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

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184 views

Imposing Restrictions on Cointegrating Vectors, R example

The code given below estimates a VEC model with 4 cointegrating vectors. It is a reproducible code, so just copy and paste into your R console (or script editor). ...
7
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3answers
10k views

Are public historical time series available for ratings of sovereign debt?

The nice list of free online data sources Data sources online does not mention any data from ratings agencies. Are historical time series available for sovereign credit ratings (other than as ...
7
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2answers
2k views

Performance of Open Source Time Series Database for Financial Market Data

We would like to store financial tick data in a database (potentially billions of rows) and then create aggregated (open-high-low-close) bar data from it (e.g. 1min or 5min bars). It was mentioned ...
7
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2answers
2k views

What are common methods for modeling intraday trading volume?

What are the most common ways to model intraday trading volume, particularly for futures contracts? There are obviously a number of seasonal-type factors, like roll, economic news releases, time of ...
7
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4answers
2k views

How to compute momentum from equity time series?

Let's say I have time series of stock prices for many stocks. What's the best way to sort the stocks based on which have been going up/stayed the same relative to others? Can this be done with a ...
7
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2answers
5k views

How GARCH/ARCH models are useful to check the volatility?

Below a R code wrote by the moderator @richardh (whom I want to thank again) about ARCH/GARCH models. ...
7
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2answers
570 views

What time series database can be used with Python and Pandas?

I'm looking for a time series database that can be easily used with Python and Pandas objects such as DataFrame, Panel... But these objects will always contains time series. Ideally I'm looking for ...
7
votes
6answers
10k 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 ...
7
votes
1answer
771 views

How to annualize Expected Shortfall?

I have a time series with monthly data from which I compute the expected shortfall empirically, following the classical definition which can be found, for example, in wikipedia's definition. That is, ...
7
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5answers
703 views

Thoughts on how quantitative hedge funds use machine learning to invest in the stock market (algorithms, examples of data, etc.)

I believe there are several post on this general topic but I thought I would start my own thread. I'm a former fundamental hedge fund investor (i.e. modeling a company's financials, forecasting the ...
7
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0answers
636 views

Alternative to Block Bootstrap for Multivariate Time Series

I currently use the following process for bootstrapping a multivariate time series in R: Determine block sizes - run the function b.star in the np package which produces a block size for each series ...
6
votes
3answers
722 views

The Basis of Using Technical Indicators as Inputs

In the process of my research I very often come across academic papers regarding modelling and trading strategies that in one way or another incorporate some technical indicators. For example in some ...
6
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2answers
1k views

How to simulate cointegrated prices

Is there any simple way to simulate cointegrated prices?
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1answer
132 views

Why is the GARCH intercept supposed to be strictly positive?

Maybe it's a simple question but I don't really understand why it is theoretically required. Let's take the standard GARCH(1,1) $$\sigma^2_{t+1}=\omega+\alpha\epsilon^2_{t}+\beta\sigma^2_{t}$$ In most ...
6
votes
2answers
299 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 ...
6
votes
1answer
356 views

Is there any measure that is a non-trivial combination of VWAP and TWAP?

Is there any measure that is a non-trivial combination of VWAP and TWAP? For example: \begin{equation} \textrm{VTWAP} = \frac{\textrm{VWAP}+\textrm{TWAP}}{2} \end{equation} I'm thinking about ...
6
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2answers
262 views

Is there any research on pyramiding techniques of entering/exiting a trend?

I am looking for any research about optimal strategies for gradually building (scaling in) positions inside a trend as well as optimal gradual exit strategies on pullbacks/reversals to minimise ...
6
votes
2answers
553 views

Choosing the time-frame to test for cointegration

Is there a technique to choose the time-frame for a cointegration test (eg Augmented Dickey-Fueller's)?
6
votes
2answers
216 views

Is the average of independent Brownian Motions still a Brownian Motion?

If $W$ and $B$ are independent Brownian Motions (BM thereafter), then the average of $W$ and $B$ is $X_t=\frac{1}{2}(W_t+B_t)$. Where do I begin to show that indeed it is still a BM? Also, if both ...
6
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1answer
331 views

How to use physics models in Time Series Analysis and Forecasting.

I have been studying methods of Technical Analysis for several years and I am disappointed. I actually do not consider it useful. I have not met anyone who can constantly win in the market using these ...
6
votes
4answers
2k views

Is a stationary process necessarily mean-reverting?

Intuitively, a stationary stochastic process needs to be mean-reverting. This should follow immediately from the definition of stationarity: the mean of the process needs to be constant over time, so ...
6
votes
1answer
6k views

GARCH model and prediction

I have a question about the prediction of volatility and returns of a time series. Basically it is a question about prediction in the ...
6
votes
1answer
579 views

Are shorter holding period strategies better?

Consider two statistically identical strategies (identical information ratios, sample size, ratio of transaction costs to total profit, etc.) except that one has a much shorter average holding period. ...
6
votes
1answer
177 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 ...
6
votes
2answers
282 views

How to deal with zeroes in returns?

Suppose there are two time series that I want to analyze and compare. However, many, or most, of the data are zeroes for some reason. For example, consider a pair of intraday trading returns time ...
6
votes
1answer
513 views

Applying models with normality assumption on tick data?

Beginner question. Having read a couple of papers and book chapters on high-frequency data forecasting, I'm surprised (and confused) that the same time series techniques can be applied to high-...
6
votes
2answers
774 views

How do I incorporate time-variability in a pair trading framework?

Recently I have been looking at pair trading strategies from a cointegration perspective, as described in chapter 5 of Carol Alexander's Market Risk Analysis volume 2. As most quantitative finance ...
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2answers
516 views

Value at Risk for Futures Contracts

I would like to know how you would compute Value at Risk on a portfolio of futures i.e rates futures, commodity futures and equity. How do you deal with the discontinuous form of commodity futures for ...
6
votes
2answers
422 views

Is it too important that my residuals be normal? I am Using an ARMA/GARCH model

I am trying to fit an ARMA/GARCH model to a time series. I found that the best candidate is an ARMA(1,0) + GARCH(1,1) with gaussian white noise It has coefficients with p-values near cero and the ...
6
votes
1answer
252 views

Up and Down days in GBPUSD and a Filter

I want to study if the odds of an up or down day in a forex pairs is 50-50. I just count the total number of up and down days in X years and compare it with the total days. The results are very ...
6
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0answers
282 views

Markov-Switching Multifractal and FX Rates

Is there a better model than Markov-Switching Multifractal (MSM) for detecting regime shifts in FX rates across multiple time horizons? I am especially interested in the different aspects of the ...
5
votes
3answers
640 views

Why do we usually model returns and not prices?

I think this is a quite similar question for most of you, however it is not completely understandable for me at the moment: Why do we usually use returns and not prices to model financial data in ...
5
votes
1answer
724 views

How to apply quasi-Monte Carlo to path-dependent options?

Following up on my recent question on variance reduction in a Cox-Ingersoll-Ross Monte Carlo simulation, I would like to learn more about using a quasi-random sequence, such as Sobol or Niederreiter, ...
5
votes
3answers
922 views

How to normalize Futures data(different leverage) for cointegration test?

For example I want to construct 2 time series, one for ES and the other for NQ and test for cointegration. ES one point equal to 50$. NQ one point equal to 20$. If I have the following data: ES[0]=...
5
votes
1answer
434 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 ...
5
votes
1answer
220 views

From $AR(p)$ to SDE

Let the Vasicek model to be $$\Delta r_{t}=k(\theta - r_{t-1})\Delta t+\sigma\Delta z_{t}$$ Due to the fact that $$\Delta r_{t}=r_{t}-r_{t-1}$$ if you let $\Delta t=1$, it is easy to see by ...
5
votes
2answers
211 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 ...
5
votes
1answer
2k views

How to calculate the conditional variance of a time series?

I am reading a paper where the term conditional variance is mentioned, but I am not really sure what is meant by this and how this can be calculated: Fig. 2 shows the conditional variances of the ...
5
votes
1answer
3k views

How to use Newey West covariance corrector?

I have implemented the following model: daily_vol(t+1) = A*daily_vol(t) + B*weekly_vol(t) + C*monthly_vol(t) + error where vol means volatility, and A, B, C are ...
5
votes
4answers
707 views

Regressor: Nominal return, continuous return or first difference?

Suppose the application is linear models in financial econometrics. If we want to analyze stocks, the standard approach is to take the continuous/log return: $\ln{ \frac{P_t}{P_{t-1}} }$. Suppose, ...
5
votes
1answer
422 views

Major FX pairs - Pentahedron Data Structure

I read an interview today with Stephane Coquillaud. He talked about this idea of formulating a data set of the G5 currencies as a pentahedron. The obvious benefit is the fact that there is more ...
5
votes
2answers
137 views

Interpretation of Correlation

I have two geometric Brownian motions (GBMs) driven by the same underlying Brownin motion, namely \begin{align*} S_t^1 = S_0^1\exp\left(\left(\mu_1 - \frac{\sigma_1^2}{2}\right)t + \sigma_1 W_t\right),...
5
votes
3answers
385 views

How is stock data objectively different to this random walk?

I have a random walk that is generated as so using python, numpy, and matplotlib ...
5
votes
2answers
174 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 ...
5
votes
2answers
122 views

Is there a relation between these two forecasting/estimation approaches?

When learning econometrics I have usually seen stuff from the following perspective: Assume $Y_t = f(X_t) + e_t$, where f is some function of $X_t$ (typically linear). For example, assume $Y_t = X_t ...
5
votes
1answer
185 views

DCC GARCH - Specificating of ARCH and GARCH parameter Matrices STATA

The command in STATA to calculate the DCC model of two variables is: mgarch dcc ( x1 x2=, noconstant) , arch(1) garch(1) distribution(t) $$ \begin{bmatrix} ...
5
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0answers
204 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 ...
5
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0answers
791 views

Asymmetric Volatility Modeling (Interpretation)

I am currently writing a paper on asymmetric volatility modeling of brent, gold, silver, wheat, soybean and corn from 1986-2012 and divided them into 4 sub-sample periods (i.e. 1986-1991, 1991-1997, ...
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votes
5answers
1k views

Is there any way to easily estimate and forecast seasonal ARIMA-GARCH model in any software?

I use R to estimate a seasonal ARIMA(8,0,0)(5,0,1)[7] model for the seasonal differences of logs of daily electricity prices: ...
4
votes
2answers
919 views

How to remove outliers in financial times series?

I have a bunch of time series; i need to clean them before modelling. So far I just know the “filtering/smoothing” method : -Ex: moving average methodology (filter the data with a moving average (...