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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 ...
Johan Smith'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
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0 answers
62 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-...
JOUD's user avatar
  • 1
0 votes
1 answer
59 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 ...
finance_bro'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
  • 368
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1 answer
78 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 ...
Chris's user avatar
  • 1
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 ...
Pavel Filip's user avatar
0 votes
0 answers
81 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 ...
JakcieJnr's user avatar
  • 141
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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 ...
Ricter's user avatar
  • 101
0 votes
0 answers
63 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, ...
daniel dvali's user avatar
0 votes
1 answer
76 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 ...
user70803's user avatar
1 vote
2 answers
296 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 ...
rodrigo's user avatar
  • 45
1 vote
0 answers
140 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 (...
rodrigo's user avatar
  • 45
0 votes
0 answers
92 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 ...
Michael's user avatar
  • 500
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0 answers
87 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 ...
MilTom's user avatar
  • 165
2 votes
1 answer
142 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, ...
Pat's user avatar
  • 21
3 votes
1 answer
276 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 ...
QMath's user avatar
  • 249
1 vote
1 answer
330 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. ...
user1414512's user avatar
0 votes
0 answers
214 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 ...
probablysid's user avatar
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 ...
Lejoon's user avatar
  • 147
0 votes
2 answers
229 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 ...
Special Sauce's user avatar
2 votes
2 answers
448 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 ...
deblue's user avatar
  • 281
0 votes
0 answers
115 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 ...
Petra Di Mario's user avatar
1 vote
1 answer
882 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 ...
Moataz's user avatar
  • 43
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 ...
savoga's user avatar
  • 11
1 vote
0 answers
836 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
0 votes
1 answer
129 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 ...
stevew's user avatar
  • 145
1 vote
2 answers
563 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 ...
wlog's user avatar
  • 11
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
0 votes
0 answers
407 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, ...
GusC's user avatar
  • 1
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 ...
BSel's user avatar
  • 1
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?
edd's user avatar
  • 223
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 ...
Dark2018's user avatar
  • 101
1 vote
0 answers
122 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 ...
Jordan Wrong'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
  • 11
0 votes
0 answers
113 views

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 ...
Mark Fisher's user avatar
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
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
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
0 votes
1 answer
63 views

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 ...
Melly Donald's user avatar
0 votes
0 answers
46 views

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 ...
Sebastian Strauss Hansen's user avatar
11 votes
1 answer
317 views

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,...
sets's user avatar
  • 1,471
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
3 votes
1 answer
380 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 ...
Hans's user avatar
  • 2,806
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
-4 votes
1 answer
368 views

Using geometric brownian motion for stock price forecasting [closed]

I am doing a dissertation in finance on a maths degree. I wanted to forecast stock prices using artifcial neural networks but none of my tutors are able to supervise so I'm having to do something else....
PlatinumMaths's user avatar
0 votes
2 answers
159 views

Forecasts for the S&P 500?

Would anyone know of any monthly forecasts for the S&P 500, historical over a long time periods. Websites like estimize provide forecasts of all sorts of things likes stocks and the balance of ...
user30609's user avatar
  • 133
0 votes
1 answer
143 views

How do you simulate returns for a portfolio when you have Lumpsum + Monthly investments (SIP) in place?

I'm trying to simulate portfolio returns using Norm.inv function in excel. Inputs to the formula: Prob= Rand, Std dev= Historical, Mean= 5 year historical average. Its easy to do this when you're ...
Swaraj_r's user avatar
0 votes
0 answers
71 views

Fitting a Spread into ARIMA AR(1) process

I'm a newbie to econometrics. I've simply ran a regression and have coefficient values of the variables. I'm running a regression for a crypto data, and I've gotten the Spread of the variables. To ...
ken4ward's user avatar
  • 101
2 votes
1 answer
164 views

forecasting hourly variance with higher resolution data available

Assume one has price data $P_{1}, P_{2}, \dots, P_{n}$ with one hour resolution and aims to forecast the variance for one hour ahead return. The first approach to try is ARCH or GARCH models. There ...
ABK's user avatar
  • 126

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