# Tagged Questions

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

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### 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 ...
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### Is R being replaced by Python at quant desks?

I know the title sounds a little extreme but I wonder whether R is phased out by a lot of quant desks at sell side banks as well as hedge funds in favor of Python. I get the impression that with ...
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### Bloomberg tick data timezone offset

I am using python to access the Bloomberg Desktop API and am running into issues with the timezone conversion for their tick data. The data they deliver is supposed to be UTC but there is something ...
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### Time series analysis on illiquid price data?

Say for example I have the following company in some specialized industry: A - Company that is about to be listed in Exchange 1, i.e., no price history B - Company that produce similar products as ...
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### johansen cointegration test eviews interpreation

I am not sure whether i am interpreting the cointegration test correct. This is the test result : Because of the probability of the test i understand that my series are cointegrated of order 2. ...
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### 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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### High frequency price forecast model ARMA GARCH or another?

Can you reccomend model for high frequency data (1 second and less) (returns and volatility forecasting)? Most papers use ARMA, GARCH etc in 1 minute and lower time frame. PROBLEM ARMA does not know ...
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### Unsmoothing of returns

The following problem arises in the context of private equity, which typically report "smoothed" returns (think of it as a moving average). As you can imagine, "smoothed" returns would have a much ...
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### Efficiently storing real-time intraday data in an application agnostic way

What would be the best approach to handle real-time intraday data storage? For personal research I've always imported from flat files only into memory (historical EOD), so I don't have much ...
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### 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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### Regressing NYSE returns: Lagged intercept term & efficient market hypothesis

By performing the following OLS time series regression, $y_t$ = $\beta_0$ + $\beta_1$*$y_{t-1}$ + $\beta_0$*$y_{t-1}^2$ + $\epsilon$ I cannot reject the null hypothesis that b1=b2=0. However, ...
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### Cointegration and variance of time series

Given that $X_t , Y_t$ are two cointegrated random processes, what can we say about the relationship between variance of the two increments $var(X_{t+h}-X_t)$ , $var(Y_{t+h}-Y_t)$ for a given ...
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### Need help in interpreting the Johansen co integration test

I got the following result for 10 markets Johansen cointegration in R as follows in the trace statistic, up to r<=9 is significant at 5% level. So what should be the cointegration interpretation? ...
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### remove seasonality in future contracts

very new to commodities. I have raw agriculture future data, and I need to remove the seasonality (de-seasonalize) from the data, what is the general approach ? Thanks for the help!
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### Alternative ways to understand time-varying comovement between two time-series?

I have been looking into ways to better understand how the dependencies/correlations/etc between two time series can vary over time. I first thought about using a Kalman/particle filter over a ...
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### 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 ...
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### variance ratio for pair-trading

I am using the variance ratio test to check whether my sequence is mean reverting in that test there is a parameter n, How in general I choose this n? or what is the meaning of this parameter? ...
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### 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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### 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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### What are recent important papers on credit portfolio risk modeling?

I'm interested in papers which consider mathematical models of risks of different portfolios of retail credit. This is not my area of research, so I may be misusing some terms. The idea is simple: I ...
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### Can Hurst exponent be used to characterize nonlinear dependence in time series?

It appears to me that the answer is no, because Hurst exponent measures persistence in terms of autocorrelation, which is a linear measure. So even if a time series of asset returns is driven by ...
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### rollapply with Arima model: testing for stability of coefficients

I am trying to fit an arima model on a rolling window using rollapply.My aim is to plot a graph of the evolution of the coefficient, plot the error and the standard deviation. well i encountered the ...
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### 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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### What is the best way of updating data while using Empirical Mode Decomposition to analyze

I have a question about EMD updating new data points. For an entire time series, from beginning to the end, the EMD preforms quite good using the cubic spline function. The problem happens when new ...
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### 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 ...
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### 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: ...
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### Can I do a GARCH model to forecast a time series?

I read this paper https://research.aston.ac.uk/portal/files/240393/AURA_2_unmarked_Energy_demand_and_price_forecasting_using_wavelet_transform_and_adaptive_forecasting_models.pdf the two authors ...
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### 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 ...
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### 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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### How to fit ARMA+GARCH Model In R?

I am currently working on ARMA+GARCH model using R. I am looking out for example which explain step by step explanation for fitting this model in R. I have time series which is stationary and I am ...
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### How to find the best fitting GARCH model for a portfolio composed of 3 ETFs in R?

I am doing a project for my class Financial Time Series in which I am trying to forecast my portfolio log returns using a GARCH fit. I am having a bit of trouble determining the best way to fit this ...
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### 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 ...
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### 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 ...
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### 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. ...
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### 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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### 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 ...