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3
votes
1answer
27 views

Consensus Forecast Data for NFP

Does anybody know where I can get historical consensus forecast data for Non-forma Payroll (NFP)? Or any forecast data for NFP. Thanks,
1
vote
1answer
23 views

Data of Credit Migration Matrices

Please advise that how to get the data of credit migration matrices There is a paper of credit migration matrices, I would import the data to Matlab or R for credit analysis. https://www....
1
vote
0answers
18 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 ($a$)...
2
votes
1answer
91 views

Starting values for constrOptim() in R

I want to perform a constraint optimization for Maximum Likelihood Estimation in R to forecast volatility of returns. The probleme is that my initial values aren't in the permitted region. Is there ...
2
votes
2answers
246 views

Normalization of Market Data in Time Series Correlation

Suppose we have 2 time series of market data, one for each security and we want to correlate between these 2 securities. My question is How do we handle gaps of missing data in the time series? ...
-4
votes
0answers
49 views

How to simulate lognormal returns with Monte-Carlo?

I'm trying to forecast the price of silver over a 5 year period. I pulled silver price data going back to 1970, and then computed returns based on a 5-year lag. My problem is that these returns are ...
1
vote
0answers
28 views

Forecasting conditional returns in DCC-GARCH-copula approach in R

anyone who could help me interpreting and modifying this code? I have a dataset and want to reserve the last 100 returns for out-of-sample analysis. After specifying and fitting the garch-spd-copula, ...
2
votes
0answers
62 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 “...
23
votes
5answers
3k views

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 ...
0
votes
0answers
14 views

Is my demand prediction too low?

Hi i have a problem right now at work. For certian business segments,some sales target are establish each year. This targets are establish based on the managers feelings. Its like this: Manager: "so ...
-1
votes
1answer
46 views

Determining confidence level of directional signals

With regards to technical analysis, are there ways of determining the confidence level of a directional signal? Taking a relative strength index (RSI) as an example, can the extent to which an asset ...
1
vote
2answers
34 views

How do companies forecast revenue and earning estimates for a quarter or year in advance?

I'm sure there are models and they have low and high estimates. But how to do they decide on the percentage growth? A bit of art + science?
4
votes
1answer
87 views

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 ...
25
votes
3answers
6k views

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: Support Vector ...
1
vote
0answers
6 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/...
0
votes
0answers
29 views

Approximating the conditional expectation in simulations

I am simulating stock returns, which are governed by the following equations $r_t = \mu + \delta r_{t-1} + \sigma_t z_t$ $\sigma^2_t = \omega + \alpha \varepsilon_{t-1}^2 + \beta \sigma^2_{t-1}$ $\...
2
votes
1answer
120 views

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 ...
8
votes
3answers
230 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 ...
1
vote
2answers
115 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
1answer
89 views

how to calculate RMSE, MAE, given ugarchforecast results?

Given S&P500 returns for the past 20 years I fitted an ARMA(1,1)-GARCH(1,1) model using the rugarch package, so using ugarchspec() and the ugarchfit(), with different innovations distributions, i....
1
vote
0answers
17 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 ...
0
votes
0answers
45 views

Macroeconomic forecasting

During the last year I was working on developing several forecasting models which I was checking mainly in energy markets. They are based on regression, autocorrelation and also machine learning ...
0
votes
0answers
70 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 ...
1
vote
1answer
139 views

One-step ahead forecast of a AR(1) process (GARCH context)

I am using a Matlab toolbox for obtaining one-step ahead forecasts of the conditional mean from the ARMA(1,0)-GARCH(1,1) process and I have encountered a piece of code that contains, in my opinion, a ...
6
votes
1answer
237 views

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} = \...
1
vote
0answers
56 views

How do I use common forecasting models to forecast FUTURE values? [closed]

I would like to forecast likely future demand based on historical demand. The problem is: I have no mathematical background and in relevant tutorials and even in literature formulas are used, that ...
1
vote
1answer
47 views

ARIMA Forecasting always converges?

I read an article about arima forecasting and i said that before we forecast arima model, its stationarity has to be checked. If the model is stationary, it is clear that forecasting converges to ...
1
vote
0answers
19 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 ...
1
vote
1answer
32 views

Aggregating growth rates

I'm working on a simple forecast model that uses Cumulative Annual Growth Rate (CAGR) to project future growth, and I've run into an apparent paradox. The model includes multiple lines of business ...
1
vote
0answers
57 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 ...
4
votes
0answers
295 views

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/...
6
votes
1answer
321 views

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 ...
4
votes
1answer
515 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. ...
0
votes
0answers
29 views

Are low oil prices and low shipping costs really a leading indicator for a shrinking economy

Recent article in Bloomberg saying that lowered shipping costs n the form of the Baltic Dry Index and lowered oil prices are in someway a concern for a growing global economy: http://www....
2
votes
1answer
187 views

Forecasting using GARCH in R

I am using the predict and ugarchforecast functions in R. When I fit my models and try to forecast, I get either only increasing or decreasing values for sigma, does anyone know why? Thank you ...
5
votes
1answer
389 views

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 ...
2
votes
1answer
49 views

List of Economic Data for Index Forecast

What econometric symbol list (or tickers) could be used to forecast return of global stock market indexes (S&P500, TSX, CAC40, ...) and their subsectors? I'm aware of the answer to question: ...
0
votes
0answers
50 views

Estimate volatility in forecast

I have a model with a rolling forecast. In each time step $t$, I predict the price for the next periods, e.g. $\hat{p}(t, t+1)$ and $\hat{p}(t, t+2)$. If I start in $t=0$ and arrive at $t=2$, I ...
2
votes
1answer
107 views

FORECASTING Model AR(1) in an Autoregressive Form The Pi´s Parameters

Ive been implementing a little exercise to obtain the first 2 forecasting points of an AR(1) process. And i want to have the forecasting ponts using the three forms: Im folowing this pdf http://www.le....
2
votes
0answers
205 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-...
0
votes
1answer
121 views

Constant decreasing volatility, GARCH forecasting

I am trying to forecast the volatility using GARCH modelling in R. I fit an ARMA(1,1)-GARCH(1,1) model, but my sigma predictions are constantly decreasing. Anybody know why? ...
2
votes
2answers
181 views

Extracting Signal from Noisy Data

Consider a scenario in which Y_t represents the % change in price and we want to use X_t to predict Y_t. We assume that X_t is information we get before Y_t is revealed. Suppose that in reality Y_t =...
5
votes
2answers
122 views

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 ...
42
votes
9answers
18k views

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 ...
2
votes
0answers
290 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, ...
1
vote
1answer
210 views

Machine learning to build top 3 price scenarios over n days

I have a time series of closing prices for a given stock. I would like to formulate possible future scenarios for the price. My intention is not to use these "likely" scenarios to take any position. ...
1
vote
1answer
376 views

How to fit a SARIMA + GARCH in R?

I'd like to fit a non stationary time series using a SARIMA + GARCH model. I have not found any package that allow me to fit this model. I'm using rugarch: model=ugarchspec( variance.model = list(...
18
votes
8answers
9k views

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 ...
0
votes
1answer
214 views

How to forecast bond price with time series

I have the goal of being able to develop a model that can forecast the future prices of european government bond (or other private bonds), particularly from the historical prices and returns of the ...
4
votes
5answers
1k views

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: ...