Questions tagged [time-series]
A temporal sequence of events measured at discrete points in time.
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backtesting equities with various forecast horizons [closed]
I am looking for heuristics for the following - something simple and not necessarily optimal.
Let's say I have two types of daily frequency forecasts on stock returns:
a short term price reversal ...
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Testing predictability of a proposed predictor in case of multiple returns
Say I have a T daily observations for the last ten years on a new predictor $x_t$ which I think is a predictor of the expected weekly return on the stock market, $r_{t,t+5} = r_{t+1}+...+r_{t+5}$, ...
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Proving equivalent half life of SMA vs EWMA
I have recently read in a paper that the 25 day SMA and 36 day EWMA are equivalent for comparison in tests due to their half lives. The half life of the 25 day SMA is obviously 12.5 days and I ...
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What is the process for using OLS on time series models (HAR like)
I am reading about HAR models for realised variance and they all seem to use WLS or OLS to calculate the parameters. Now I understand how that works if you just use say the 10 years of AAPL intraday ...
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Understanding Hamilton's formula for present value [closed]
I am a mathematician with almost no knowledge in economy and econometrics trying to read Hamilton's Time series analysis.
At the very beginning of the book, Hamilton considers an order-$1$ difference ...
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Applications of a certain type of stochastic processes in quantitative finance [duplicate]
A compound Poisson random vector $Y$ is well defined in this site in wikipidia.
Nothing prevents me from compound strictly stationary stochastic processes instead of compound random vectors. The ...
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Outliers in each time series (T1 and T2), but not in the summed time series (T=T1+T2), what is the proper way to deal with such cases for T?
I have two time series (T1 and T2), each have several 5sigma+ outliers, but are of opposite sign and unequal magnitude. The resulting series T from summing the original two time series (T = T1 + T2) ...
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Compute monthly realized variance from daily data
I am confused about the correct formula to compute monthly realized variance from daily data. What is the first sigma in the picture: sum or average? I mean, after subtracting each observation from ...
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How to retrieve several years of Credit combined score for a set of companies via Eikon, to construct a panel regression against ESG score?
How to retrieve several years of Credit combined rating score for a set of companies via Thomson Reuters Eikon, to construct a panel regression against their ESG score?
I am trying to get the CCR for ...
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combining forecasts at different time horizons
I define a prediction of return of an asset as the following: at time $t=0$, I use my data and output that I expect the asset to make the following returns (in expected value) in the next n intervals $...
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Assessing the GARCH model out-of-time
I have fitted two competing GARCH models, one GARCH(1,2) model and another EGARCH(1,1,1) both with t-distributed errors, on the ...
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Multistep ahead forecasts in GARCH equations
If my one step ahead forecasts from GARCH(1,1)-X are:
\begin{equation}
\hat{h}_{t+1} = \hat{\alpha}_0 + \hat{\alpha}_1 \hat{u}^2_t + \hat{\beta}_1 \hat{h}_t + \hat{\psi} X_t
\end{equation}
Where ...
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Simulate correlated credit spread
I want to simulate a credit spread index which is negatively correlated to a given random walk of a stock index. They should be correlated in such a way that larger than average stock growth tend to ...
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What is the meaning of the following mathematical equations? [closed]
Let's say that we have a discrete probability distribution, where
$$ x_i $$ represents each of the possible outcomes (discrete set of possible outcomes), and
$$ L $$ represents the expected value we ...
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Volatility Modelling negative GJR-GARCH-X coefficient
I have estimated GARCH and GJR-GARCH with several exogenous variables. Some of the exogenous variables have negative coefficients that are statistically significant. For instance, I can write my GJR-...
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how to estimate Geometric Brownian Motion parameters on long timeseries [closed]
I'm working on a 50-years financial timeseries and I would like to simulate GBM paths from it.
The first thing I'm supposed to do is to estimate the drift $\mu$ and the volatility $\sigma$ parameters.
...
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Is intra-forecast-horizon rebalancing suboptimal?
Suppose that I have forward 1-month forecasts of returns that are updated daily. Is it suboptimal to rebalance more frequently than 1-month (e.g., daily or weekly)? Theoretically, if I forecast the ...
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Interpretation of Chu-Stinchcombe-White CUSUM Test results
Context:
I am new to quant finance. I am doing some structural break analysis on a future price time series. I applied the Chu-Stinchcombe-White CUSUM Test from Chap 17 (Advances in Financial Machine ...
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Volatility forecast for 5-minute frequency data
I have high frequency data for financial stocks (5-minute periodicity) and I want to forecast volatility.
I'm familiarized with the usual ARCH/GARCH models and their variants for daily data but after ...
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Can we spot informed trading from market prices?
Is there any consensus on what is the price behavior in presence of informed trading? Can we observe in retrospect any anomaly in the time series of prices of realized transactions, or transformations ...
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Is not handling irregularity (unevenly spaced time intervals) in stock market intra-day data ok?
I read papers and it seems not doing anything to unevenly spaced time series is the implicit common sense (apart from routine preprocessing, which has nothing to do with time interval handling) for ...
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Should we include constant in linear regression in pairs trading?
Should we include constant in linear regression while calculating hedge ratio for pairs trading strategy?
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Cointegration between crypto markets
I'm having an hard time understanding how cointegration works. Basically i'm trying to find cointegrated pairs in the crypto market, so i do the following:
Get OHLC data for the two markets (i'm ...
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How to implement rolling granger causality
I am investigating two time series where the first is the daily closing stock price changes and the other is the daily changes in the PCE index.
I want to investigate how much the PCE index explains ...
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Reasons for negative autocorrelation of forward prices
I am working on each trade day's forward prices of gasoline. I noticed that the autocorrelation at lag 6 is significantly negative. I know how to interpret negative autocorrelation in a statistical ...
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Regression taking in account size of earnings surprises
I'm trying to regress earnings surprises on variable x. However, absolute earnings surprises are mostly influenced by company total earnings and the number of shares outstanding. So I can't just use ...
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Forecasting VIX with GARCH(1,1)
Aim: Forecast VIX using GARCH(1,1)
Reason: I want to be able to forecast VIX on several horizons, in order to be able to forecast the SP500 index through linear regression.
Tools used: Python, ...
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Application of Gramian Angular Field to financial series?
I found this method to represent time series to improve performance of some ML models, any thoughts about this method? some applications or use cases in financial markets?
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Daily realized volatility and true daily volatility
Can someone help if I am thinking correctly?
If $R(t,i)$ is the i'th log-return for $i = 1\ldots,M$ of day $t$ for $t = 1\ldots,T$.
Can I assume that the daily realized volatility (denoted $RV(t)$) is ...
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Techniques for proxying time series / stock prices
What are some good techniques for proxying time series?
My purpose is for risk management / modelling and I would like proxy to missing series.
Given that I also have to account for volatility, ...
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Inconsistency between simulation and the probability of a "stock" hitting take profit before stop loss
Let's assume a stock at time $t$ is worth $X(t)$. If the returns of $X(t)$ are i.i.d. and normally distributed,the probability of $X(t)$ hitting a value $H>X(t)$ before $L<X(t)$ is $\frac{H-X(t)}...
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What are common ways to realistically simulate the stock market using historical market data?
I am currently using the FinRL library to try to automate Trading using Reinforcement Learning. However, I wanted to understand how FinRL simulates the stock market using historical data. I read here ...
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I.I.D log returns. What about their square?
If one assumes the underlying return process is I.I.D, is there a solution to the question of the autocorrelation of squared returns?
Thanks.
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Looking for options to visualize large market timeseries data
I have a large dataset that includes my strategy back-test run data. The dataset columns include candle date, close price and many strategy related data.
I’ve built a Mathplotlib visualization for my ...
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Filling in between data in finance
I'm trying to create a model on how different factors influence a particular asset. For some of these factors, like inflation, for example, I have monthly data, while for others, like exchange rates, ...
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How to determine which realized volatility estimator should be used?
There are so many realized measure have been invented in the past years like TSRV, MSRV, KRVTH, KRVC... But how to choose them in practice?
I know we cannot find the "estimation error" of ...
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2-day ahead prediction of value at risk with GARCH(1,1) in R
Let's say I have a 10 year dataset of Tesla (example) and I am taking the percentage change of lag 2:
...
user57440
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Copula Models for Asset Returns
I'm learning about copulas and their applications in finance. When used to assess the dependence structure between two indices for example, can the copula models be estimated directly on the log-...
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How to find out the dates of the different financial quarters (e.g. Q1, Q2, etc.)?
Question: Is there any resource I can use to find a list of dates which constitute the start and end of the 'financial' quarters (e.g. Q1, ..., Q4) for the years 2006 onwards?
I know sometimes they ...
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How to compute the combined probability of loss for 2 time series (consisting of historical stock prices)?
May I please ask the community's support with the following problem?
I have 2 time series, with approximately 1000 observations each (same number of observations for both). They represent the daily ...
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PCA on portfolio depending on multiple time series
There is extensive documentation about PCA on specific time series (for example the UK yield curve). When you have a portfolio which only depends on the change of the UK yield curve then a PCA on the ...
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Simulating the Value-at-Risk with $t$ distributed returns
I want to understand how the value at risk and the simulating the VaR with simple Monte Carlo method. But I want just a confirmation and are welcome any comments, since I don't have the full picture ...
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What does M^L represent over this Sigma?
This is throwing me for a loop. in regards to this passage, does the M^L represent to perform this sum over every "overlapping window" individually? Would this mean "M symbols" are ...
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Conditional Value at Risk using GARCH models
In this paper:
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwjSlIHYnMj1AhWqNOwKHZfHDhkQFnoECAkQAQ&url=https%3A%2F%2Fwww.mdpi.com%2F2076-3387%2F9%...
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Time series data for probability of default (or credit ratings)
I'm currently investigating potential correlations among ESG ratings and credit ratings; more in particular, i'm trying to understand whether such correlation evolved during the last 20 (?) years, and ...
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Why is the moving average called that way? [closed]
I am a beginner in time-series analysis.
The moving average model uses past errors*parameter, so why is it called a moving average model?
It seems counter-intuitive to me. The Auto-Regressive model ...
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How to deal with negative intercept terms on GJR-GARCH(1,1) model?
Recently, I have been studying the relationship between COVID-19 and stock returns using a GJR form of threshold ARCH model. However, I got some unusual estimation results I can't figure out whether ...
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Does it make any sense to normalize returns?
I have been going through a course for Time Series Analysis. First we learned to make returns from a time-series of stock index by (Xt - Xt-1)/Xt-1 .
This makes the series stationary, which means we ...
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Persistence and stationarity together in volatility analysis
I am trying to analyse a time series. I want to get only quantitative results (so, I'm excluding things like "looking at this plot we can note..." or "as you can see in the chart ...&...
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Memory effect of log returns of S&P 500
I am trying to reproduce the analysis discussed in https://arxiv.org/pdf/cond-mat/9905305.pdf where they use high-frequency data (1-minute frequency) of S&P500 from 1984 to 1996. In particular, ...
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