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

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2
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2answers
167 views

Does unit root stationary imply mean stationary and variance stationary?

Newbie question. I am reading about stationary series and understand that it has many forms: mean stationary variance stationary covariance stationary My question is does unit root stationary ...
5
votes
2answers
172 views

Two correlated time series - driver and follower

Say that there are two time series of highly correlated stocks one of which is the driver and the second one follows the first one. What mathematical measure or formula would you use to identify ...
15
votes
8answers
7k views

What is the best data structure/implementation for representing a time series?

I was wondering what is best practice for representing elements in a time series, especially with large amounts of data. The focus/context is in a back testing engine and comparing multiple series. ...
1
vote
2answers
263 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. ...
2
votes
1answer
211 views

Stress Testing Methods

I'm working on the following task: Given quarterly data: a time series representing the 1-year realized (10 years of data) rates of default on a portfolio of mortgages a slew of ...
2
votes
1answer
138 views

ARIMA model, cannot get rid of low order ACF spike

I've gone through all the steps to fit a good ARIMA model - I plotted the data, I looked at the ADF tests, I looked at the ACF plot with no AR and MA terms just a constants. I came up with an ...
3
votes
1answer
490 views

How to Calculate Confidence Intervals for Moving Averages Given Nonindependence?

I've plotted 30-year moving averages across time for a couple of portfolios, and I was wondering how to calculate a 95% CI for the these moving average data (i.e., across all moving average data ...
0
votes
3answers
2k 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. ...
1
vote
1answer
72 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 ...
2
votes
1answer
219 views

Event studies using revenue data vs. measuring abnormal returns

This may be a silly question, but does there exist a methodology for examining the impact of "events" on companies that are not publicly traded? I suppose it would look at abnormal revenues rather ...
0
votes
0answers
89 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: ...
3
votes
0answers
85 views

Filtering out AR(1) effects before using stochastic volatility model

I wonder if I first filter out AR(1) (autoregressive model with lag 1) effects from univariate time series and then fit stochastic volatility model does above procedure introduce any bias at first or ...
16
votes
6answers
6k views

What is the intuition behind cointegration?

What is the intuition behind cointegration? What does the Dickey-Fuller test do to test for it? Ideally, a non-technical explanation would be appreciated. Say you need to explain it to an investor ...
1
vote
1answer
143 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? ...
2
votes
3answers
235 views

Calibration of a GBM - what should dt be?

I have a time series of daily data that I want to calibrate GBM parameters $\mu$ and $\sigma$ to. Using the discretized solution $$ S_{t_{i+1}} = S_{t_i}\exp\left(\left(\mu - ...
1
vote
1answer
70 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 ...
0
votes
0answers
20 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 ...
4
votes
3answers
247 views
1
vote
0answers
178 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 ...
2
votes
1answer
49 views

What are the properties of the Expected Shortall measure when split in multiple time periods?

Suppose I have a single time series of losses $L$ that consists of two sub-parts $L_1$ and $L_2$. Is there a relationship that relates the expected shortfall of $L$ to the expected shortfall of $L_1, ...
3
votes
0answers
147 views

GMM time-series regression factor model with factors that are not returns

Factor models with factors that are not returns are usually estimated and tested by cross-sectional regressions. However, there is a way to use time-series regression to estimate and test the model. ...
10
votes
3answers
363 views

How to account for market movement when some exchanges are closed?

Daily data, such as open and close prices, is often available for much longer periods than high-frequency data. However, whenever backtesting any strategy that examines instruments traded in different ...
2
votes
2answers
207 views

Intraday Data - Stylized Facts?

Can someone give an overview or literature on Intraday Data Stylized Facts? In particular for equity market returns or exchange rates.
1
vote
1answer
184 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
124 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 ...
2
votes
1answer
451 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 ...
0
votes
0answers
390 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 ...
1
vote
0answers
60 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
191 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
214 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 ...
3
votes
3answers
100 views

Modelling currency exchange rates timeseries data across re-denomation dates

I am working with data for an exotic currency, that has been re-denominated a couple of times during the twenty years of data that I have. What is the best way of 'normalising' the data, so that I ...
2
votes
0answers
97 views

State Space models with Short Time Series

My problem is that I have a state space model that I estimate using the Berndt–Hall–Hall–Hausman (BHHH) algorithm. The state space model is relatively simple in that the hidden part follows a pure ...
4
votes
3answers
1k views

Pairs trading: Question on non-negative profits, size of the positions and trading signals

I'm trying to backtest Pairs Trading but have become a bit confused on the different methods of selecting pairs, how to look for trading signals and what size of the positions to take in the assets. ...
1
vote
1answer
184 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.
6
votes
6answers
6k views

Why non-stationary data cannot be analyzed?

Searching online, i found out that non-stationary cannot be analyzed with traditional econometric techniques as in case of non-stationarity some basic model assupmtions are not met and correct ...
0
votes
1answer
172 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 ...
1
vote
1answer
174 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 ...
2
votes
2answers
165 views

What are the proper metrics to look at for checking discrepancies in these two time series

I am obtaining bid/ask price and volume market data from two different sources for the same ticker and for the same day and checking to see that at time intervals X they are "roughly the same". The ...
1
vote
3answers
480 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
0answers
73 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 ...
3
votes
5answers
152 views
4
votes
1answer
166 views

Simulating state space model with AR(1) dynamics

I asked a question similar to this previously: https://dsp.stackexchange.com/questions/16341/simulating-a-state-space-model However I think I have a better handle on it now and want to re-ask it: I ...
5
votes
2answers
170 views

Economic contagion to individual stocks (ideas for analysis)

I'm doing my undergraduate thesis on firm-level contagion. Specifically I look at a measure of performance over a financial crisis (e.g. raw stock returns), then run cross-sectional regressions with ...
4
votes
0answers
154 views

Calculating volatility of inhomogeneous time series

I am reading an article by Zumbach and Müller whose name is Operators on Inhomogeneous Time Series. This is interesting in general, but my main goal is to learn a good and efficient method to ...
1
vote
1answer
205 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 ...
0
votes
3answers
292 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 ...
5
votes
0answers
94 views

2-state HMM / ARMA process?

I have issues with this problem: Let $\{X_t, t\in \Bbb N\}$ be a 2-state stationary Markov chain, with transition $M$ (and $M(1,2)\neq 0 \neq M(2,1)$), let $\{W_t, t\in \Bbb N\}$ be a strong Gaussian ...
4
votes
1answer
262 views

Ornstein versus AR(1) for modeling stationary data

I've come across several posts regarding parameter estimation for O-U models given some stationary data (say, some sort of mean reverting spread), but I can't seem to find an answer as to why modeling ...
6
votes
2answers
270 views

Why do I have a statistically significant slope regressing R(t) on R(t-1)

I am reading Cochrane's lecture note here He mentioned that when you regress annual return on time t on that of time t-1, you will have neither statistically significant nor economically significant ...
1
vote
1answer
130 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 ...