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Questions tagged [time-series]

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

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Temporal dependencies in time-series

To my knowledge, the algorithms that require stationary input can't capture temporal dependencies. This is inherent due to the fact that the input features must be stationary, thus things like trends, ...
Dylan McClish's user avatar
1 vote
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69 views

Financial Time-Series: Stochastic or Dynamic?

I have learned how some methods of constructing predictive models of financial time-series involves assumptions of stochasticity. For example, reinforcement learning utilizes the Markov Decision ...
Dylan McClish's user avatar
2 votes
1 answer
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Weak Stationarity for Neural Network Input?

I am taking a course that detailed that input data into neural networks should be at least weakly predictive and weakly stationary (stable mean). Does this principle apply to other ML models like tree-...
Dylan McClish's user avatar
2 votes
1 answer
135 views

Cross sectional momentum vs time series momentum

What are the advantages/disadvantages of creating quantitative strategies using cross sectional momentum vs time series momentum? From my perspective, time series momentum is a better indicator of ...
Dylan McClish's user avatar
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1 answer
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Mean-reversion strategy with overnight gaps

When using stocks as time series data, it is common to encounter large overnight gaps, sometimes because of earnings, other times because of press releases. So, how to correctly account for this ...
quanted's user avatar
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Residual Function

In a time series with OLS regression curve Y-hat (rolling linear regression), and with n=20, what can I say about this transformation? This formula is similar to a differential dY/dt minus an integral ...
NEO ULTRA's user avatar
2 votes
1 answer
61 views

Reducing possible models count for calibration in ARFIMA-GARCH models

I have the question connected with ARFIMA-GARCH models. I have a time series for which I want to calibrate best model (p,q)-(P, Q) (via BIC) with $ p,q <= 4, P,Q <=2$. GARCH part can be "...
Dmitriy's user avatar
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-1 votes
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How do I understand and calculate daily log returns? [closed]

I'm relatively new to the Quantitative community. I was trying to work with a dataset where I want to calculate daily log returns. My dataset consists of multiple timestamps for each business day and ...
Shubhankar Agrawal's user avatar
1 vote
1 answer
86 views

Is it possible to discretize OU with a more general AR(p) / ARMA (p,q) models?

The discrete analogue of an OU process is a simple AR(1) model. More general AR(p) or ARMA(p,q) models can also be regarded as discrete analogues of an OU process? If so, which coefficients describe ...
Sane's user avatar
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YUIMA: Drift and diffusion parameters must be different?

I am currently working with the Yuima package and trying the estimate the parameters of a CARMA(p,q) model to real data. Using the eacf function of the TSA package a ARMA (2,1) process is recommended ...
Valentin's user avatar
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Robust or Stochastic Optimization Approach for Maximizing Profit with Limited Price Information

I am tackling a linear maximization problem where I need to select the optimal product among several options over a series of weeks, given certain constraints, in order to maximize future profit. The ...
anasse's user avatar
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Analyzing portfolio returns using Fama-French Factors

Here is my problem - I have monthly returns from few portfolios. I also have monthly return from benchmark portfolio. I downloaded F-F 5 factor daily data. Also downloaded Momentum data. Converted ...
deb's user avatar
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LSTM multivariate Time Series Simulation

I am currently working on a project involving the simulation of multivariate time series (implied-volatilities). To facilitate this process, I am seeking a GitHub repository that provides an ...
DivertingPie's user avatar
1 vote
1 answer
165 views

Why does AR(1) model with a small coefficient exhibit faster mean-reversion than one with a greater coefficient (when |$\beta$|<1)? [closed]

Suppose we have two mean-reverting AR(1) models, given by $$X_{t}=\beta X_{t-1}+\epsilon_t,$$ where $|\beta|<1$. How fast series reverts to its mean is determined by the coefficient $\beta$. As far ...
Sane's user avatar
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How to calculate the spot variance from the TSRV (Two-Scale Realized variance)

If the TSRV is given by: $$TSRV = \frac{1}{K} \sum_{i=K}^{n} (S_i - S_{i-K})^2 - \frac{\bar{n}}{n}\sum_{i=1}^n (S_i - S_{i-1})^2 $$ where $\bar{n} = \frac{n - K + 1}{K}$, with $n$ is the number of ...
Xerium's user avatar
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1 answer
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Average correlations of stock returns

Say I had a pool of companies (specifically the Latin American Countries). The task was to work out the 'Correlation coefficient between the returns of any 2 companies selected from "different ...
Bossman Joestar's user avatar
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Does cointegration test of exogenous variable with Y variable make sense when doing ARIMAX/SARIMAX?

The cointegration test between two time series variable is generally relevant from my understanding when you are performing a regression model. In terms of ARIMA model the approach is straightforward ...
Sayooj Balakrishnan's user avatar
1 vote
1 answer
72 views

In copula modeling for time series data, why do we need to fit ARIMA/GARCH and then work on standardized residulas.?

I have read that for standard copula modeling, you can get empirical cdf of data and use it for copulas. But for time series data, we must first fit ARIMA/GARCH, get standardized residuals, and only ...
nadeem's user avatar
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1 vote
1 answer
74 views

Can the white noise in multivariate GARCH have different distributions?

I have two datasets of log returns, one is clearly normal while the other is t-distributed. I want to fit these with a mutlivariate GARCH model. A multivariate GARCH model is defined as $$\mathbf{r}_t=...
Isaac E's user avatar
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How should I create a Risk measurement Variable?

I have clients who take loans (Advances) weekly. The way that they repay the advance is after 3 weeks when their goods are sold, using the sales proceeds of the goods. But if the goods don't sell for ...
user70803's user avatar
1 vote
1 answer
98 views

the pre-averaging function in Jacod et al

In the paper of jacod et al the authors used the pre-averaging function to deal with microstructure noise. They suggest the easiest function which is $$\bar{Z_i} = \frac{1}{kn} \left( \sum_{j=kn/2}^{...
XY0's user avatar
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Data separation for Time Series Models? (both Statistical and ML)

I had a big question about data slicing for Time-Series model fitting. In university, since I wasn't a stats major, so I didn't do much model fitting for data, other than my ML class, and have ZERO ...
matthewzz's user avatar
4 votes
2 answers
766 views

Highest resolution of stock data?

Out of curiosity, I'm wondering what the highest resolution of stock data there is out there. Is there stock trading data for every nanosecond, picosecond, or even lower? And how is this limit ...
BeefJerky's user avatar
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How does historical data from bloomberg interact with timezones?

I'm running analysis on multiple countries bonds over a long stretch of time. I was asked about what determines the date of data in Bloomberg, ex: December 31st in NY will be January 1st in Japan and ...
Ahhhhhhhhh's user avatar
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Historic Time Series Data for entire Tech Sector on Aikon [duplicate]

I am attempting to gather data for my dissertation on daily closing prices for all 1700ish companies within the US tech sector. Now, doing this one by one through something like datastream is going to ...
Sandy P's user avatar
1 vote
1 answer
98 views

Neural network time series prediction tool [closed]

What are some of the state of the art time series prediction tool with neural network?
Hans's user avatar
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3 votes
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135 views

Understanding the Intersection of "Advances in Financial Machine Learning" and "Asset Pricing in Stock Market Prediction"

I have been reading "Advances in Financial Machine Learning" by Marcos Lopez de Prado and "Machine Learning in Asset Pricing" by Stefan Nagel, and I noticed that there seems to be ...
RRR's user avatar
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468 views

Value at Risk for Portfolio of Futures

I'm working in a very small commodity trading company. They are not used to excel at all, so i built their trading sheet to follow open positions & past positions. Now they asked me to calculate ...
Koba's user avatar
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Cumulative returns when shorting with regards to variance drag

What is the convention when calculating/analyzing daily returns for a strategy when shorting is involved? I found the following answer regarding variance drag useful in understanding why there is a ...
mimi's user avatar
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Heston model using YUIMA package

I am trying to estimate a Heston model using the Yuima package, but i am in trouble. This is my script: ...
Luiz Araújo's user avatar
1 vote
1 answer
88 views

Dummy time series to be considered

When estimating various risk measure like VaR a good amount of times series data is required. Somethings it happens that sufficient data may not available of ...
Bogaso's user avatar
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80 views

Backtesting: choosing the "sample split" between in-sample and out-of-sample data

Aims: Given approximately 11 years of historical time-series data, to determine how much of this data should be reserved for in-sample and ...
p.luck's user avatar
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1 vote
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48 views

What Do These ACF PACF Tell you? [closed]

I came across some very strange ACF, PACF chart during my research. how should these charts be interpreted? From top to bottom are chart of the original time series, the acf chart and the pacf chart
sander's user avatar
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Empirical Evidence for Support/Resistance Levels in Martingale Price Processes and Its Impact on Option Volatility Surfaces

In financial mathematics, the martingale property often serves as an essential foundation for the stochastic processes that underlie securities pricing models. According to martingale theory, the most ...
GotTheTrumpCard's user avatar
2 votes
1 answer
168 views

Are ARMA-GARCH-type models suitable for monthly data?

I understand that ARMA-GARCH models and their variations are usually applied to daily time series. While I know that such models can be also estimated on monthly data, I have seen few applications in ...
Barbab's user avatar
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29 views

Notation in Advances in Finance ML for time interval

I was reading Prof. Lopez book Advances in Finance ML and he uses a notation I could not understand. In 4.2 (Overlapping Outcomes) he says: In Chapter 3 we assigned a label $y_i$ to an observed ...
Code Pope's user avatar
  • 101
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Filtering a time series of returns by deleting some points

I have a time series of returns of an asset (call this $ X_t $) which I have verified to be stationary. Let's say I generate a new time series $Y_i$ which is a filtered version of $X_t$ (that is I ...
Akshat Joshi's user avatar
2 votes
0 answers
137 views

Is there daily SPX level data going back to 1927?

While attempting to model the SPX index over time, I found a source here that purportedly has historical daily SPX data going back to 1789 which very likely seems to be backcasted since the ~500 stock ...
QMath's user avatar
  • 249
2 votes
1 answer
256 views

Critical values of trace statistic of Johansen cointegration test for arbitrary number of I(1) variables

I am trying to find the critical values of the trace statistic Johansen cointegration test for a large number of I(1) variables. However, I cannot find these values tabulated anywhere beyond n = 12 ...
md0101's user avatar
  • 63
3 votes
1 answer
285 views

Effect of back-transforming forecasted mean of log returns to get forecasted mean of price

When trying to forecast time series, say forecasting the level of a stock index so we can forecast the future values of an option, it tends to be helpful to analyze the log returns versus the original ...
QMath's user avatar
  • 249
1 vote
1 answer
613 views

How does one show that the Sharpe Ratio is closely related to the t-statistic of the mean differential return?

I see it being mentioned in many places, such as here, and even here. How should I interpret it? Suppose I have an array of signals, I, and returns of those signals, R Then my regression is R = a + BI ...
Dumb chimp's user avatar
1 vote
0 answers
52 views

GRS test does not reject a scalar multiple of the market factor

I have been playing with the GRS test (see my R script below) in relation to Why not use a time series regression when the factor is not a return?. I generated a $10,000\times 26$ matrix of returns on ...
Richard Hardy's user avatar
2 votes
1 answer
220 views

GRS test does not reject a nonsense factor in place of the market factor

I have been playing with the GRS test (see my R script below) in relation to Why not use a time series regression when the factor is not a return?. I generated a $630\times 26$ matrix of returns on 25 ...
Richard Hardy's user avatar
1 vote
2 answers
430 views

Equivalence of exponential moving average to simple moving average

I am aware of the differences between an exponential moving average and a simple moving average, with the weights of the latter being fixed and equal for each observation in the calculation compared ...
The User's user avatar
6 votes
2 answers
579 views

Why not use a time series regression when the factor is not a return?

I am trying to wrap my head around the statement that time series regression should not be used for testing a factor model when the factor is not a return. This has been mentioned in multiple posts, ...
Richard Hardy's user avatar
4 votes
1 answer
265 views

What color financial time series are there?

There is a folklore white noise hypothesis related to (and equivalent to some forms of) the efficient market hypothesis in finance -see references below. But are there some asset pairs whose return ...
plm's user avatar
  • 141
0 votes
1 answer
117 views

Estimate Open, High and Low prices from bid, ask and close prices

I know it's possible to efficiently estimate bid/ask spreads from OHLC prices and there are several methods to do so. However I have a data source that contains only bids, asks and close prices, no ...
pyCthon's user avatar
  • 2,131
1 vote
0 answers
54 views

Testing predictability of a proposed predictor in case of multiple returns

Say I have a T daily observations for the last ten years on a new predictor $x_t$ which I think is a predictor of the expected weekly return on the stock market, $r_{t,t+5} = r_{t+1}+...+r_{t+5}$, ...
wlsdnwlsntus's user avatar
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0 answers
134 views

Proving equivalent half life of SMA vs EWMA

I have recently read in a paper that the 25 day SMA and 36 day EWMA are equivalent for comparison in tests due to their half lives. The half life of the 25 day SMA is obviously 12.5 days and I ...
user66412's user avatar
2 votes
2 answers
182 views

What is the process for using OLS on time series models (HAR like)

I am reading about HAR models for realised variance and they all seem to use WLS or OLS to calculate the parameters. Now I understand how that works if you just use say the 10 years of AAPL intraday ...
BlueTurtle's user avatar

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