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The bid-ask spread before transactions

They are a lot of ways to compute an "estimated bid-ask spread". The most straightforward one is to sample the bid-ask on a regular time grid (for instance every second), but that for you ...
XY0's user avatar
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1 vote
1 answer
77 views

Sampling dollar bars for ML model of multiple tickers

I have a Neural Network model that provides predictions for the future returns of a portfolio comprising stocks and cryptocurrencies. The original model operates on standard time bars and generates ...
apt45's user avatar
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Clustering of Maximum Drawdown Values in Monte Carlo Simulations (Jaekle & Tomasini example)

Hope this question isn't too naive. I've been trying to replicate the Monte Carlo method using sampling without replacement as described in the Jaekle & Tomasini book (Trading Systems: A New ...
djhanson's user avatar
1 vote
1 answer
191 views

Single outsized daily return value creates substantive discrepancy between annualized variance calculated from daily vs monthly returns

I am new here, and to the field. I hope my clunkiness in expressing myself can be forgiven. My situation is as follows: I have around three years of daily return data for some financial asset. Out of ...
Tim Molendijk's user avatar
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252 views

What are the advantages and disadvantages of converting standard deviation of higher-frequency returns to a lower sampling frequency?

I have a minute-by-minute price series of a stock. I would like to calculate the daily volatility or standard deviation of the stock's returns. One way to do so is to get the end-of-day prices (i.e. ...
finstats's user avatar
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1 answer
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Estimate market positioning from flow data

I have a set of time series data from a bank that is transaction data from all its clients on a particular currency. From that data, I attempt to estimate the current "position" of all ...
Felton Wang's user avatar
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1 answer
289 views

Sampling dollar bars for a machine learning model

I'm trying to understand the rationale behind using information drive bars over traditional time bars and specifically when it comes to practically feeding those in to a machine learning model to run ...
PlatinumMaths's user avatar
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1 answer
173 views

Markov Chain Monte Carlo Sampling

I have just been having a read about mcmc for path dependent options. I am still trying to understand the logic of it, I don’t have the technical background to understand all the formulae etc. behind ...
NutellaMonster's user avatar
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1 answer
274 views

Distribution of Geometric Brownian Motion drawdowns from realizations of multivariate Normal and Laplace distributions

I am trying to simulate the distribution of Geometric Brownian Motion drawdowns from samples of multivariate Normal and Laplace distributions under the same covariance structure. Drawdowns are defined ...
Bryan Franco's user avatar
2 votes
0 answers
399 views

Implied Gamma VS Implied Volatility

Reading this paper, I'm struggling to understand what the author is saying with paragraphs below (see pages 39-42): We define Implied Gamma ($\Gamma_{\operatorname{implied}}$) as the value of the ...
user853717's user avatar
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1 answer
367 views

Tactical Investment Algorithms

I am reading paper "Tactical Investment Algorithms" (link) (NOTE: you can download the paper without registration, just press "Download" and then "Download without ...
ABK's user avatar
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1 answer
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How to up-sample monthly returns into daily returns?

I know how to down-sample daily returns (large-sample data) to monthly returns (small-sample data) by using rolling windows, which feels like estimating a sub-sample from the population (something ...
develarist's user avatar
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1 vote
1 answer
229 views

does oversampling affect the correlation?

I have a dataset of monthly data. One column is my target variable and all the other are my feature. I have computed correlation between my target and all the other feature and then I made linear ...
Luigi87's user avatar
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204 views

Literature on realized volatility and sampling frequency?

I was looking for some papers that explicitly show how realized volatility changes as we change the sampling frequency. For example, comparing the annualized volatility estimated from daily data is ...
AK88's user avatar
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1 vote
1 answer
517 views

Volume bars, dollar bars from low-frequency data?

Financial models by default use time bars of prices/returns for input data. I use time bars to refer to both intraday (high frequency) and interday (low frequency) data since the sampling occurs at ...
develarist's user avatar
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0 votes
1 answer
123 views

Portfolio optimization with multivariate returns of different length

The mean variance model of Markowitz that uses multivariate covariance matrix requires the length of each of the N assets return time series under consideration to be of equal length. Are there any ...
develarist's user avatar
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1 answer
196 views

Sampling and cross-validating with tick, volume and dollar bars

Financial data is usually structured with time bars. Other sampling techniques include: tick bars volume bars dollar bars. These are so-called sampling techniques to better identify signals and ...
develarist's user avatar
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0 votes
1 answer
211 views

Sampling from SDE

In the case of the classic Geometric Brownian motion $$dS_t = \mu S_t dt + \sigma S_tdW_t$$ we solve it as $$ S_t = S_0 \exp\left[ \left(\mu - \frac{\sigma^2}{2}\right)t + \sigma dW_t\right] $$ and ...
qarabala's user avatar
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2 votes
1 answer
106 views

Sampling from an empirical distribution

I want to sample from the empirical distribution of returns. To do so, I do not want to make the preliminary assumption of which distribution the returns follow, rather I would like to sample from the ...
Vitomir's user avatar
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0 answers
25 views

Sample conditional multivariate random variable?

There's multivariate random variable, future prices of assets, 5 years from now: $$X = [Gold, Silver, SP500]$$ There's historical prices for $X$ available for last 50 years. It's possible to fit ...
Alex Craft's user avatar
2 votes
0 answers
152 views

Stratified sampling in asian options

I am using the procedure of stratified sampling for variance reduction. In the Glasserman book the algorithm for stratified the terminal value of the Brownian motion is given for european options. For ...
Dhruv Mahajan's user avatar
4 votes
0 answers
963 views

Information Driven Bars (Advances in Financial Machine Learning)

My team and I are busy coding up a python implementation of the information driven bars (imbalance and run bars) mentioned in Chapter 2 of the text book Advances in Financial Machine Learning. There ...
Jacques Joubert's user avatar
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2 answers
86 views

Compound 3-year returns to obtain 10-year returns: How to do?

I have 3-year returns at a monthly frequency, snippet below. How to compound the 3-year returns to obtain 10-year returns (since the cumulative product of 3 3-year return would be the 9-year return). ...
k1000x's user avatar
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1 vote
1 answer
222 views

Anti-thetic sampling and second moment matching

Background: This is in reference to ch 7 problem 10 of Mark Joshi's concepts of mathematical finance. Question: A normal random generator produces the following draws: $$0.68, -0.31, -0.49, -0....
Wolfy's user avatar
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