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Questions tagged [time-series]

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

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1answer
29 views

serial correlation and CUSUM results

I have the following CUSUM test resulted from autoregressive distributed lag models (ARDL). Does the CUSUM results show that the model is stable? I am a bit confused because the red line in CUSUM ...
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1answer
30 views

Calculating Ex-ante Sharpe Ratio in multi-period setting

I have built a return process $\{x_t, t = 1,\dots,T\}$ for an asset. Suppose I have generated $K$ sample paths $\{x_t^j, t=1,\dots,T\}, j=1,\dots,K$. I think of two ways to compute the Sharpe ratio. ...
3
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3answers
180 views

what are the criteria to select pairs?

I'm new to this forum, this is the first question I posted. I have many candidate pairs and I've used ADF test to make a first selection. There are more than 800 selected. The pairs are absolutely too ...
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1answer
54 views

Multivariate Markov Regime switching GARCH

I have a regression with 4 independent variables and a dependent variable. I want to implement a Regime switching GARCH model but have been unable to find a package in R,Python or Matlab. MSGARCH ...
2
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1answer
230 views

Generating surface of Kernel Density Estimates over time

I have a 1-minutely OHLC dataset indexed by time as follows: ...
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0answers
69 views

R Equilibrium FX using VEC or Behavioural Equilibrium Exchange Rate (BEER)

I dont have much experience with R. I would like to do create model for FX Equlibrium using VEC or BEER. I already know what variables I want to use in model: trade differential between UK and the ...
5
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1answer
516 views

What machine learning method is more suitable for prediction of financial time series?

I have time series data for various assets and which I transform to create various features. I have framed the problem as a classification task where I attempt to predict either a positive or negative ...
2
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1answer
74 views

Can you use GARCH-MIDAS for intraday data?

I'm working on a project to forecast volatility and I'm using intraday data (1 min). I want to include exogenous variables to the model that have daily frequency. I was wondering if GARCH-MIDAS can be ...
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1answer
53 views

Volume or Dollar bars vs. volatility normalized and demeaned financial time series

In his book - Advances in Financial Machine Learning, Marcos Lopez de Prado familiarises the reader with a number of ways of normalizing our financial time series data. Below I provide a couple of ...
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0answers
47 views

Some basic examples for Granger causality

I have two time series, X and Y. The number of observations in each time series is the same and the variables would be price(logged). The goal of my research is to analyze if one variable X follows ...
3
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1answer
54 views

Is there such a thing as resonance in economic underliers?

In physics the occurence of resonance is explained and widely understood in its linear form and subject to research in nonlinear resonance. Example for instance are resonant frequencies of objects. ...
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0answers
67 views

Autoregressive Distributed Lag Models (ARDL) results analysis

When using autoregressive distributed lag models (ARDL), I usually get a counter-intuitive result for the selected lag. For example, when examining the relationship between GDP and Foreign Direct ...
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0answers
16 views

Realised option values. (Beginner in R/ Coding****) [migrated]

I have started playing around with R and quantmod. I want to create a dataset for realised option values on the S&P500 and compare this to the Black-Scholes estimated value for a finance class ...
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1answer
2k views

How to forecast high-frequency data?

Introduction: I have seen a plenty of articles/books regarding volatility forecasting applied to high frequency data, but none of them were dedicated to forecasting the actual prices (for example bid/...
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4answers
5k views

How much data is needed to validate a short-horizon trading strategy?

Suppose one has an idea for a short-horizon trading strategy, which we will define as having an average holding period of under 1 week and a required latency between signal calculation and execution ...
5
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0answers
80 views

Feller Condition (Cox-Ingersoll-Ross) source

For the Cox-Ingersoll-Ross model $$\text{d}r_t = a(b-r_t)\text{d}t+\sigma\sqrt{r_t}\text{d}W_t$$ the condition (referred to as "Feller condition") $$2ab\geq\sigma^2$$ ensures that the solution is ...
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0answers
52 views

Standard GARCH(1,1) model with external regressors

I have a queastion how does a standard GARCH(1,1) model with external regressors in mean and variance euqations look like ? I know that standard GARCH(1,1) model without external regressors has the ...
2
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1answer
122 views

Why does computing correlation between index levels vs. percentage changes yield completely different results?

I am examining the relationship between the S&P 500 and the Industrial Production Index. Computing the correlation between these these variables yield vastly different results if expressed in ...
2
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3answers
156 views

PCA for Risk bucketing

I'm working on a project to justify the use the certain tenors (2y, 5y, 10y, 30y) for risk bucketing. I'm a little stuck after calculating the principal components. Just to describe my approach- a) ...
2
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1answer
80 views

How to compute cumulative performance of a portfolio with two equities?

I have a time series of adjusted returns for two companies, A and B. I have created a portfolio consisting of these two time series with equal weighting (sum of weights must equal 1): $w_a = w_b=0.5$...
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0answers
577 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 ...
1
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1answer
51 views

Data Sources for Timestamps of Individual Trades [duplicate]

Are there any data sources where I can get the timestamps of individual trades/transactions? I'd like them to be at the second level or even the millisecond/nanosecond level. Ideally, the trades would ...
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2answers
115 views

Do you optimise models on bootstrapped time series?

As Quants, we soon learn to optimise models, by fitting them to historical time series, e.g. the historical daily returns of some stock. But the historical series of daily returns is just one ...
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0answers
122 views

What is the best source to get 10 milliseconds time-series data for numerical computation?

I am working with 4th order Runge-Kutta method to compute a second order differential equation. For the best accuracy, I need a 10 milliseconds ohlcv time-series data. I know that I can build it ...
1
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1answer
63 views

Calculate New Portfolio Weights Given Today's Returns

I'm looking for a formula to recalculate my portfolio's weights at the end of time $T$, given a vector of the asset weights at $T$ and a vector of returns at $T$. For example: ...
6
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1answer
124 views

How to perform cross-sectional asset pricing regression?

I'm wondering is that possible to get insignificant beta estimates in the time-series context, but highly significant risk premium associated with that beta in the cross-sectional regression? Any ...
3
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0answers
88 views

Detecting leading stocks using lag correlation

I am working on a project to find leading stocks in a stock market by using lag correlation. Say I want to compare 2 stocks, X and Y, and I have the time series of stock prices. Assume that the ...
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0answers
29 views

General to specific approach to modelling

I am trying to find the relationship of stock indices across the world. This has been done by the literature, however, I am wondering about the methods chosen. I have decided to go with what I think ...
12
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4answers
902 views

Why (most) quants think that the risk-neutral measure should not be used for financial forecasting?

In posts regarding the $\mathbb{P}$ vs $\mathbb{Q}$ debate (see 1, 2, 3 or 4), most answers seem to conclude that historical-based methods are better suited than risk-neutral models for financial ...
2
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2answers
151 views

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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0answers
56 views

How does the FED calculate SAAR for GDP?

In looking at the Fed's GDP growth rate data, it looks like the fed uses a different calculation for calculating annualized growth rate than the typical annualized rate of change. Does anyone have any ...
2
votes
1answer
60 views

Monte Carlo simulations of stock price percentage change rather than stock price

Say we have a stock price time series $S_k$. We can do monte carlo simulations on the stock price to make predictions about future prices (e.g. through Geometric Brownian Motion SDE's). Does it make ...
4
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1answer
344 views

Application of ACD models

I have been playing around with autoregressive conditional duration (ACD) models and I have a nicely working R based implementation using real high frequency data (trades only data). However, what's ...
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0answers
68 views

Rolling forecast using GARCH model

EDIT This is not a duplicate of my original question linked, since I have since overcome that problem and have posted an answer. Since solving the previous problem, I have run into the problem ...
2
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0answers
242 views

RiskMetrics VAR calculations and conditional distribution of sum of log returns

According to Tsay's book in Chapter 7, for the Risk Metrics model: A nice property of such a special random-walk IGARCH model is that the conditional distribution of a multiperiod return is ...
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3answers
182 views

Reverse chronological time-series / inverse time-series

If a timeseries follows a BM, is it true that the inverse ts and reverse chronological ts is also a BM? What if the ts exhibits mean reversion tendencies? Would these tendencies become a momentum ...
2
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1answer
137 views

Discretizing a Continuous Time Stochastic Volatility Model

How does the discrete time stochastic volatility model arise from the continuous time one? Also, forgive me for cross-posting. I have the following continuous time SDE for a stochastic volatility ...
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8answers
35k views

How to check if a timeseries is stationary?

I'm using KPSS Method to check if the series is stationary, but I would also like to use another test to confirm if the series is stationary or not, what method coudl I use?
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0answers
101 views

Poor results forecasting stock price volatility using Python's GARCH model

As far as I understand, forecasting stock price volatility should be more achievable than forecasting absolute prices or returns. It seems as though GARCH models are the traditional and most widely ...
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1answer
21 views

Auto-covariance function of station time series

How to show that for any stationary time series its auto-covariance function is symmetric about the origin, that is $\gamma_{k}=\gamma_{-k}$ where, $\gamma_k=cov(z_t,z_{t-k})$
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1answer
2k views

ARMA+GARCH prediction with package rugarch (R)

I am analyzing FTSE 100 series, from 2007-01-01 to 2010-12-31 (university exam homework). I have to use the data 'til 2010-11-30 as sample, and the remaining (23) observations as in-sample forecast (...
7
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2answers
850 views

Fitting Copula and Simulation

I would greatly appreciate any insights into the problem described below, regarding using the data obtained from applying the functions of the rugarch package ...
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0answers
31 views

Johansen Cointegration Test in R

I know its probably been asked bevor but i just don't get it. I have 2 values (Oil and corn price) and i want to check if they are cointegrated. Bevor that, i have tested if they really are non ...
8
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1answer
317 views

Block bootstrap to synthesize asset prices

I have a few basic questions on block bootstrapping on a financial time series ('TS'). Assuming my trade universe consists of 10 stocks, I would like to create a set of synthetic prices for all 10 ...
6
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2answers
244 views

Does predictability in a VAR process imply mean reversion or momentum?

There seems to be some disagreement in the literature about this. Define predicability of a stationary series to be $\sigma^2_{t-1} / \sigma^2_t$ Finding mean reverting portfolios using canonical ...
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0answers
5k views

How to use statsmodels' Granger causality test to measure the lag between two time series?

I am using the Granger causality test to measure the lag between pairs of time series where it is already apparent that one is following the other. So I am not expecting this test to tell me whether ...
4
votes
2answers
160 views

Lagged Residual as Independent Variable

I am building a factor model to estimate future equity returns. I'd like to include an autoregressive residual term in this model. I'd like to have yesterday's error (the difference between yesterday'...
10
votes
1answer
12k 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 ...
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8answers
24k views

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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0answers
21 views

Sample distribution of cross-sectional statistics of returns

Currently doing an application of VaR on sample of industry portfolios in the US. I have a matrix of $n$ industry portfolios with $m$ time-series observations. I calculate cross-sectionally (for each ...