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

learn more… | top users | synonyms

2
votes
0answers
63 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
votes
0answers
105 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
votes
1answer
288 views

Average beta of index consitutents w.r.t. the index is 0.60

I have 1 year time series data of 300 constituents of the Australian All Ordinaries index (which is composed of 491 firms). The missing firms are mostly smaller firms. I run the market model $R_{it} ...
2
votes
1answer
653 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
votes
0answers
261 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 ...
1
vote
3answers
398 views

Handling Missing values in stocks returns when estimating the co variance matrix

What is the best way to handle missing values when stocks did not exist for the entire historical period?.
1
vote
3answers
2k 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 ...
1
vote
3answers
127 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 ...
1
vote
2answers
341 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 ...
1
vote
1answer
383 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
vote
1answer
142 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 ...
1
vote
1answer
182 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 ...
1
vote
1answer
387 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 ...
1
vote
1answer
207 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 ...
1
vote
1answer
192 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 ...
1
vote
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, ...
1
vote
1answer
293 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 ...
1
vote
1answer
368 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 ...
1
vote
1answer
103 views

using garch to forecast volatility but getting low persistence model

I am using a GARCH(1, 1) model to try model volatility for a certain stock. I have a GARCH function in matlab that returns the three parameters, omega, alpha & beta. I then use this parameters ...
1
vote
2answers
223 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. ...
1
vote
1answer
104 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 ...
1
vote
1answer
70 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 ...
1
vote
1answer
126 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? ...
1
vote
1answer
60 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
vote
1answer
121 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 ...
1
vote
1answer
169 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 ...
1
vote
1answer
126 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 ...
1
vote
1answer
534 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 ...
1
vote
1answer
125 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 ...
1
vote
0answers
52 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 ...
1
vote
0answers
25 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 ...
1
vote
0answers
153 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 ...
1
vote
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 ...
1
vote
1answer
160 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.
1
vote
0answers
70 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 ...
1
vote
1answer
202 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 ...
1
vote
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? ...
0
votes
1answer
163 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 ...
0
votes
3answers
1k 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. ...
0
votes
3answers
280 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 ...
0
votes
1answer
478 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 ...
0
votes
1answer
73 views

How to forecast bond price with time series

I have the goal of being able to develop a model that can forecast the future prices of european government bond (or other private bonds), particularly from the historical prices and returns of the ...
0
votes
0answers
12 views

Option Time decay [on hold]

I have option prices.If I am naked in straddle then time decay is use full for hedge my position in range bound market. when is the correct time to setup my portfolio? And big thing is that just 7 ...
0
votes
0answers
13 views

Cross-post on the prediction mean squared error of a model

In accordance with what discussed in the meta here I am cross-posting this question from cross-validated. Suppose my model is $y_t = \alpha + \beta t + \epsilon_t$ the l-step-ahead prediction is ...
0
votes
0answers
20 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 ...
0
votes
0answers
54 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 ...
0
votes
0answers
67 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: ...
0
votes
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 ...
0
votes
0answers
304 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 ...
0
votes
0answers
87 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 ...