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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 $\...
Donny Lee's user avatar
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4 votes
0 answers
180 views

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 ...
ninjaSurfer's user avatar
4 votes
0 answers
132 views

How to find a probability of VIX moving from one price to another

I asked a similar question on here with a bounty. I decided to modify the question to simplify what I am trying to do. Is there a package on MATLAB or some other tool where I can find the probability ...
Wolfy's user avatar
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4 votes
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272 views

Rolling window Kendall's tau against APARCH(1,1) correlation

Assume you want to forecast the correlation matrix of a stocks' basket (say 15 ~ 20 stocks from different sectors); assume you need to forecast at $T$ days because you will use the forecast ouput with ...
Lisa Ann's user avatar
  • 2,133
4 votes
1 answer
2k views

Moving window forecasting in Python

I am looking to create some code that will out-of-sample forecast the HAR-RV model. The model itself is formulated as the following, and the betas are estimated through HAC-OLS or Newey-West. ...
Niklas Lindeke's user avatar
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 ...
FooBaz's user avatar
  • 31
3 votes
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3k views

QLIKE loss function to evaluate forecasting model of log(realized volatility)

I use QLIKE as loss function to evaluate the forecasting performance of a RV realized volatility model. QLIKE = log $h$ + $\frac{\hat{\sigma}^2}{h}$ where $h$ is volatility forecast and $\hat{\sigma}...
Fra_Ve's user avatar
  • 141
3 votes
0 answers
1k views

Forecast of ARMA-GARCH model in R

I managed to forecast a GARCH model yesterday and run a Monte Carlo simulation on R. Nevertheless, I can't do the same with an ARMA-GARCH. I tested 4 different method but without achieving an ARMA-...
Tom's user avatar
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3 votes
0 answers
715 views

GARCH modelling and forecasting

I have a few questions regarding GARCH modelling and forecasting and it would be great if someone could help me. I am modelling the log return of oil spot prices using various GARCH models: GARCH, ...
user3384794's user avatar
3 votes
0 answers
333 views

Optimal Position Size with Transaction Costs given Forecast Mean and StDev

I have rather a challenging question. I'm hoping that someone can share their experience. I will build up the problem in steps. Let's start our thinking with the idea of a buy and hold strategy of ...
Stewart Charles's user avatar
3 votes
0 answers
582 views

Oscillatory time-series forecasting

I was wondering if this mean(160)-reverting/oscillatory time series "SUM" can be considered chaotic & forecastable to some extend short-term? http://sg.myfreepost.com/sgTOTO_analysispower.php?...
Shelagh's user avatar
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2 votes
0 answers
452 views

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 ...
Kareem Sayed's user avatar
2 votes
0 answers
104 views

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 ...
George's user avatar
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2 votes
0 answers
83 views

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 ...
user30609's user avatar
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2 votes
0 answers
289 views

Risk neutral probability and forecasting

When our goal is pricing of derivative products we, due to no arbitrage conditions, have to use the risk neutral probability. In other side if we have risk management purpose we have to use the “...
markowitz's user avatar
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2 votes
0 answers
49 views

Non-overlapping ranges of HCNN' observables and of state transition function

In the artcicle Forecasting and Trading the High-Low Range of Stocks and ETFs with Neural Networks HCNN is used for forecasting of nine time-series, namely: returns of the lows returns of the highs ...
zer0hedge's user avatar
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2 votes
0 answers
530 views

Stationarity tests in the frequency domain for regression

Strict stationarity is the strongest form of stationarity. It means that the joint statistical distribution of any collection of the time series variates never depends on time. So, the mean, variance ...
Barnaby's user avatar
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2 votes
0 answers
156 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 ...
Stewart Charles's user avatar
2 votes
0 answers
266 views

Modeling asset performance to Bitcoin revenue

I'm attempting to model asset performance to Bitcoin revenue, which is a driving force in the Bitcoin community. Question Is there any model, or research being done that tracks "hashes per second" (...
makerofthings7's user avatar
1 vote
0 answers
91 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 ...
Sane's user avatar
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1 vote
0 answers
835 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 ...
Sebastien Wdowiak's user avatar
1 vote
0 answers
75 views

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 ...
P. Howe's user avatar
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1 vote
0 answers
47 views

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. ...
yash agarwal's user avatar
1 vote
0 answers
158 views

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 ...
lara_toff's user avatar
  • 113
1 vote
0 answers
66 views

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, ...
CasusBelli's user avatar
1 vote
0 answers
50 views

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 ...
rjdata-analyst's user avatar
1 vote
0 answers
54 views

Volatility forecast on SPX option expiration day

I am looking for methods and papers on forecasting SPX option at-the-money implied volatility or realized volatility within its expiration day. What are some stylized facts and forecasting methods?
Hans's user avatar
  • 2,806
1 vote
0 answers
138 views

Modelling volatility for higher frequency data

I'm doing some academic work on volatility forecasting. I've got 1-minute bar data. It is not clear to me what model is best suited for forecasting volatility when higher frequency data is available. ...
s5s's user avatar
  • 442
1 vote
0 answers
59 views

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 ...
John Smith's user avatar
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 ...
duruixuan's user avatar
1 vote
0 answers
96 views

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 ...
Louis's user avatar
  • 11
1 vote
0 answers
72 views

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 (...
markowitz's user avatar
  • 324
1 vote
0 answers
752 views

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 ...
KOB's user avatar
  • 193
1 vote
0 answers
111 views

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 ...
Drew Brady's user avatar
1 vote
0 answers
106 views

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 ...
QFqs's user avatar
  • 125
1 vote
0 answers
73 views

Minimum Lower Partial Moment (n=2) hedging ratio

I would like to better have understanding on the minimum-LPM hedging. I have understood that the co-LPM matrix cannot be modeled by GARCH type models that are used to estimate to the covariance matrix,...
Techonomist's user avatar
1 vote
0 answers
189 views

Wavelet transform (the à trous time-based decomposition) in R

I urgently need to know how to apply the 'à trous' time-based decomposition in R [also known as Stationary Wavelet Transform] on a time series as a preprocessing, to use the result in forecasting and ...
ahmed reda's user avatar
1 vote
0 answers
150 views

Monte Carlo volatily

I was wondering if we could do a forecast on volatility using monte carlo on an underlying asset. For example EUR/USD : Simulating a lot of possible paths on 1 year then calculate the volatilty for ...
SquaredCircle's user avatar
1 vote
0 answers
152 views

What are the current gold standards for volatility prediction error?

I'm working on volatility forecasting models for equities and currencies. I am using daily data and am interested in producing forecasts for the next n days. To ...
Edward Yu's user avatar
  • 247
1 vote
0 answers
712 views

Forecasting volatility with rugarch and Covariance Matrix

I am trying to do a financial time series forecast in order to build a portfolio. I already have some code running rugarch library and I am not sure if I am forecasting correctly, after that I would ...
Manzha's user avatar
  • 11
1 vote
0 answers
58 views

How to reduce data dependence for empirically assessing option pricing model performance?

I am preparing a paper about mitigating assessment failures for option pricing models. For the sake of simpliciy, suppose we are talkin about European options. In basic terms, what I would like to say ...
berkorbay's user avatar
  • 1,051
1 vote
0 answers
123 views

Transformation of GARCH Equation to multiple-day Forecast Equation

I want to understand the procedure of how to predict with the GARCH Modell. Therefore it is said that a one day ahead forecast is easy due to the fact that the GARCH equation can produce this. ...
clee1994's user avatar
1 vote
0 answers
156 views

ARIMA prediction for currencies

I was browsing TradingEconomics.com and I came across their forecast models which immediately captivated my interest. They describe them as "projected using an autoregressive integrated moving average ...
Justin's user avatar
  • 11
1 vote
0 answers
305 views

Relationship between in-sample and out-sample periods length

I have two general questions regarding "in-sample fitting vs. out-of-sample backtesting" kind of analyses. Is there any relationship between the length of the data collected for in-sample fitting ($a$)...
Kondo's user avatar
  • 449
1 vote
0 answers
36 views

What are appropriate algorithms for forecasting contract schedules to maximize profit?

Imagine a situation where a business negotiates contracts for the maintenance of widgets it sells. Situation Customer buys 20 widgets. Customer negotiates contract for widgets to be serviced/...
ds1984's user avatar
  • 111
1 vote
0 answers
59 views

Relative merits of Adjusted versus Closing prices for market predictions

Basic question I am familiar with the data returned from Yahoo. For indices and the like (e.g. ETFs) there are seven columns of data: Date, Open, High, Low, Close, Volume, Adjusted. We only need ...
n1k31t4's user avatar
  • 121
1 vote
0 answers
359 views

Asset allocation and GARCH models

I am trying to solve an asset allocation problem and I am having some troubles grasping the concept. I am working with excess returns on 4 stock indices and I am obtaining the excess returns forecasts ...
Masher's user avatar
  • 491
1 vote
0 answers
23 views

Reconciling forecasted growth of components and sum

I'm working with a very basic basic forecast model using Compound Annual Growth Rate and I need to reconcile the forecasts at different levels of detail. Suppose I have two business lines with ...
user2864947's user avatar
1 vote
0 answers
86 views

Cross-sectional moments

I got a seminar topic named Forecasting risk from cross sectional moments? Could at least someone tell me what should I write about and if there is any paper that I could read. Thank you very much in ...
user17539's user avatar
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 ...
nadeem's user avatar
  • 23