Questions tagged [time-series]
A temporal sequence of events measured at discrete points in time.
817 questions
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Were internet related companies at the height of the dot-com bubble really overvalued from a long term persepective?
TL DR: What was the long-term return of the original Dow Jones Internet Composite Index constituents (no rebalancing but reinvesting dividends) from 1999/2000 to today?
This question is motivated by ...
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Volatility Estimation and forecasting for Ultra-High frequency data
I am unable find resources for Volatility Estimation in ultra-high frequency settings. I am aware of HAR-RV and it's counterparts. These models seem to estimate daily volatility using high-frequency ...
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Finding an invariant/stationary quantity in prediction markets?
I am looking at trade data for specific outcome of an event on a prediction market (Kalshi, but this could apply to others) and trying to model market microstructure effects such as inferring mid ...
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Process for Removing Error/Incorrect Cryptocurrency Trades
I have many time series of cryptocurrency pair trade data from Kraken (available for batch download here) with some that are very long and others that are very short.
I am attempting to build a basic ...
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Is GARCH assumption on constant drift wrong in log space?
GARCH assumes constant drift $\mu$ - this imply $E[e^r]$ won't be constant and jump wildly. And it contradicts the reality, for stock prices $E[S_{t}/S_{t-1}]=E[e^r]$ doesn't jump with each time step.
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Extracting seasonal factor of CPI from BLS
I want to extract the seasonal factor to get the seasonal adjustments of each CPI fixing in order to create a seasonally-adjusted CPI series.
The BLS publishes the seasonal-factor on their website at ...
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Kolmogorov Smirnov test on derivatives market data
If I want to test market data( say volatility or liquidity spreads) for swaptions in a particular currency (eg. USD) for 2 different periods of time and ascertain whether their distribution is more or ...
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Does Computing Log Returns as $\log(x_t / x_{t-1})$ Introduce Information Leakage in Time Series Forecasting?
I have tick-level data for a single trading day of a specific contract and aim to conduct time series analysis on it. The mid-price at each tick is computed as $MidPrice=0.5×(Ask_1 +Bid_1)$. The data ...
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How does Ang et al. (2006) aggregate daily‐frequency FVIX returns into a monthly FVIX factor?
I’m trying to replicate Ang et al. (2006) “The Cross‐Section of Volatility and Expected Returns” where they construct a daily‐frequency volatility‐mimicking factor FVIX via
$\Delta VIX_t = c + b'X_t +...
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How can I robustly detect dynamic support and resistance levels programmatically in Python?
I am working on a Python project to programmatically detect dynamic support and resistance levels in historical price data, particularly for forex instruments such as EUR/USD. My primary goal is to ...
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Metric for volatility time series similarity - European swaptions
I'm trying to estimate the volatility surface of illiquid swaptions (say CHF) given hourly data (atm vol, skew, for different strikes) of other liquid swaptions (EUR, USD, etc.). Having the underlying ...
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Generating returns from another variable; is this approach sound?
Assume I have some financial return time series for every year, $y_{1y}, y_{2y}, y_{3y},...$.
I also have a financial return time series for every month, $x_{1m}, x_{2m}, ...$
We can of course ...
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Size of blocks in Block Bootstrapping of returns
I am working on long term portfolio allocations. With larger investment horizon like 5-10 years, 20 years of data that I have is not enough. So I decided to do some bootstrapping to generate some ...
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Python implementation of the BNS (Barndorff-Nielsen & Shephard) jump test
Is there a reliable implementation in python of the BNS jump test available?
Barndorff-Nielsen & Shephard (2006) "Econometrics of Testing for Jumps in Financial Economics Using Bipower ...
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What is the precise meaning of signal and noise in finance
A lot of people working in the finance industry are saying that what makes finance hard is that the signal to noise ratio (SNR) is extremely low.
I don't get what is precisely meant by that. How are ...
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How does clustering affect volatility and correlations differently?
In Betting Against Beta - Frazzini & Pedersen (2014, JFE), the authors state that correlations appear to move more slowly than volatilities, which implies that the clustering phenomenon affects ...
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Estimating asset correlation coefficient using CDS data and default probability
I have a CDS dataset with several instruments belonging to different sectors, par mid spread values and probability of default (recorded daily 2009-2023).
I want to take all the firms within one ...
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Shrinkage estimators outside MVO? Sample mean or James-Stein estimator?
Generic question: Are there any uses of Shrinkage estimators, such as James-Stein estimator for mean or Ledoit-Wolf estimator for covariance matrix outside mean-variance optimization (MVO) framework? ...
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How do you avoid noise in daily averages?
Let's say I am computing daily implied volatilities of a range of options and averaging them.
On any given day, the range of options I have available may be different from the range of options I had ...
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non-parametric methods to generate high-probability sample sequences from an existing univariate continuous time series
If I have an existing time series (of univariate continuous values), how can I generate a "realistic" sequence of future samples? Assume the samples of the existing time series are fairly ...
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How to deal with vectors that are orthogonal to cointegration vectors?
In the context of cointegration, when we have the long-term equilibrium defined as $\mathbf{\beta}\mathbf{p} = \eta$, introducing an arbitrary vector $\mathbf{v}$ that is orthogonal to $\beta$ doesn't ...
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Theoretical pricing models as time to expiry approaches
I was reading Option pricing and volatility by Sheldon Natenburg and in chapter 5 he says the following:
“Although traders typically express time to expiration in days, a trader may want to use a ...
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Train-test split configuration on timeseries data for machine learning optimization
I have a strategy (running in the seconds scale) which parameters I would like to optimize. The thing is I'm relatively new to financial machine learning and I'm not quite sure how to split the data ...
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Continuation of GARCH(1,1) without data
Please be easy on me since quant finance is not my strength.
I have the following Python code that models volatility under GARCH(1,1) for the S&P500:
...
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Temporal dependencies in time-series
To my knowledge, the algorithms that require stationary input can't capture temporal dependencies. This is inherent due to the fact that the input features must be stationary, thus things like trends, ...
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128
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Financial Time-Series: Stochastic or Dynamic?
I have learned how some methods of constructing predictive models of financial time-series involves assumptions of stochasticity. For example, reinforcement learning utilizes the Markov Decision ...
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167
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Weak Stationarity for Neural Network Input?
I am taking a course that detailed that input data into neural networks should be at least weakly predictive and weakly stationary (stable mean).
Does this principle apply to other ML models like tree-...
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744
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Cross sectional momentum vs time series momentum
What are the advantages/disadvantages of creating quantitative strategies using cross sectional momentum vs time series momentum?
From my perspective, time series momentum is a better indicator of ...
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Mean-reversion strategy with overnight gaps
When using stocks as time series data, it is common to encounter large overnight gaps, sometimes because of earnings, other times because of press releases. So, how to correctly account for this ...
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Residual Function
In a time series with OLS regression curve Y-hat (rolling linear regression), and with n=20, what can I say about this transformation? This formula is similar to a differential dY/dt minus an integral ...
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Reducing possible models count for calibration in ARFIMA-GARCH models
I have the question connected with ARFIMA-GARCH models.
I have a time series for which I want to calibrate best model (p,q)-(P, Q) (via BIC) with $ p,q <= 4, P,Q <=2$. GARCH part can be "...
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How do I understand and calculate daily log returns? [closed]
I'm relatively new to the Quantitative community. I was trying to work with a dataset where I want to calculate daily log returns.
My dataset consists of multiple timestamps for each business day and ...
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Is it possible to discretize OU with a more general AR(p) / ARMA (p,q) models?
The discrete analogue of an OU process is a simple AR(1) model. More general AR(p) or ARMA(p,q) models can also be regarded as discrete analogues of an OU process? If so, which coefficients describe ...
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Robust or Stochastic Optimization Approach for Maximizing Profit with Limited Price Information
I am tackling a linear maximization problem where I need to select the optimal product among several options over a series of weeks, given certain constraints, in order to maximize future profit. The ...
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Analyzing portfolio returns using Fama-French Factors
Here is my problem - I have monthly returns from few portfolios. I also have monthly return from benchmark portfolio. I downloaded F-F 5 factor daily data. Also downloaded Momentum data. Converted ...
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Why does AR(1) model with a small coefficient exhibit faster mean-reversion than one with a greater coefficient (when |$\beta$|<1)? [closed]
Suppose we have two mean-reverting AR(1) models, given by
$$X_{t}=\beta X_{t-1}+\epsilon_t,$$
where $|\beta|<1$.
How fast series reverts to its mean is determined by the coefficient $\beta$. As far ...
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How to calculate the spot variance from the TSRV (Two-Scale Realized variance)
If the TSRV is given by:
$$TSRV = \frac{1}{K} \sum_{i=K}^{n} (S_i - S_{i-K})^2 - \frac{\bar{n}}{n}\sum_{i=1}^n (S_i - S_{i-1})^2 $$
where $\bar{n} = \frac{n - K + 1}{K}$, with $n$ is the number of ...
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Average correlations of stock returns
Say I had a pool of companies (specifically the Latin American Countries). The task was to work out the 'Correlation coefficient between the returns of any 2 companies selected from "different ...
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Does cointegration test of exogenous variable with Y variable make sense when doing ARIMAX/SARIMAX?
The cointegration test between two time series variable is generally relevant from my understanding when you are performing a regression model. In terms of ARIMA model the approach is straightforward ...
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In copula modeling for time series data, why do we need to fit ARIMA/GARCH and then work on standardized residulas.?
I have read that for standard copula modeling, you can get empirical cdf of data and use it for copulas. But for time series data, we must first fit ARIMA/GARCH, get standardized residuals, and only ...
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Can the white noise in multivariate GARCH have different distributions?
I have two datasets of log returns, one is clearly normal while the other is t-distributed. I want to fit these with a mutlivariate GARCH model. A multivariate GARCH model is defined as
$$\mathbf{r}_t=...
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How should I create a Risk measurement Variable?
I have clients who take loans (Advances) weekly. The way that they repay the advance is after 3 weeks when their goods are sold, using the sales proceeds of the goods. But if the goods don't sell for ...
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the pre-averaging function in Jacod et al
In the paper of jacod et al the authors used the pre-averaging function to deal with microstructure noise. They suggest the easiest function which is $$\bar{Z_i} = \frac{1}{kn} \left( \sum_{j=kn/2}^{...
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Highest resolution of stock data?
Out of curiosity, I'm wondering what the highest resolution of stock data there is out there. Is there stock trading data for every nanosecond, picosecond, or even lower? And how is this limit ...
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How does historical data from bloomberg interact with timezones?
I'm running analysis on multiple countries bonds over a long stretch of time. I was asked about what determines the date of data in Bloomberg, ex: December 31st in NY will be January 1st in Japan and ...
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Neural network time series prediction tool [closed]
What are some of the state of the art time series prediction tool with neural network?
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Understanding the Intersection of "Advances in Financial Machine Learning" and "Asset Pricing in Stock Market Prediction"
I have been reading "Advances in Financial Machine Learning" by Marcos Lopez de Prado and "Machine Learning in Asset Pricing" by Stefan Nagel, and I noticed that there seems to be ...
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Value at Risk for Portfolio of Futures
I'm working in a very small commodity trading company. They are not used to excel at all, so i built their trading sheet to follow open positions & past positions.
Now they asked me to calculate ...
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Dummy time series to be considered
When estimating various risk measure like VaR a good amount of times series data is required. Somethings it happens that sufficient data may not available of ...
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