A sequence of events measured at disrete points in time.

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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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3answers
62 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
315 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
362 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 ...
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1answer
119 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
164 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
333 views

How does Volatility Pairs Trading work?

I've read some material related to pairs trading for equities and I understand the process of finding non-stationary pairs price series that can be cointegrated to form a stationary series. The basic ...
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1answer
204 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
187 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
1k 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
288 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
357 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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2answers
183 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
65 views

What do I need to do with my data before fitting the ARIMA model?

I'm fitting a stock price time series data to ARIMA model and I have a question about the assumption. Is it that ARIMA only applies to stationary data? The ACF and PACF of the data (and the logged ...
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1answer
108 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
54 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
120 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
166 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
122 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
557 views

Value Weighted Return

I recently have started to look at some data from CRSP, and they have a metric called Value Weighted Return (two versions with and without distributions). When I looked it up, it seemed that this ...
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1answer
500 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
124 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
20 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
52 views

Volatility updating rule using r

I'm trying to program a volatility updating rule using iteration. I start with the well know Heston-Nandi model where the returns dynamics are : with is iid standard normal randome variable, where ...
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1answer
87 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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0answers
131 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
59 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
142 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
67 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
196 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
463 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? ...
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1answer
148 views

Book recommendation for time series analysis

I have been trying to wrap my head around Engel-Granger test and jcitest etc. I have failed thus far. If possible can someone guide me about which books to start with and possibly reach to ...
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3answers
947 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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3answers
278 views

Modeling Financial Time Series

Price time series are not stationary. So we difference them and get the return time series, which are stationary. Does this mean, it is always a good idea to model only the return series of financial ...
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1answer
451 views

Selecting timeframe for time series analysis

In technical analysis, we may use confluence of direction for 3 timeframes to roughly gauge bias of market now. Similarly, if we use time series forecasting methods to predict(say daily data-whether ...
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0answers
14 views

Finding optimal ewma and number of periods usedas features in a time series regression

I am using an exponential moving average (ema) to smooth the return of a price time series. I then want to use the last n periods (features) as the independent variables of the time series to predict ...
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0answers
44 views

Greenplum database storage model for time series data

I have to deploy a greenplum database for analysis of time series data. I will have around 50 different time series (s1,s2,s3,...s50) and each series will have multiple pairs (time is 1 hour average ...
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50 views

Interpretation of Cointegration results, pValues and t-Stat

This is a follow up to: Cointegration results interpretation validation? I ran another Engel Granger Test on a pair, The results I get: ...
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0answers
19 views

Residual maturity vol

The following question is probably (from a practical point of view) more relevant for EM markets which typically exhibit a more pronounced forward volatility compared to spot volatility. Say I buy a ...
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0answers
241 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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78 views

Identifiability for Time Invariant State Space Models

Kevin Murphy's Kalman Filter toolbox (for Matlab) contains an example where it's the fact that the state space system in not identifiable causes problems. I include the example in it's entirety but ...
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2answers
243 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, ...
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0answers
120 views

Fitting Egarch Model

I am performing a monte-carlo simulation in MATLAB for the first order EGARCH model in which case I am simulating 100 paths of size 500 assuming Gaussian and Student's-t distributions for the ...
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1answer
84 views

Can we model components in a set of multivariate multi-period time-series data?

There are N data sets in periods occurring weekly/monthly, across a 10-year historical timeline. In each period, five dates are observed (labelled a to e), where a denotes the day the period ...
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0answers
97 views

Modelling interest rate: AR(2) modelling

I have a time series of spread that follows an $AR(2)$ (Autoregressive model of Order 2). I need an interest rate model that represents that dynamics. What model should I use?
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0answers
254 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
266 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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36 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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252 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 ...
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40 views

Inferring Returns From Minimal Data Points [duplicate]

Possible Duplicate: How much data is needed to validate a short-horizon trading strategy? Suppose I have daily returns for a trading strategy against one month of data. Before starting ...