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4
votes
1answer
463 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. ...
2
votes
1answer
74 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 ...
-1
votes
1answer
31 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 ...
4
votes
0answers
275 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 ...
4
votes
0answers
171 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 ...
2
votes
0answers
160 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 ...
2
votes
0answers
256 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, ...
2
votes
0answers
24 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 ...
2
votes
0answers
175 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 ...
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 ...
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 ...
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
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 ...
1
vote
0answers
474 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? ...
1
vote
0answers
218 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" ...
0
votes
0answers
27 views

Approximating the conditional expectation in simulations

I am simulating stock returns, which are governed by the following equations $r_t = \mu + \delta r_{t-1} + \sigma_t z_t$ $\sigma^2_t = \omega + \alpha \varepsilon_{t-1}^2 + \beta \sigma^2_{t-1}$ ...
0
votes
0answers
42 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
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0answers
57 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 ...
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: ...
0
votes
0answers
48 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 ...