Questions tagged [time-series]
A temporal sequence of events measured at discrete points in time.
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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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Cumulative returns when shorting with regards to variance drag
What is the convention when calculating/analyzing daily returns for a strategy when shorting is involved? I found the following answer regarding variance drag useful in understanding why there is a ...
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Heston model using YUIMA package
I am trying to estimate a Heston model using the Yuima package, but i am in trouble.
This is my script:
...
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1
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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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Backtesting: choosing the "sample split" between in-sample and out-of-sample data
Aims:
Given approximately 11 years of historical time-series data, to determine how much of this data should be reserved for in-sample and ...
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What Do These ACF PACF Tell you? [closed]
I came across some very strange ACF, PACF chart during my research. how should these charts be interpreted? From top to bottom are chart of the original time series, the acf chart and the pacf chart
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Empirical Evidence for Support/Resistance Levels in Martingale Price Processes and Its Impact on Option Volatility Surfaces
In financial mathematics, the martingale property often serves as an essential foundation for the stochastic processes that underlie securities pricing models. According to martingale theory, the most ...
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Are ARMA-GARCH-type models suitable for monthly data?
I understand that ARMA-GARCH models and their variations are usually applied to daily time series. While I know that such models can be also estimated on monthly data, I have seen few applications in ...
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Notation in Advances in Finance ML for time interval
I was reading Prof. Lopez book Advances in Finance ML and he uses a notation I could not understand. In 4.2 (Overlapping Outcomes) he says:
In Chapter 3 we assigned a label $y_i$ to an observed ...
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Filtering a time series of returns by deleting some points
I have a time series of returns of an asset (call this $ X_t $) which I have verified to be stationary. Let's say I generate a new time series $Y_i$ which is a filtered version of $X_t$ (that is I ...
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Is there daily SPX level data going back to 1927?
While attempting to model the SPX index over time, I found a source here that purportedly has historical daily SPX data going back to 1789 which very likely seems to be backcasted since the ~500 stock ...
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Critical values of trace statistic of Johansen cointegration test for arbitrary number of I(1) variables
I am trying to find the critical values of the trace statistic Johansen cointegration test for a large number of I(1) variables. However, I cannot find these values tabulated anywhere beyond n = 12 ...
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Effect of back-transforming forecasted mean of log returns to get forecasted mean of price
When trying to forecast time series, say forecasting the level of a stock index so we can forecast the future values of an option, it tends to be helpful to analyze the log returns versus the original ...
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How does one show that the Sharpe Ratio is closely related to the t-statistic of the mean differential return?
I see it being mentioned in many places, such as here, and even here.
How should I interpret it?
Suppose I have an array of signals, I, and returns of those signals, R
Then my regression is
R = a + BI
...
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GRS test does not reject a scalar multiple of the market factor
I have been playing with the GRS test (see my R script below) in relation to Why not use a time series regression when the factor is not a return?. I generated a $10,000\times 26$ matrix of returns on ...
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GRS test does not reject a nonsense factor in place of the market factor
I have been playing with the GRS test (see my R script below) in relation to Why not use a time series regression when the factor is not a return?. I generated a $630\times 26$ matrix of returns on 25 ...
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Equivalence of exponential moving average to simple moving average
I am aware of the differences between an exponential moving average and a simple moving average, with the weights of the latter being fixed and equal for each observation in the calculation compared ...
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Why not use a time series regression when the factor is not a return?
I am trying to wrap my head around the statement that time series regression should not be used for testing a factor model when the factor is not a return. This has been mentioned in multiple posts, ...
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How to express the process of number of stock (nt) in a portfolio using ito's lemma
We have a regular self-financing portfolio $W_t$:
$$dW_t = n_t dS_t + (W_t − n_t S_t) r dt$$
Where $W_t$ is total wealth, $n_t$ is amount of stock, $S_t$ is stock price, $r$ is the risk-free rate.
And ...
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What color financial time series are there?
There is a folklore white noise hypothesis related to (and equivalent to some forms of) the efficient market hypothesis in finance -see references below. But are there some asset pairs whose return ...
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Estimate Open, High and Low prices from bid, ask and close prices
I know it's possible to efficiently estimate bid/ask spreads from OHLC prices and there are several methods to do so. However I have a data source that contains only bids, asks and close prices, no ...
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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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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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2
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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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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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