funds, HFT, long reads, options pricing, quant, quant trading, stat arb, tech, vol arb
Posted in Uncategorized | Leave a Comment »
“This has become something of a ‘me-too’ trade lately,” said Mark Spitznagel, a former Taleb trading partner at Empirica.The upside of everyone piling onto these catastrophe protection funds is that — guess what — all those people hedging against a catastrophe will reduce its likelihood of happening.
Tags:algo trading, alo trading, Black-Scholes, hedge funds, HFT, options pricing, quant trading, stat arb, vol arb
Posted in Uncategorized | Leave a Comment »
Which of these pictures come from a random normal distribution and which come from a mixed distribution?
plot(rnorm,-3,3);
mix <- function(x) {
rnorm(x)+rnorm(x-3)
}
plot(mix(x), -3,3);
Tags:aleatoric, algo trading, alo trading, Black-Scholes, data, education, Gaussian, hedge funds, HFT, math, mathematics, maths, non-Gaussian, options pricing, probability, puzzle, quant trading, r, random, randomness, stat arb, stochastic, vol arb
Posted in Uncategorized | Leave a Comment »
I’m deeply skeptical about this. Why is Ernie Chan telling me how I can make millions? Is he just trying to increase the pool of suckers for him and his like to take money from? If so, how does he know other firms wouldn’t profit from the suckers instead of his?
Tags:algo trading, alo trading, Black-Scholes, books, hedge funds, HFT, long reads, math, mathematics, maths, options pricing, quant trading, stat arb, tech, vol arb
Posted in Uncategorized | Leave a Comment »