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

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1answer
59 views

Is there any package in R for conditional autoregressive range model (CARR)?

I am working on a project which requires volatility estimation using range based volatility. Is there any package in R which helps me in estimating the CARR model proposed by Chou (2005).
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2answers
191 views

Logistic Regression of tick data

I've been given some data (it's financial tick data) and I want to predict based on some observed variables whether the next move will be up, down or unchanged. So I have been trying to use ...
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1answer
64 views

Does it make sense to interpret autocorrelation and box test on 5 data points?

I am trying to see if after I trade a stock the price movements at 2, 5, 7, 10, 30 and 60 seconds after exhibit any autocorrelation. Below I have the returns from my trade price to the trade 2,5,7,10 ...
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1answer
129 views

Cointegration tests: how do you accurately test the necessity of time trends in the Johansen and Engle-Granger Test?

Is there a correct and up to date procedure? I just run the equation in VEC form and test the significance of the time trends? What are the possible problems that I should be aware of?
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1answer
210 views

Machine learning to build top 3 price scenarios over n days

I have a time series of closing prices for a given stock. I would like to formulate possible future scenarios for the price. My intention is not to use these "likely" scenarios to take any position. ...
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2answers
489 views

Using Technical Indicators for forecasting Financial time series using Machine learning models

Hi I am trying to use financial technical Indicators for forecasting, using machine learning models. The usual approach in time series cross validation is to use a moving window or growing window. ...
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1answer
105 views

To lump or not to lump

Suppose I have a very simple asset whose price takes only three possible values: $X_t\in \{-1,0,1\}$. I also got some discrete time series $X = (X_t)_{t\geq 0}$ and I would like to come up with a ...
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1answer
110 views

How to model the effect of earnings surprises on long-term returns?

I'm looking into modeling the relationship between EPS announcement surprises with long-term returns (1 quarter to 3 years with intervals). I've based my current methodology off papers looking at the ...
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1answer
140 views

Explain drop in Correlation between two time series in consecutive periods

I have a time series for a security list with 2 parameters calculated for each time period. For example, for a stock XYZ, I have Param1 and Param2 calculated over various time periods stacked against ...
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1answer
217 views

High correlation will help detect spurious regression over cointegration?

I'm analyzing two financial time series with Johansen method. A high Correlation coefficient using the Pearson method will help me to detect spurious cointegration models to avoid? If this is not ...
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1answer
169 views

Constant term in linear regresion

Can someone give a mathematical proof as to why including a constant in a linear regression equivalent is to running a regression with demeaned data and zero constant? More specifically, consider the ...
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1answer
833 views

Lagged dependent variable, yes or no?

I read conflicting opinions about the inclusion of lagged dependent variables in modeling, and I guess it is partly up to the researcher and depending on the scope and goal of the research. I'm ...
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1answer
136 views

How does one use the Johansen cointegration test in a linear time series model?

How does one use the Johansen cointegration test in a linear time series model? Should I only use normalized coeffients for interpretation? Or, once I know that the variables are cointegrated, do I ...
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0answers
32 views

Example Scalar Model Extended Kalman Filter

I have a simple question. I think not a question is, is a request. This month I have been studying how to understand and implement the Kalman filter algorithm for simple models such as the local level....
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0answers
33 views

Problems in computing VaR with GARCH-GPD-copula approach

I use a time-varying Gaussian copula (with GARCH-filtered standardized residuals modeled semiparametrically with Gaussian kernel interior and GPD tails, i.e. generalized pareto distributed) to ...
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0answers
10 views

How to get daily OHLC (fints) from minutes OHLC (fints) in MatLab?

I have a minutes OHLC time series stored in fints object, how can I get a new fints object which contains daily OHLC? What is the easiest way to do it?
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0answers
33 views

Account for empirical relationship between signal and market data

I have two monthly time series : one is a 'signal', on which I will base my decision to buy or short-sell, and the second one is the time serie of a given asset's price. I have implemented this ...
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0answers
30 views

Estimating time-varying tail dependence for Archimedean copulas

Patton (2006) defines the upper tail dependence coefficient for a time-varying bivariate SJC copula as $$\tau^u_t=\Lambda \left(\omega_u + \beta_u \tau^u_{t-1}+\alpha_u \frac{1}{10}\sum^{10}_{i=1}|u_{...
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0answers
64 views

Cointegration for forex using ARMA model to forecast the spread

I am working on an automatized quantitative strategy that use cointegration in Forex. I am backtesting this strategy in Python. Please see below the python file: https://drive.google.com/file/d/...
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0answers
46 views

How to write time-varying functions in R? Applied example

Let's say I want to use a Gaussian copula $$C_{R_t}(\eta_1, ..., \eta_n) = N_{R_t}(N^{-1}(\eta_1), ...,N^{-1}(\eta_n))$$ with a time-varying correlation matrix $R_t$. Through DCC we model the ...
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0answers
68 views

How to choose a GARCH model which delivers iid standardized residuals?

For my thesis I first need to examine nine financial time series and fit a conditional volatility model such that the obtained standardized residuals ($z_t = \epsilon_t / \sigma_t$) are approximately ...
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0answers
30 views

Marginal Distribution using GARCH model

I have n return series. I fitted AR(1)-GARCH(1,1) to each return series. Then used PIT(residuals) to transform the residuals to uniform. Then I fitted n dim copula to the data. I simulated 1000 points ...
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0answers
41 views

Distribution of AR and MA polynoms roots in ARMA/ARMA-GARCH models

I have another noob question. So, for example, I have ARMA(2,2) model: $$ x_{t} = \phi_{1}x_{t-1} + \phi_{2}x_{t-2} + e_{t} + \theta_{1} e_{t-1} + \theta_{2} e_{t-2}$$. So, I have 2 polynoms: $$1 - \...
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0answers
37 views

Problem with overlapping data when testing futures market efficiency

In my case non-overlapping data would represent the scenario where futures prices (3 months) do not correspond to the futures spot prices in terms of delivery date. For example, futures settlement ...
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0answers
35 views

Are there alternatives to the Box-Tiao decomposition in identifying mean reverting portfolios?

As documented in this paper, Box-Tiao decomposition (a way to decompose multiple time series into components with different speeds of mean reversion) can be used to identify mean reverting portfolios. ...
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0answers
54 views

Estimating Daily Dynamics using Hourly Data

This article gives a nice outline of how daily data can be used to estimate cointegration on a monthly horizon. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1404905 I'd like to use the same ...
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0answers
67 views

How to fit exogenous + GARCH Model In Python?

I am studying a textbook of statistics / econometrics, using Python for my computational needs. I have encountered GARCH models and my understanding is that this is a commonly used model. In an ...
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0answers
51 views

copulas and time series

Can anbody explain how Copulas are used to describe the dependency between, for example, the return on two different stocks? I understand how Copulas are the "glue" that binds the two marginals ...
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0answers
127 views

Application of time series analysis to Bitcoin prices

Various exchanges allow for the trading of Bitcoins. The price of Bitcoin was very volatile since the inception of the system, today it is 391.76 USD: I wonder whether time series analysis tools ...
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0answers
52 views

state space for affine yield curve

i would like to reproduce in R the working paper " Affine free arbitrage class of Nelson Siegel term structure". The authors considering the equation of nelson siegel plus an adjustment term(C(t,T)) ...
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0answers
52 views

Transforming Variables in time series regression

I have multiple quarterly time series data and trying to build a linear regression model using this dataset. Should the transformations on the LHS and RHS be the same i.e QoQ percent changes? Could ...
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0answers
50 views

labeling high frequency signal data

Was curious if anyone has methodologies they can recommend for systematically labeling (discrete) signals generated from intraday tick data for use in classification or detection models ?
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0answers
80 views

Cointegration and variance of time series

Given that $X_t , Y_t$ are two cointegrated random processes, what can we say about the relationship between variance of the two increments $var(X_{t+h}-X_t)$ , $var(Y_{t+h}-Y_t)$ for a given $h>0$...
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0answers
57 views

Modelling turnovers with a random walk. Is it right?

I need to analyse a bunch of weekly time series that reflect the turnovers of various companies. I already read that return rates or share prices show stochastic patterns that can be modelled by a ...
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0answers
112 views

modeling regime switching for Correlation matrix

I am trying to estimate covariance in multiple time series. However, I want to do this using a regime-switching framework. So, I start with fitting a GARCH(1,1) model and then de-volatalize the series....
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0answers
65 views

How to estimate constrained a constrained VAR(1) with MATLAB?

Suppose I want to estimate the following VAR(1) model: $$ Y_t = \mu + \Phi Y_{t-1} + \varepsilon_t $$ where $Y_t=(y_{1t}, y_{2t},…,y_{kt})'$, $\mu=(\mu_1,…,\mu_{k})’$ and $\Phi$ a matrix of ...
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0answers
281 views

What machine learning method is more suitable for prediction of financial time series? [closed]

I have some time series from a stock exchange market. For each of them, I want to answer the question that whether the price will grow at least p percent in the d coming days or NOT(and during these ...
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0answers
783 views

Exporting Time Series Data For Securities Prices From Bloomberg to Excel

I have a list of securities over a thousand entries long that I want to construct a time series of prices for over a specified historical period (e.g. 2/01/10-2/20/10). Doing this manually would take ...
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0answers
79 views

Rule of Thumb for minimum length of time series for AR(1) estimation

I have a data set of 350 points, I want to estimate the lag 1 auto correlation for different sub-sets of the data. More precisely I want to take non overlapping windows of length 1,2,3....n and ...
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1answer
326 views

Simulate non-stationary time series with cointegration

how can I simulate/generate two non-stationary time series (with unit root) so that they can be also cointegrated (using R or Matlab). Thanks in advance.
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0answers
83 views

Max Likelihood via Marquardt Optimisation

I asked a related question here: How to apply Levenberg Marquardt to Max Likelihood Estimation I tried the approach suggested it works for some of the parameters but not the variances. I spoke to ...
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1answer
233 views

how to compute daily skewness of S&P daily return timeseries under no other more high - frequency time series?

As we all know , return time series marked features: fat tail or negative skewness and peakedness. For a similar problem of variance computation, we can compute variance by garch model and other ...
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0answers
370 views

Time-varying correlation via state-space representation and Kalman filter

Let a linear time-varying mode like this one: $y_{t}=\alpha_{t}+\beta_{t}x_{t}+\epsilon_{t}$. You can also suppress the constant term to simplify this example: $y_{t}=\beta_{t}x_{t}+\epsilon_{t}$. ...
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0answers
294 views

Fluid dynamics for order book depth modelling

Would someone be able to give me an idea what type of fluid dynamics I could look at for modelling the order book? My background is more signals-related maths (correlation, covariance, fourier etc). ...
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0answers
41 views

How to make a historical index of a group of materials in which the set of materials changes every month?

The question may sound simple however for the moment it is a brainteaser to get it right, let me explain: the exercise is to be done on +/- 200 groups of materials (matgroups) one matgroup can ...
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0answers
269 views

Simple EOD computations for tick data

As part of End-Of-Day calculations once a particular market/exchange has closed for all the tickers traded on that market one may typically compute the following properties: OHLC Bid/Ask Price (mean,...
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0answers
475 views

Oscillatory time-series forecasting

I was wondering if this mean(160)-reverting/oscillatory time series "SUM" can be considered chaotic & forecastable to some extend short-term? http://sg.myfreepost.com/sgTOTO_analysispower.php?...
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1answer
127 views

Building predictive model for closing price using only previous days data

I am trying to determine which quantitative model to try and build a predictive model for the next day's closing price for all the S&P stocks based on their bar for that particular day. However, I ...
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5answers
5k views

Which library shall I use for time series analysis in Java?

I'm looking for a library to do some time series analysis in Java but I can't find anything suitable. I've found plenty of libraries such as Math3 of JSAT but there's much I can you for my problem. ...
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4answers
331 views

Intermarket analysis - related time series?

I'm about to embark on training a neural network on daily forex data, with a view to obtaining a predictive network. I'm also interested in using data other than the forex currency pair data itself, ...