Questions tagged [time-series]

A temporal sequence of events measured at discrete points in time.

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34 views

Price volatility short-term (10 seconds) forecast

Dataset: list of all realized trades (BTCUSDT) from a certain cryptoexchange with timestamps (15 days worth of data) Problem: predict the "price volatility" (standard deviation of realized ...
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35 views

The question is related to the regression analysis - stationarity testing

How to interpret different scenarios in ADF test. Scenarios: ADF Test: Type: None, Drift, Trend What exactly each of the types specify and when to use which 'type' during performing stationarity and ...
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26 views

Athens Stock Exchange GARCH-M (Paper replication)

I am trying to replicate one part of a paper which tries to model the Athens Stock Exchange daily returns. I do not have the original dataset, so some differences are expected, but when I fit the ...
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28 views

Combining multiple securities' Net Asset Value time-series into one total NAV series

I have a number of individual securities that each have a Net Asset Value (NAV) time-series. For example: ...
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14 views

Use of ugarchroll vs ugarchforecast: setting parameters

I would like to generate 21 day ahead forecast volatility with ugarchroll. I know it is similar to ugarchforecast with the exception that ugarchroll is a rolling average which considers initially the ...
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1answer
67 views

Factor model for Gold has low adjusted R2

I am trying to build up a factor model for gold. To be able to identify the correct factors, I did a correlation analysis between a few factors vs gold and I integrated this analysis with what I saw ...
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18 views

BEKK Garch for time-varying beta in python

I am currently trying to analyse stocks of the S&P500 for their time-varying beta using BEKK Garch in python(jupyter). Unfortunately, I can't find any good packages and the documentation for bekk ...
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3answers
80 views

Consistent offset/lag in time-series prediction using Neural Network (all code provided)

I'm using a neural network (keras package) to predict Bitcoin prices 48 hours in advance. The issue is that for some reason, my predictions are "correct" but they are lagging behind the true ...
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47 views

How can the different r2 score of an AR(1) model on prices vs. returns be explained

This is maybe a silly question, but I want to understand. As far as I understand an AR(1) model, it is basically a linear regression model with the same but lagged variable, right? However I am ...
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1answer
58 views

Do stationary prices need to be differenced for VaR?

I have a time series of electricity futures prices that I have shown to be stationary via the Augmented Dickey Fuller test (alpha = 0.05). Does that mean that, in calculating their individual values-...
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1answer
55 views

Working with 1 minute bar returns - do I throw out the first return of the day?

I am doing some academic work and using 1 minute bar data. I am wondering if when calculating the return time series, do I need to throw out the first return of the day because it is the return ...
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32 views

how to interpret the results of a GARCH model fit R/python

I have got the following output from a gjrGARCH model, and I need help to interpret it in order to decide whether it is already a good model and proceed with the forecast. ...
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63 views

How to use autocorrelation plot to interpret time series data?

how can we use auto correlation plot or correlogram to interpret time series data? I have 6 different acf plots (a,b,c,d,e,f), from this 6 plots what kind of informations and patterns can I identify? ...
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25 views

What can one do with cross-sectional relationships?

This is somewhat of a broad question, but I think we all would like to find signals that predict something in the future. However, often times, we are just left with cross-sectional relationships (...
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55 views

How to connect Bloomberg's xbbp api to “Bloomberg Anywhere”

Due to COVID's remote work situation I found myself unable to access my physical terminal so I've had to use bloomberg anywhere (bba), the issue I'm having is that when I try to use python's xbbg on ...
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53 views

Modelling volatility for higher frequency data

I'm doing some academic work on volatility forecasting. I've got 1-minute bar data. It is not clear to me what model is best suited for forecasting volatility when higher frequency data is available. ...
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36 views

Non-Linear Time-Dependent Volatility

My data consist of monthly electricity futures contracts. Unlike other commodities, electricity is delivered throughout a month (rather than on a specific date), which means that, as the active month ...
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1answer
43 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 ...
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18 views

Interpretation of Impulse Responses of VAR

Sorry for the dumb question. My model includes oil prices in level. So, for example CPI is log differences. I calculated IRFs of one standard deviation shock of real oil prices. How should I interpret ...
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53 views

Optimal Change Point Detection Problem for a timeseries

W. T. Ziemba, S. Lleo and M. V. Zhitlukhin suggested an Exit Model for selling an asset based on Change Point Detection Theory from the field of Statistical Quality Control https://ideas.repec.org/h/...
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1answer
29 views

In your experience, when trying to predict something that occurs, do you model with a fixed time period?

Let's say you are building a simple model (like the classroom examples) of trying to predict, given past information, if the stock goes up or down in the future. One could, like in classroom examples, ...
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24 views

How to Compute the Return for a Series of Investments with Payouts?

I am looking for a metric to track the performance of investments in a security over time. I know what I have in mind but I am not sure how to map it onto a known metric. I want to answer the ...
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42 views

Cumulative returns are more correlated than non-cumulative

I was just comparing two daily returns series and noted that the correlation between them is a lot higher if they are cumulated (about .95 for cumulative returns, vs .15 for non-cumulative). I feel ...
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42 views

Conditional and unconditional variance, autocovariance and autocorrelation of an ARMA process

Given an ARMA(1,1) process $x_t = a + bx_{t-1} + \varepsilon_t + \theta\varepsilon_{t-1}$, how can we find the conditional variance, i.e. $Var_{t-1}(x_t)$, find the unconditional variance, i.e. $Var(...
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1answer
83 views

Why are my Neural Network predictions “correct”, but offset from true value? Not using any past lagged values

Please bear with me through the whole question - I just want to make it very clear what I've done so far and why I'm so perplexed. I am working with a neural network with the Keras package in R, ...
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15 views

Do volume/dollar bar series always have less observations than time (bar) series?

Traditionally, financial returns are derived from prices that have been sampled based on constant time intervals. These are known as time bars, and a series of returns based on time bars are what we ...
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13 views

How to simulate the cross-section

I am looking to simulate the whole cross-section of daily return series for 20 to 60 days. The purpose is to test some risk measures based no maximum drawdown. Thus, it needs the whole time series. ...
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38 views

Using unsupervised classification to find support and resistance levels

I do not have a specific question, it's more of a general & conceptual one. What would be the optimal approach to finding support and resistance levels? Have you approached this problem ...
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22 views

Why would this price data have lots of spikes above the trend line?

I am completely new to quant finance, so I apologise if this is a ridiculous question. Here is a picture of a snippet of some price data from the security 'NYSEARCA: SPY' (which is SPDR S&P 500 ...
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1answer
61 views

Serial correlation, quadratic variation and variance of returns

On p. 3 of Lorenzo Bergomi's book on Stochastic Volatility Modeling, there is the following assertion: Indeed, to a good approximation, the variance of returns scales linearly with their time scale, ...
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49 views

Options pricing model inversion

He cited about Roll's compound formula for finding the lead-lag effects between stocks and options. I have a similar data for National Stock Exchange's Index, NIFTY but it's daily, not intra-day. I ...
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2answers
85 views

lead lag relationship among futures, options and stock prices

I have the data of past 10 years of NIFTY (the National Stock Exchange of India) stock, futures and options and I want to show the lead-lag relationship (which reacts first, futures, options or stocks)...
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19 views

Statistical test for comparing two different speed of mean reversion parameters for CIR model

I am trying to compare two different values of speed of mean reversion parameter for CIR model. I would like to know if there exists a statistical test for comparing these two parameters. the estimate ...
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27 views

CIR-Model with regime switching Mean-Reversion level - Hamilton Algorithm

I am trying to implement the Algorithm from (1) in R, using the same approach as in (2) and respectively. The main idea is that $x_t$ follows a CIR-process and that the parameters of the latent ...
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1answer
73 views

How to obtain one-step ahead forecast in Python based on GARCH?

I am trying to produce one-step ahead forecast using GARCH in Python using a fixed windows method. I ultimately want to put the code below in a for loop, but this code snippet does not perform as I ...
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1answer
94 views

Turning a covariance sum into an integral

I am reading Lorenzo's Bergomi's book Stochastic Volatility Modeling, and I have come to this passage. I just would like to understand the derivation between the first and the second equality. I ...
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1answer
94 views

What are some good models for stock price predictions?

For the fitting and forecasting of time-series data on stock price, the most frequent model I have heard of is ARIMA. ARIMA is actually conducting a regression of stock prices and residuals of stock ...
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23 views

PanelOLS or simple OLS?

It is probably a silly question but please assume the following. I gather hedge funds returns and I want to do a regression against few dependent variables (FF factors, etc.). In essence, I get a ...
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1answer
54 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 ...
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1answer
52 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 ...
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28 views

How to set up the dummy variable for OLS event study regression

I've been going back and forth with how I should work to find an event effect. would be so grateful for some clarification. I have daily time series of exchange rates for different countries ( 1 for ...
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1answer
60 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 ...
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2answers
133 views

Converting time bars to tick bars or volume bars in python

Recently I've started reading Advances in Financial Machine Learning by Marcos Lopez de Prado. In the second chapter the author defines some essential financial data structures, like tick bars, volume ...
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What is the correct order of operations when cleaning and structuring financial time series?

I'm studying Lopez' Advances in Financial Machine Learning where he talks about how to sample and structure financial data, as well as how to apply machine learning models to the data. I am also ...
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25 views

How to get daily S&P500 dividend and CPI data?

For a project where I'm modelling asset prices using heterogeneous dividend expectatations, I want to investigate the effect of the COVID-19 virus. This means I'm looking at daily stock price data ...
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25 views

Forecasting accuracy in one month and hedging

I am working on predicting the daily data of a financial time series $[Y(t+1),...Y(t+j)]$ =$f(X_1(t),...X_1(t-i),.....,X_n(t),...X_n(t-i))$ where $Y$ is a commodity price $X_i$ are predictor variables ...
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12 views

Engle-Granger Regression for returns or prices?

Im trying to find a cointegration relationship between a spot and futures series as I'd like to compute the optimal hedge ratios. As I find the spot and futures at the price level to be nonstationary, ...
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1answer
62 views

Align volume bars for multivariate analysis

Looking at the book "Advances in financial machine learning" the author proposes a way to sample high frequency financial data in several fashions which are not only the standard time bars. I was ...
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217 views

Is there a way to tell if a time series price data is reversed?

Assuming you are given an array of values representing a stock's historical price, without timestamps, is there a way to tell if this array of prices has been reversed?

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