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1answer
73 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 ...
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0answers
38 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 ...
0
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1answer
45 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 ...
0
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0answers
39 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 ...
4
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1answer
271 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
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1answer
31 views

Diebold-Mariano test

I am trying to use the Diebold-Mariano test but it doesnt work for some reason. Here is my code: dm.test(maegarch14,maeegarch14,h=126) where ...
-3
votes
1answer
62 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? ...
1
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2answers
103 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 ...
4
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2answers
117 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 ...
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9answers
14k 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 ...
1
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0answers
97 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
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1answer
159 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. ...
0
votes
1answer
124 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 = ...
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8answers
7k 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
98 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 ...
3
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5answers
516 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: ...
1
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2answers
143 views

How to write a home task report which is part of the interview process for a quant position in a trading firm

I recently appeared in an interview for a quant research post in a trading company. As part of the interview, I was given a home-task to solve in a week. The inerviewer gave me a dataset consisting of ...
1
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1answer
62 views

How can I forecast future correlation?

There are some standard models for forecasting volatility (e.g., GARCH) and for forecasting returns (e.g., factor models). What kind of standard models exist for forecasting future correlation between ...
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0answers
18 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 ...
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0answers
29 views

Forecasting bond yields

Do you know any models which can be used for prediction of corporate bonds yields (or goverment bonds yields) when we know forecasts of macroeconomic fundamentals (gdp, fed funds rate, interbank rate, ...
1
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2answers
279 views

Using Technical Indicators for forecasting Financial time series using Machine learning models

Hi I am trying to use financial technical Indicators for forecasting, using machine learning models. The usual approach in time series cross validation is to use a moving window or growing window. ...
2
votes
1answer
142 views

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 ...
1
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1answer
286 views

HAR-RV, realized GARCH and HEAVY model for realized volatility

I don't have much experience with volatility modeling using intraday data but I'm in the process of collecting 5mins data. Currently I have ~6 months of data. Is it enough to use these models with ...
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0answers
113 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
1answer
158 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? ...
6
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1answer
448 views

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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4answers
359 views

Implementing A 50/50 Prediction Model Strategy

Reworded the question for clarity (see edits for original post): How can one knowingly foresee where a 50/50 prediction model will be profitable? For previous posts: I understand that if I have a ...
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5answers
5k views

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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3answers
455 views

What Is A Good Success Rate Using Machine Learning For A Beginner?

I know this question will be quickly destroyed and my account summarily banned, but I just have to ask: For a trader using machine-learning algorithms (SVMs, ANNs, GAs, Decision Trees) for ...
1
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2answers
90 views

How To Account For Inflation Over Historical Data

I believe inflation is greatly affecting my sample data, even when using percent-changes for movements. I have read this post, which recommends the formula ((Current-Base Year CPI) * Price) / ...
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3answers
2k views

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 ...
2
votes
1answer
268 views

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 ...
1
vote
1answer
187 views

How would you correct a GARCH model to deal with non mean reverting volatility?

I am currently attempting to model and forecast volatility of bitcoin but have not been able to find a GARCH model that fits the data appropriately. I've used tick data sampled at 1 hour intervals ...
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3answers
5k 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 ...
2
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1answer
93 views

To understand FOMC events and its impact on the market

Last month when FOMC meeting decision went out that fed would start to exit QE3, immediately we saw a deleveraging effect: SPY went down, GLD went down, and LQD (bond) went down, but US dollars went ...
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4answers
12k views

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 ...
2
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1answer
398 views

Calculating the probability of a price change using an options pricing formula

I don't know if I'm doing this right and I'd greatly appreciate help. I'm trying to use an option pricing formula to backout the likelihood of the Euro dropping below $1.27, even for a minute, at any ...
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2answers
351 views

What stock market indicators to model based on twitter feed? [closed]

We are developing an algorithm that models twitter users and groups of words that may indicate real world events. One application is modelling elections, i.e which party is likely going to win. ...
2
votes
1answer
200 views

How to calculate two-time scale variance?

I am having trouble understanding how to calculate two-time scale variance as I do not have a strong mathematical background. Suppose I want to calculate the TSRV at 5 min intervals. Do I calculate ...
6
votes
3answers
617 views

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 ...
0
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1answer
517 views

Selecting timeframe for time series analysis

In technical analysis, we may use confluence of direction for 3 timeframes to roughly gauge bias of market now. Similarly, if we use time series forecasting methods to predict(say daily data-whether ...
1
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0answers
465 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? ...
9
votes
1answer
887 views

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 ...
5
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2answers
771 views

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 ...
4
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1answer
519 views

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 ...
1
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2answers
566 views

Evaluating forecasting algorithm

I am trying to evaluate a forecasting algorithm for stock price prediction. However, the performance of the algorithm may be very much tied to the trading strategy. Is there a systematic way for ...
4
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0answers
155 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 ...
1
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0answers
209 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" ...
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4answers
483 views

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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2answers
191 views

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 ...