Questions tagged [forecasting]
The forecasting tag has no usage guidance.
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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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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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$n$-day ahead forecast for asymmetric DCC-GARCH model
I am working on forecasting covariances with the use of MGARCH models. I was wondering if anyone knows how to implement a n-day ahead forecast of the aDCC (asymmetric DCC) model in R. The ...
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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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Why is the expected value of bias statistic one?
I have been reading about factor models recently. One of the ways in which the developer of these models (Barra/ Axioma) measure the accuracy of their models is by calculating the bias statistic for ...
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Find out the effective monthly discount rate for a 10% annual discount rate
First time posting. Apologies in advance if this is not the right question for this forum. If it is, please let me know if I should reformat this in a particular way. If it isn't, would it be more ...
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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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How To Account For Inflation Over Historical Data
I believe inflation is greatly affecting my sample data, even when using percent-changes for movements. I have read this post, which recommends the formula ((Current-Base Year CPI) * Price) / (...
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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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How to forecast volatility using high-frequency data?
There is a large literature covering volatility forecasts with high-frequency tick data. Much of this has surrounded the concept of "realized volatility", such as:
"Realized Volatility and ...
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Historic EPS forecasts for S&P500 [duplicate]
Looking for a free dataset of earnings forecasts for S&P500. It would be interesting to plot the accuracy of forecasts vs the actual.
Cheers!
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Combining Mulitple Forecasts? Budged Constraints?
I'm hoping that someone can lend a hand. I have been reading various papers on how to combine multiple forecast time series. The main paper is Granger and Bates 1969. The suggestion here is that there ...
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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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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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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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Modelling and forecasting mixed frequency financial data
I was wondering if someone could provide some guidance to me. I would like to
Combine various financial data of mixed frequencies (some daily,
weekly, some quarterly) to a composite index. I have ...
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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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How to obtain one-step ahead forecast in Python based on GARCH?
I am trying to produce one-step ahead forecast using GARCH in Python using a fixed windows method. I ultimately want to put the code below in a for loop, but this code snippet does not perform as I ...
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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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Why 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 conclude that historical-based forecast are better suited than risk-neutral models for financial predictions....
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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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Why is volatility said to be persistent?
Persistence in volatility of stock returns is one of the common 'stylized facts' when it comes to analyzing time series. However, I am wondering for theoretical arguments why (estimated) volatility ...
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Is there any research on applying state-space or dynamic linear models to forecasting equity risk premia?
Is there any research on applying state-space or dynamic linear models to forecasting equity risk premia on a security-by-security basis with a medium term horizon (say 3 month to 12 months horizon)?
...
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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 do you evaluate a covariance forecast?
Suppose you have two sources of covariance forecasts on a fixed set of $n$ assets, method A and method B (you can think of them as black box forecasts, from two vendors, say), which are known to be ...
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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 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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Any research on how natural language processing can be used to forecast stocks?
Is there any published research of decent quality linking news or unstructured information to asset returns? I know that Thomson Reuters offers its Machine Readable news (MRN), so somebody must use it....
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How does Linear-Exponential Loss (Linex) function tend towards Quadratic Loss function?
Thank you for your help everyone, and I apologise beforehand if this is a lousy or dumb question.
I am looking to read up more on Quadratic Loss & Linex Loss, and forecast optimality.
In my ...
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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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How to fit a SARIMA + GARCH in R?
I'd like to fit a non stationary time series using a SARIMA + GARCH model. I have not found any package that allow me to fit this model.
I'm using rugarch:
...
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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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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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Use futures contracts of different lengths to predict spot prices
So I am trying to see how future contracts prices with different time to maturity are able to predict the actual spot price of crude oil at the time of maturity for the contracts.
I have the simple ...
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How are cryptography and speech recognition technology applied to forecasting financial markets?
One of the answers to my previous question regarding the strategy of Renaissance Technologies, there was a reference to The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly ...
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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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Confidence Intervals for ARMA+GARCH forecasts
I have fitted an ARMA(1,1)+GARCH(1,1) model to my logreturns series. When it comes to my standarized error's distribution however, I have opted for a Skewed Generalized Error Distribution, because of ...
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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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Use of ugarchroll vs ugarchforecast: setting parameters
I would like to generate 21 day ahead forecast volatility with ugarchroll.
I know it is similar to ugarchforecast with the exception that ugarchroll is a rolling average which considers initially the ...
