For my thesis, we expanded on Brownian Motion by adding intra-day (up to 5-minute) returns data as well as time-dependent stochastic volatility (to simulate news events/shocks) to Brownian motion, effectively allowing skewness and kurtosis into the picture. This allowed us to describe real-world IBM share price movements much more accurately than using only normally distributed innovation processes and daily share price returns.
https://youtu.be/-qgCtNhaRt8
I'm learning about this in class and was wondering how a person would distinguish between something like thermal vibrations and Brownian motion.
So here's the situation: two balloons, same amount of the same gas, in perfect-gas-conditions. One balloon is under significant more atmospheric pressure than the other, but with the same room temperature. If we tracked a given particle inside each of the balloons, would it cover significantly less space in one case than in the other? Or would pressure difference compensate difference in volume (available space)?
So i am trying to make a physics project and i've been working on something for the past week.The thing is that i have to make a program in pascal,c++ or even html in which there are particles of different sizes and speeds that collide with eachother.I am really at hope's end and i don't know how i can code something along those lines.
I would appreciate your help.I am truly desperate at this point.
Using volatility estimates as features
> TL;DR: Code
Hi guys, I published a notebook implementing multiple different volatility estimators that can be used as features for your models.
What is so important about volatility?
Volatility is basically the "speed" of the asset.
However, predicting volatility is basically nearly as hard as predicting the price itself. Simply due to the fact that the future volatility IS the price just without the sign.
Fortunately, in contrast to predicting future prices, volatility has some interesting properties which we can use for estimating future volatility. This is an established field and there are many different types of volatility models that are described in what is extremely broad literature.
In the notebook above I implemented multiple volatility metrics that can be used as features for your models.
Realized Volatility: Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson Volatility
Parkinson volatility is a volatility measure that uses the stockโs high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to cl
... keep reading on reddit โกI have had some circumstances come into play which have affected my ability to study, and in turn I've got pretty poor marks (A weighted average mark of around 70), I am studying a Bachelors in Computer Science and I also have a statistics major too.
I reckon I have some good foundation in terms of Machine Learning, general programming, general maths (Calculus and Linear Algebra, Statistics obviously).
I have came across this thread:
I am sort of new to the financial field BUT, I have been trying to navigate through it all. I am actually extremely interested in this field as I am someone who likes to creatively solve problems and actually get something working like a model etc.
First, I started of looking at technical analysis and implementing simple strategies such as moving average cross overs, I made some of my own strategies too and I have made some indicators myself too. However, I mostly think it is all bullshit because optimising and tuning instruments so they perform well on back tests (even if its time series cross validation), does not seem right to me and it does not tell me anything about the data, (For example are certain moving average window parameters better than others because of how volatility changes, is there a relationship between the hyperparameter used and the data) etc?
Secondly, I also started of looking at price direction predictions using various different types of models. I farmed like more than 100 indicators and applied linear regression on shifted prices to see if there were any predictive value in the actual value of the indicators (literally none lol, when comparing it to price changes, a scatter plot literally shows a circle). I used filtration strategies, used methods such as DFT with FFT to try and extract underlying sine curves which might give me an indication on the cyclical nature of data etc. Manually fitting Sine curves to limited segments of data then made predictions etc.
Thirdly, now I am in the process of using Neural Networks (Feed Forward Networks, CNN, LSTM) to create indicators based on some metric that I probably won't share. All results have been meaningless and useless, the signals produced have been trashed. I have been using Stock and Crypto Data for this too.
So now, I feel like I
... keep reading on reddit โกI don't want to step on anybody's toes here, but the amount of non-dad jokes here in this subreddit really annoys me. First of all, dad jokes CAN be NSFW, it clearly says so in the sub rules. Secondly, it doesn't automatically make it a dad joke if it's from a conversation between you and your child. Most importantly, the jokes that your CHILDREN tell YOU are not dad jokes. The point of a dad joke is that it's so cheesy only a dad who's trying to be funny would make such a joke. That's it. They are stupid plays on words, lame puns and so on. There has to be a clever pun or wordplay for it to be considered a dad joke.
Again, to all the fellow dads, I apologise if I'm sounding too harsh. But I just needed to get it off my chest.
Do your worst!
I'm surprised it hasn't decade.
For context I'm a Refuse Driver (Garbage man) & today I was on food waste. After I'd tipped I was checking the wagon for any defects when I spotted a lone pea balanced on the lifts.
I said "hey look, an escaPEA"
No one near me but it didn't half make me laugh for a good hour or so!
Edit: I can't believe how much this has blown up. Thank you everyone I've had a blast reading through the replies ๐
It really does, I swear!
Because she wanted to see the task manager.
Theyโre on standbi
For my thesis, we expanded on Brownian Motion by adding intra-day (up to 5-minute) returns data as well as time-dependent stochastic volatility (to simulate news events/shocks) to Brownian motion, effectively allowing skewness and kurtosis into the picture. This allowed us to describe real-world IBM share price movements much more accurately than using only normally distributed innovation processes and daily share price returns.
https://youtu.be/-qgCtNhaRt8
Using volatility estimates as features
> TL;DR: Code
Hi guys, I published a notebook implementing multiple different volatility estimators that can be used as features for your models.
What is so important about volatility?
Volatility is basically the "speed" of the asset.
However, predicting volatility is basically nearly as hard as predicting the price itself. Simply due to the fact that the future volatility IS the price just without the sign.
Fortunately, in contrast to predicting future prices, volatility has some interesting properties which we can use for estimating future volatility. This is an established field and there are many different types of volatility models that are described in what is extremely broad literature.
In the notebook above I implemented multiple volatility metrics that can be used as features for your models.
Realized Volatility: Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson Volatility
Parkinson volatility is a volatility measure that uses the stockโs high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to cl
... keep reading on reddit โก
