Questions tagged [forecasting]
The forecasting tag has no usage guidance.
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How useful is the genetic algorithm for financial market forecasting?
There is a large body of literature on the "success" of the application of evolutionary algorithms in general, and the genetic algorithm in particular, to the financial markets.
However, I feel ...
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Why aren't econometric models used more in Quant Finance?
There is a big body of literature on econometric models like ARIMA, ARIMAX or VAR. Yet to the best of my knowledge practically nobody is making use of that in Quantitative Finance. Yes, there is a ...
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What types of neural networks are most appropriate for trading?
What types of neural networks are most appropriate for forecasting returns? Can neural networks be the basis for a high-frequency trading strategy?
Types of neural networks include:
Radial Basis ...
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How to estimate real-world probabilities
In the world of finance, Risk-neutral pricing allow us to estimate the fair value of derivatives using the risk free rate as the expected return of the underlyings.
However, the behavior of ...
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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 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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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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Why are GARCH models used to forecast volatility if residuals are often correlated?
The answers to this question on forecast assessment suggest that if the sequence of residuals from the forecast are not properly independent, then the model is missing something and further changes ...
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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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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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What type of analysis is appropriate for assessing the performance time-series forecasts?
When using time-series analysis to forecast some type of value, what types of error analysis are worth considering when trying to determine which models are appropriate.
One of the big issues that ...
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How to forecast expected volatility from high-frequency equity panel data?
I'm wading through the vast sea of literature on realized volatility estimation and expected volatility forecasting (see, e.g. Realized Volatility by Andersen and Benzoni, which cites 120 other papers,...
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Using linear regression on (lagged) returns of one stock to predict returns of another
Suppose I want to build a linear regression to see if returns of one stock can predict returns of another. For example, let's say I want to see if the VIX return on day X is predictive of the S&P ...
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1
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What is the Sugihara Trading System?
I recently heard the term Sugihara Trading System. I guess it might be some trading strategy or a special model to predict trends in market data, but I couldn't find out anything about it. Does anyone ...
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Why are stock index futures not used to forecast how much the stock market will rise, given that interest rates futures are used for this purpose?
In news articles, the reader often read interest rates forecasts calculated based on interest rate futures.
An example is here;
How did traders calculate that the expected number of rate hikes is 4 ...
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What methods do I need to learn in order forecast asset price movements?
What are the standard models used to forecast asset price movements? For example, if I were to trade an option, what model would I use in conjunction with option pricing models to forecast the stock ...
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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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Can the futures market's open interest predict commodity, treasury, and equity returns?
I came across this article and became curious. Can the futures market's open interest really predict market action?
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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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Is volatility for the next day forecastable? To any extent?
In a more general way: is there
1) a methodological approach to quantify the correctness of a model that produces a probability distribution for the, say, S&P 500 index return for the next ...
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How we can forecast stock prices using chaos theory?
I saw an article in which the writer had mentioned that he used chaos theory to predict stock prices and ended up with a profit over 30%. Chaos theory is basically about finding patterns called ...
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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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How to forecast high-frequency data?
Introduction: I have seen a plenty of articles/books regarding volatility forecasting applied to high frequency data, but none of them were dedicated to forecasting the actual prices (for example bid/...
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Position management in presence of continuous forecast
Let's say we have an equity liquidity-providing model that was fitted on 1 minute bar periods. The model forecasts the 1-min next period return given the activity of the previous bars. Now, when we ...
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How do I replicate John Hussman's recession forecasting methodology?
John Hussman has a recession forecasting methodology he often posts about on his blog, and I am trying to replicate it using publicly available data. I would like to assess his accuracy in predicting ...
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The T+H Problem in Factor model forecasts
Suppose we train on M individuals consisting of T observations (i.e. TxM design matrix). The dependent variable is one-year return for each security (H = horizon of one year). In a factor model ...
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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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What are the ensemble techniques to forecast returns?
It was pointed in an other question that ensemble methods can help to reduce curve fitting.
What are your experience with these and which one seems the most appropriate? If I had two forecasters that ...
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What is the best way to forecast prepayment rate in an emerging market mortgage loan portfolio?
I constructed a model to forecast the prepayment rates for a mortgage loan portfolio (of mortgages in an emerging market) using probit regression on factors such as loan-to-value, PTI, time from ...
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Density forecast of a GARCH model
I am currently working on developing a series of density forecasts and I am encountering some problems. I am working on weekly S&P 500 returns and the returns process is described as
$r_{t} = \...
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Estimate rolling stochastic volatility forecast using stochvol in R
I want to use the R package stochvol to fit a SV model to a DAX training set and use the output to estimate a rolling one-step-ahead forecast:
...
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Is there an optimal covariance one would want forecasts to have?
Often in a quant process, one will generate a time series of return forecasts and use them in some sort of optimization to generate a portfolio. Generally, there will be a covariance matrix of market ...
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The Basis of Using Technical Indicators as Inputs
In the process of my research I very often come across academic papers regarding modelling and trading strategies that in one way or another incorporate some technical indicators. For example in some ...
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What is a sound way to project Company X's earnings over the next Y years?
I need to estimate cumulative earnings over the next Y years and I'd like to find a solution that is theoretically sound and relatively simple. Can anyone recommend an approach?
Given:
I have 30 ...
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How to use physics models in Time Series Analysis and Forecasting.
I have been studying methods of Technical Analysis for several years and I am disappointed. I actually do not consider it useful. I have not met anyone who can constantly win in the market using these ...
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How do you synthesize a probability density function (pdf) from equally weighted price data?
What I'm working with:
I have a collection of prices that has very few to no repeating values (depending on the look back period) ie each price value is unique, some prices are clustered and some can ...
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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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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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Why are multiple custom curves (swap) built for one desk?
Currently in a journey of learning and getting my hands a bit dirty with Interest Rate Swaps.
Why there are multiple customized curves built by many even within one desk? For e.g. Short Rates desk ...
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Is there any way to easily estimate and forecast seasonal ARIMA-GARCH model in any software?
I use R to estimate a seasonal ARIMA(8,0,0)(5,0,1)[7] model for the seasonal differences of logs of daily electricity prices:
...
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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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Measure how different forecasted volatility is from realized volatility
Hi Quantitative Finance Stack Exchange,
I'm looking for an opinion on a simple question. Suppose I use a Garch(1,1) model to make a volatility forecast.
At time $t$, I have realized volatility $\...
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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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Predicting the Future FX Spot Rates
Say I need to predict what the spot rate between USD and CAD will be in 3 months. What will be the most accurate measure or model that I could possibly use? Does the 3 month forward rate necessarily ...
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Coin Toss System
Coin Toss Runs Calculator
The expected number of runs for two consecutive heads or tails is 3. Is there an edge if we place a progressive constant size bet(limited to 3 times)for consecutive ...
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Is there a relation between these two forecasting/estimation approaches?
When learning econometrics I have usually seen stuff from the following perspective:
Assume $Y_t = f(X_t) + e_t$, where f is some function of $X_t$ (typically linear). For example, assume $Y_t = X_t *...
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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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Simulating returns from ARMA(1,0)-GARCH(1,1) model
I want to obtain a simulation of one-step ahead forecasts of stock returns process governed by ARMA(1,0)-GARCH(1,1) process. The returns are of form:
$x_t = \mu + \delta x_{t-1} + \sigma_t z_t$
From ...
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How is a GARCH model readily complementary to a forecasting model?
Hi Quantitative Finance Stack Exchange,
It's my first go at GARCH models so give me a chance with my phrasing. I'm looking for an answer to a general question.
First, I understand that you can have ...
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ARIMA model, cannot get rid of low order ACF spike
I've gone through all the steps to fit a good ARIMA model - I plotted the data, I looked at the ADF tests, I looked at the ACF plot with no AR and MA terms just a constants. I came up with an ARMA(0,1,...
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