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

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83 views

Identifiability of a state space model (Dynamic Linear Model)

Take a general linear Gaussian state space model (SSM)(aka Dynamic Linear Model DLM): $X_{t+1}=FX_t + V_t$ $Y=HX_t+W_t$ $V_t \sim N(0,Q)$ $W_t \sim N(0,R)$ I am interested in the ...
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49 views

How to construct a cointegrating vector using more than 2 price series in R?

I use now this code from hier Why does the following data fail my cointegration test? with slightly modification of possibility to load something directly from Dropbox file storage . ...
2
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0answers
184 views

Potential pitfalls in the use of correlation

Background: The red line is an index, which goes from 0 to 100, measuring uncertainty in the markets. The dark blue line is a price index, which has a lower bound at 0, and virtually no upper bound. ...
2
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1answer
365 views

Interpretation of cross-correlation matrix when one sample distribution is not normal

I am looking at the variance of (log) price changes in securities vs. the amount of social media discussion about them. I'm not interested in building a model. I'm just looking to see if there is a ...
2
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0answers
70 views

Is there an appropriate sequence to tests during model diagnosis?

How should one order (sequence) the following tests? Stationarity test Johansen cointegration test Normality/Histogram test Autocorrelation test Heteroskedasticity test Multicollinearity test ...
2
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0answers
112 views

Difference between kappa and delta in mixed-effects model

(This question is a crosspost from Cross Validated) I have a following stochastic model describing evolution of a process (Y) in space and time. Ds and Dt are domain in space (2D with x and y axes) ...
2
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1answer
731 views

a simpler test for normality given skewness, kurtosis and autocorrelation and size of time series

I typically do a JB (Jarque Bera) test and DW (Durbin Watson) tests for check for normality given skewness, kurtosis and autocorrelation of the data. However this requires a CHI distribution table ...
2
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0answers
272 views

What does T statistics of Information Coefficient indicate?

Hi I am looking for a clear explanation of T statistics concept. Especially in quantitative equity portfolio management context, what does T statistics of monthly Information Coefficient for one ...
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3answers
3k views

What does it mean by autocorrelation coefficient near 1?

It is said that the time series has a stochastic trend if the first autocorrelation coefficient will be near 1. Q1) What does it mean by the above statement? Q2) How do we calculate the first ...
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2answers
130 views

What methods are there for showing a time series is mean reverting?

What methods are there for showing a time series is mean reverting? Is there a hypothesis relating to the Ornstein-Uhlenbeck process for example?
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3answers
161 views

Technical Indicators reference

I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
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2answers
426 views

Optimizing Principal Component factor weightings over time

I was given the returns of a cross-asset class portfolio of ETFs and I conducted PCA to obtain factors on dates from T-n, T-3, T-2,..., T. What I would like to do is decompose the market moves from ...
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1answer
434 views

Why does $\hat{\epsilon}'\hat{\epsilon}$ of a factor model measure risk?

$\hat{\epsilon}'\hat{\epsilon}$ from the market model: $R_{it} - \hat{\alpha} - \hat{\beta}R_{mt} = \hat{\epsilon}$, or from a factor model such as the Fama-French 3 factor model, is often used in the ...
1
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1answer
237 views

predict next day's close price using hmm

I am reading this paper(Stock market forecasting using hidden Markov model: a new approach) and get confused about how they predict the next day's close price. Below is what the authors say about how ...
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1answer
410 views

detecting and measuring lead lag effect

Given two time series data. I remember there is one statistics that tells you one is the leading factor while the other is the lagging factor. However, i do not remember the exact details. correlation ...
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1answer
215 views

Estimate correlation of time series whose histories differ in length

Very often in quantitative analysis (e.g. calculating portfolio volatility) we have to analyze various time series - mostly returns - whose lenghts differ. Risk systems usually apply a one-factor ...
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1answer
223 views

Should I analyze the tick data day by day?

Let assume that we have one month of tick data which were traded at NYSE. We want to model the price changes as a function of the last p lags of price changes and the last q lags of the time duration ...
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1answer
207 views

Examples of non-increasing variance of a time homogeneous Markovian process

This is an edit to the previous question, on stationary process, which was answered by Richard below. Let $x_t$ be a zero mean, time homogeneous Markovian process over time $t$ starting from ...
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1answer
2k views

How to apply Ljung Box Test?

I am checking the closing prices(about 9000+ prices) of the stocks data to test for randomness. The test I am using is Ljung Box test, in MFE toolbox for MATLAB, I used 300 data of closing prices, ...
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1answer
298 views

Help with understanding a normal distribution/probability question

Could someone please help me translate what this is saying on page P15, section 4.2: http://www.ntuzov.com/Nik_Site/Niks_files/Research/papers/stat_arb/Ahmed_2009.pdf Specifically: When the ...
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1answer
428 views

Unsystematic/Idiosyncratic/Firm-specific volatility/variance in the market model?

I was asked to use idiosyncratic volatility as a regressor in a cross-sectional regression upon cross-sectional returns as the dependent variable. Returns can be thought of as the raw log stock return ...
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1answer
59 views

Memory-efficient clustering algorithm for large time-series datasets

I have a simulation task at hand with ~1e6 time series to be clustered on the basis of statistical measures every few days in the simulation. Most clustering methods I'm aware of require an affinity ...
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1answer
30 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
160 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
58 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
92 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
203 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
376 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
195 views

Normalization of Market Data in Time Series Correlation

Suppose we have 2 time series of market data, one for each security and we want to correlate between these 2 securities. My question is How do we handle gaps of missing data in the time series? ...
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1answer
98 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 ...
1
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1answer
134 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
199 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
153 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
729 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
133 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
11 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
45 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
35 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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1answer
132 views

ARMA+GARCH prediction with package rugarch (R)

I am analyzing FTSE 100 series, from 2007-01-01 to 2010-12-31 (university exam homework). I have to use the data 'til 2010-11-30 as sample, and the remaining (23) observations as in-sample forecast ...
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0answers
44 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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1answer
83 views

Normalizing SPY ETF time series data with its sector ETFs?

I am looking to compare the returns of a sector rotation strategy between the various SPDR sector ETFs XLY, XLP, XLE, XLF, XLV, XLI, XLB, XLK, XLU vs. ...
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0answers
87 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
49 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
47 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
42 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
55 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
103 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 ...
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0answers
51 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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250 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 ...