Questions tagged [forecasting]
The forecasting tag has no usage guidance.
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Optimal predictors for 1-month returns
I am implementing a Random Forest classifier algorithm on Python for predicting future stock returns (one month). My goal is to foresee whether the cumulative returns in a month will be negative or ...
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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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GARCH(1,1) forecast plot in R with training data
I've fit a GARCH(1,1) model in R and would like to create a plot similar to the one in this question: Is this the correct way to forecast stock price volatility using GARCH
Could someone direct me to ...
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What are some good models for stock price predictions?
For the fitting and forecasting of time-series data on stock price, the most frequent model I have heard of is ARIMA. ARIMA is actually conducting a regression of stock prices and residuals of stock ...
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Why are cashflows "modelled backwards in time"?
A am currently reading a manual on how to use some actuarial modelling software to project the expected liability payments made under an annuity contract. In this guide, the following statement is ...
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How do you build a model with uncertain time range?
Let's say you want to test the hypothesis that given a signal reaches some threshold, some asset will have some return over the next period.
Here we have two unknowns.
One, the value of your ...
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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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Forecasting accuracy in one month and hedging
I am working on predicting the daily data of a financial time series $[Y(t+1),...Y(t+j)]$ =$f(X_1(t),...X_1(t-i),.....,X_n(t),...X_n(t-i))$ where $Y$ is a commodity price $X_i$ are predictor variables ...
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Forecasting volatility farther ahead with autoregressive machine learning
ARIMA and GARCH are old news for predicting volatility time series of asset returns. I am aware of papers that replace ARIMA and GARCH with machine learning algorithms to predict financial volatility ...
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white noise not forecastable ? stationarity doesn't imply forecastability?
We know that white noise isn't forecastable because of its random aspect. White noise is also stationary, and which is confusing me, is that we always try to make a serie stationary to make forecasts, ...
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How would you forecast volatility without using any programming languages or machine learning or anything of that sort?
I am trying to forecast volatility. I am on the tactical asset allocation team. No one on our team knows machine learning or any programming languages. We are fundamental equity research analysts ...
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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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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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Subscription Based Revenue Prediction
My dataset is on revenues from subscription-based (no commitment, can cancel any time). We have people signing up every year, continue paying for a few years and then gradually cancel the subscription....
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Predicting natural gas prices using weather data
I developed a model for predicting temperatures and I am planning to add this to a natural gas fair value model together with other parameters.
My question is:
is the natural gas future price ...
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Excel formula for Laplace distribution
I am trying to create a forecast model, projecting the number of passengers through an airport over a period of time (daily, weekly, and monthly). I've already used Excel's FORECAST and POISSON ...
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Can variance change over time?
I'm working on a toy project that involves fantasy basketball, I know this is the quantitative finance stackexchange, but it seemed like the best place to ask this question.
My goal is to make ...
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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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Can someone explain rigorously Taleb's criticism of Nate Silver's election forecasting?
Taleb makes the claim in this paper (and others) that there exists some sort of bound on the variance of a binary forecast such that if a forecaster's binary predictions exceed the bounds on variance ...
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How can I forecast the Exponential Moving Average of the next day?
I am trying to forecast prices with exponential moving average method.
The equation for EMA = [(Closing * k) + (EMA(y) * (1-k)]
where: Closing is closing price of today, k is the weighted multiplier,
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Exponential Smoothing - Alpha greater than 1
Simple stats question.
I'm having trouble finding anything in the literature as to why the smoothing coefficient can never be greater than 1. This question was started by me doing time series ARIMA ...
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Forecasting a seasonal series with R
I am working with the program "R". I used the command "seas (X-13)" to deseasonalize my quarterly series, then I did the forecast with it. Therefore my forecast is in deseasonalized terms.
Now, I was ...
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Generate scenarios of multiple related parameters
Assume I have three industry datasets: interest rates, inflation and unemployment. Data contains information of last ten years and it's monthly.
Now, I would like to create N possible scenarios of ...
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Predicting stock returns using principal components of macroeconomic variables
I'm trying to detect return predictability by regressing stock returns on the first couple of principal components of a set of macroeconomic variables. I'm doing this for different stock styles such ...
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Predicting portfolio returns
I suppose there are roughly two approaches to predict portfolio returns.
Either predict the returns of all underlying stocks and aggregate all individual stock predictions, or predict the portfolio ...
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bond yield forecasting
About the problem of interest rate forecasting I find various paper that address the problem from the perspective of risk premia and affine term structure model. For example Cochrane and Piazzesi (...
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What is the best GARCH model for forecasting daily stock return and why?
If I want to forecast daily stock return let say Apple what would be the best GARCH model and why?
(ARCH, GARCH-M, IGARCH, EGARCH, TARCH etc)
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How to prove that the expected squared error associated with the optimal combination weight is smaller than the minimum of 2 forecast variances?
I am looking at linear combination of two forecasts (Bates and Granger, 1969). I would like to understand how to prove that the expected squared error associated with the optimal combination weight is ...
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Why can the t-bill rate forecast stock returns?
The tbill rate is used as a predictor of the equity premium in a number of papers.
Whilst there is not a general consensus about whether it is a significant predictor, it is still widely used.
I ...
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GJR-GARCH model using garchFit function
I'm trying to use the garchFit function described here in order to define a GJR-GARCH model to estimate volatility and then forecast VaR.
I tried using ...
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How to test the linearity assumption of a model?
Let's say I want to have a model that projects income over a stressed period. I have a marked-to-market component that shows the P&L of trading book positions during this stressed period. Along ...
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Rolling forecast using GARCH model
EDIT
This is not a duplicate of my original question linked, since I have since overcome that problem and have posted an answer. Since solving the previous problem, I have run into the problem ...
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Is this the correct way to forecast stock price volatility using GARCH
I am attempting to make a forecast of a stock's volatility some time into the future (say 90 days). It seems that GARCH is a traditionally used model for this.
I have implemented this below using ...
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Poor results forecasting stock price volatility using Python's GARCH model
As far as I understand, forecasting stock price volatility should be more achievable than forecasting absolute prices or returns. It seems as though GARCH models are the traditional and most widely ...
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Financial forecasting and Optimal order submission [closed]
For instance, If i have a model that can accurately forecast 3s ahead, would the trading logic be rather trivial? I have fit a series of distributions to L2 data and believe I have a fairly good grasp ...
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Predicting stock returns with GARCH in Python
I have seen this post: Correctly applying GARCH in Python which shows how to correctly apply GARCH models in Python using the arch library. Now I am wondering how I ...
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Why does computing correlation between index levels vs. percentage changes yield completely different results?
I am examining the relationship between the S&P 500 and the Industrial Production Index. Computing the correlation between these these variables yield vastly different results if expressed in ...
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Comparison of normalization methods on market returns
I am looking to use a multi-factor model to make target-return predictions. Since the factor-returns come from different scales I need to normalize first.
There are different ways to normalize ...
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Forecasting time series data using auxiliary information and associated questions
Suppose I want to forecast MSFT time series, using MSFT history as well as SPY history. Are there good time series forecasting methods that permit auxiliary data to be used? Perhaps you should just ...
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Filling a few missing data in time series?
I'm writing a paper about Uncertainty indices like VIX, etc. I already collected all data but it seems that some of the variables got a few or a little more missing data. I have daily and monthly data ...
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What is the optimal approach to "backcasting" alternative asset classes (i.e. PE, Hedge Funds, Real Estate)?
I am interested in coming up with better risk calculations for alternative asset classes. As these are illiquid, not a lot of historical data is available.
My idea is to use performance of stocks ...
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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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How to interpret the accuracy result of the forecaste?
I'm trying to forecast the vacancy rate of multifamily rental property. I have the data from 1992 until today. I'm trying to fit a model with the serie without the last 2 observations.
I only need ...
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How to interpret and define statistics of GBM output
I am trying to model the future prices of a number of commodities. For this, I am applying geometric Brownian motion, writing a Monte Carlo code in Python. Given that I want to estimate tommorows ...
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Probability of outlier events for laplace distribution
I've read that the laplace distribution is better for forecasting purposes than the normal distribution due to it better accounting for fat tails. However, when I run the numbers in matlab, laplace ...
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What is the purpose of short rate models?
Just venturing into quantitative finance and studying short rate models (Vasicek, CIR, Hull-White etc.). Wanted to ask a very simple intuitive question. How would a practitioner use these models? I ...
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Bond asset class long term assumptions [closed]
How are long term capital market expectations set in the industry?
I'm looking to get some pointers about setting long term assumptions for fixed income asset classes like global high yield credit, ...
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What is the difference between squared returns and variance?
I am trying to calculate 1-day ahead volatility forecasts using the exponentially weighted moving average, however I am unsure on how to read the formula provided within Risk-Metrics Technical ...
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ARIMA vs ARIMA + GARCH [closed]
If an ARIMA model converges quickly, would using GARCH improve the forecast performance? By improve I mean provide longer time periods for forecasts. Basically trying to forecast returns.
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Accuracy for GARCH models
How does one calculate the accuracy of forecasts given by GARCH models considering GARCH is run on returns. Assuming GARCH is a derivative of a regression based prediction model, would regular ...
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