Questions tagged [forecasting]
The forecasting tag has no usage guidance.
247
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Cross-day realized volatility
I've been looking for papers on volatility forecast, and most of them focus either on daily volatility (often using daily returns to access predictions for monthly volatility). Others focus on ...
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What Quantitative Methods Best Predict Silver Prices Based on Macroeconomic Indicators?
I'm seeking guidance on developing a robust quantitative model to predict silver prices using macroeconomic indicators. How can I incorporate variables like GDP growth, inflation rates, and monetary ...
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Time-varying Normal copulas, generating residulas with parameters
I am working with time-varying normal copulas who equation is given by
The dynamic equation of dependence parameter $\rho$ is :
Where
$u_1=F_1 (ε_{1,t} )$ and $u_2=F_2 (ε_{2,t} ) $
I ...
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GARCH-MIDAS model for forecasting volatility?
I had a problem when I just estimated the GARCH-MIDAS model on Eviews: I found only the MIDAS model. Can I estimate the GARCH(1,1) model and MIDAS separately, and then multiply them to have GARCH-...
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Profitability on Value at Risk forecasting
I'm conducting a research related to Value at Risk forecasting using volatility models like GARCH and others. My predictions are turning out quite well with some models. Is there a way to capitalize ...
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What are best models to predict mean-reverting processes?
Surprisingly to me, I could not find any paper in the literature that discusses methods to predict a mean-reverting process. What are the best models to predict mean-reverting processes? Would also ...
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Predictive Forecast (Close, 14)
I've been following an asset wherein a "R-squared predictive forecast (close, 14)" is posted online each day. On some days, this figure is extremely high, like .92.
Exactly what is the ...
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Variance decomposition in the frequency domain
I have done a time-domain decomposition of a generalized forecast error variance from a VAR model of exchange rates and inflation rates. The data are monthly. I am not very adept at doing the ...
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What are state-of-the-art methods for forecasting of rates and volatilities?
Usually forecasting is based on a model for the evolution of a value $x(t)$ based on some parameters ${\beta}$ that can then be estimated using various statistical means.
For yield curves and ...
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Macro-economic model to predict Copper Prices
I'm currently developing a model based on the current macroeconomic scenario in the world to predict the price of copper 1, 2 and 3 months ahead. That's my idea and I'd like to know what are your ...
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Boosting models for algo trading
I’m currently working on a xgboost model to predict the price change above or below a given percentage between a candle’s open price and the next candle’s close price. I use a wide range of features, ...
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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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Return forecasting for portfolio optimization
I have some questions related to forecasting returns and how it's used to generate the inputs for portfolio optimization.
First, I want to understand why factor models such as FF- 3-factor model are ...
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Portfolio construction in the real world [closed]
Good day. I am looking to understand how the portfolio construction process is actually done in the industry. Now, I do not know if there are too many resources on how things are currently being done (...
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Backtesting on factor model residual returns
I've heard in quantitative equity strategies, people backtest signals on residual returns. How does this work in practice? Do people find signals that forecast residual returns and then run the full ...
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Forecasting forward curve using Gaussian Process Regression
I have daily closing prices of crude oil monthly contracts up to 36 months. Some contracts are not very liquid so there are missing prices at random. I stitched together contracts to make them rolling ...
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Recommended books/resources for IRRBB risk metrics calculation
Any recommendations for books/resources/videos/on-demand courses for in-depth IRRBB-related risk metrics calculation etc?
Yield Curve Risk, Basis Risk, Repricing Risk, Optionality Risk, Value at Risk, ...
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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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Is a volatility forecast essentially a delta forecast in vanilla European options?
As the title suggests.
I want to understand why delta hedging is done. I'd like to illustrate with an example:
Say you have 7 dte option chain with 15.8% IV ATM straddle on an underlying of spot 100.
...
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Is my time horizon for GARCH(1,1)/ARCH(1)/EGARCH(1,1) reasonable?
I am trying to learn about volatility forecasting using three models: ARCH(1), GARCH(1, 1) and EGARCH(1, 1) using python. I wanted to know if my general procedure is correct, and specifically if my ...
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Optimal Input and Target Variables for Forecasting Using a Deep Neural Network on Daily Stock/Index Data [closed]
What is the optimal input and target variables for forecasting with a deep neural network on daily stock/index data? More specifically I’m training a temporal convolutional network, but a more general ...
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Appropriate way to combine alternative volatility estimates
I have a number of different annualized realized volatility estimates (for the same point in time) that I'd like to combine. Is a simple average over these appropriate? Or should I do this in the ...
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473
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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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Move from risk-neutral probability to historical probability
I am working on a density forecasting project using options. Using the Breeden-Litzenberger formula it is possible to find the implied density at maturity under the risk neutral probability of an ...
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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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Inflation in wealth forecast [closed]
I am building a model to simulate people's wealth in the next years.
Say Mr X has a portfolio with an expected return of 3% (annual). From this I can simulate the return of his portfolio in the next ...
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861
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How to forecast volatility using gamma exposure index?
Brainstorming this afternoon.
GEX is the gamma exposure index (https://squeezemetrics.com/monitor/static/guide.pdf). It's the sum of gamma exposure for call and put.
Using IV, strike and BDS you can ...
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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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589
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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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"Better" forecasts lead to worse asset allocation performance
Short version
If you're trying to produce an asset allocation system, it feels pretty natural to split it into an estimation component that forecasts asset means and covariance, and a weighting ...
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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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Good performance of naive forecasting in efficient markets
I am doing spot price forecasting for a market, and so far, the naive forecasting model, which forecasts with the last observed prices, is the best forecasting model. I know that it might be because ...
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How to calculate the term structure of an index that doesn’t have futures
I would like to calculate the term structure of the VVIX index.
Only way I have found so far is forecasting historical prices N months out.
Any other idea?
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Suggestion on the models to estimate public indeces future returns
I would like to to estimate the future returns of some public indeces. I have several of them so it is a multivariate problem.
The series are quarterly and the estimation should be of at least 15-20 ...
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On a relative level how do you value single name volatility? [closed]
Let's say I am looking to price AAPL 30 day volatility on a relative level.
My first thought would be to take SPY vols and multiply it by AAPL's beta.
But this leaves out the volatility caused by the ...
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Building multivariate model to predict trading volumes
I am building a multivariate statistical model to forecast the trading volume of the S&P 500 stock based on its previous values and on other covariates. Being new to finance, I am having problems ...
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Good (non-random walk) financial time series to perform forecasting on
I would like to start with a brief caveat, namely that I am by no means a domain expert in financial markets. Therefore the question I am asking may sound silly to a practitioner but I am asking it ...
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Presence of underestimation bias in consensus earnings predictions
I am working on a financial data that entails forecasted revenue a company generates over a fiscal quarter and the actual revenue for that quarter.
...
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456
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Are there any public implementations of realized kernels? (preferably in Python)
looking to implement a realized kernel model to forecast realized variance of around ~140 equities and indices in Python given order book data.
I have read "Realised Kernels in Practice: Trades ...
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Trying to recreate results from a research paper on HMM and Kolmogorov-Smirnov Test for forecasting regime switching on SP500
I am trying to recreate this research: Regime-Switching Factor Investing with Hidden Markov Models,
by Matthew Wang, Yi-Hong Lin and Ilya Mikhelson
https://www.mdpi.com/1911-8074/13/12/311/htm
My ...
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How to create a local price index?
I have a set of real estate data; historic sales price, square meters, location (latitude, longitude), neighbourhood, city, sold date and bunch of other features. I have used a boosting model to ...
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Perfect in-sample size for out-sampling volatility prediction (EGARCH(1,1)
I have a few questions regarding in-sample size for volatility forecasting in EGARCH(1,1). I'm currently sitting with a dataset consisting of 1387 trading days of the S&P-500 index. I would like ...
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Trading strategy for a misspecified density
I am trying to implement a strategy that exploits potential misspecifications in density predictions (e.g.: long states with too-low probability; short states with too-high probability).
In particular,...
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Price Prediction Intervals from Forecasted Returns (ARIMA)
I have successfully fit an ARIMA model to a time series of the daily returns of power futures prices. The question I have is: How can I create a prediction interval for the prices? Or, alternatively, ...
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Is there a HAR that deals with the leverage effect?
The EGARCH is a special GARCH model that treats the leverage effect of the volatility. The HARV does not make a distinction between negative and positive returns. Is there a special HARV that deals ...
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How do I deal with nonexistant data in a time series with an irregular frequency?
I am trying to do some time series analysis on the margin resulting from three specific commodity futures contracts and ultimately forecast the margin. The margin is calculated as M = F1 + F2 - F3. I ...
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Using geometric brownian motion for stock price forecasting [closed]
I am doing a dissertation in finance on a maths degree. I wanted to forecast stock prices using artifcial neural networks but none of my tutors are able to supervise so I'm having to do something else....
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Forecasts for the S&P 500?
Would anyone know of any monthly forecasts for the S&P 500, historical over a long time periods.
Websites like estimize provide forecasts of all sorts of things likes stocks and the balance of ...
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How do you simulate returns for a portfolio when you have Lumpsum + Monthly investments (SIP) in place?
I'm trying to simulate portfolio returns using Norm.inv function in excel.
Inputs to the formula: Prob= Rand, Std dev= Historical, Mean= 5 year historical average.
Its easy to do this when you're ...
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Fitting a Spread into ARIMA AR(1) process
I'm a newbie to econometrics. I've simply ran a regression and have coefficient values of the variables. I'm running a regression for a crypto data, and I've gotten the Spread of the variables. To ...
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