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68 views

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 ...
0 votes
0 answers
17 views

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 ...
0 votes
0 answers
55 views

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-...
0 votes
1 answer
77 views

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 ...
0 votes
1 answer
58 views

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 ...
1 vote
0 answers
84 views

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 ...
1 vote
2 answers
293 views

$n$-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 ...
0 votes
0 answers
34 views

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 ...
0 votes
0 answers
79 views

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 ...
0 votes
1 answer
64 views

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 ...
0 votes
0 answers
48 views

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 ...
0 votes
0 answers
62 views

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, ...
1 vote
1 answer
326 views

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. ...
1 vote
2 answers
288 views

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 ...
1 vote
0 answers
138 views

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 (...
0 votes
0 answers
84 views

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 ...
0 votes
1 answer
2k views

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 ...
0 votes
0 answers
86 views

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 ...
2 votes
1 answer
140 views

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, ...
3 votes
1 answer
273 views

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 ...
1 vote
1 answer
1k views

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 ...
1 vote
2 answers
13k views

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 ...
0 votes
0 answers
207 views

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 ...
1 vote
2 answers
867 views

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) / (...
0 votes
1 answer
68 views

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 ...
0 votes
2 answers
227 views

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 ...
26 votes
3 answers
5k views

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 ...
2 votes
0 answers
155 views

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 ...
2 votes
2 answers
439 views

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 ...
1 vote
1 answer
864 views

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 ...
0 votes
0 answers
112 views

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 ...
1 vote
0 answers
37 views

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 ...
6 votes
3 answers
2k views

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 ...
1 vote
0 answers
815 views

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 ...
0 votes
1 answer
127 views

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 ...
0 votes
2 answers
2k views

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 ...
1 vote
2 answers
559 views

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 ...
21 votes
5 answers
3k views

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....
3 votes
0 answers
225 views

"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 ...
0 votes
0 answers
402 views

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, ...
12 votes
4 answers
2k views

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 ...
8 votes
2 answers
956 views

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)? ...
0 votes
0 answers
88 views

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 ...
22 votes
5 answers
9k views

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 ...
3 votes
1 answer
378 views

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 ...
0 votes
0 answers
73 views

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?
0 votes
0 answers
38 views

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 ...
1 vote
0 answers
121 views

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 ...
31 votes
6 answers
9k views

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....
2 votes
2 answers
1k views

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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