Add to ORKGIn this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper.本文フィルはリンク先を参照のこ
This paper investigates the use of a flexible forecasting method based on non-linear Markov modellin...
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post vo...
Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows o...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and dail...
Extensive research has been done within the field of finance to better predict future volatility and...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the int...
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
In this research, different models are used to construct volatility surfaces and these models are co...
Volatility forecast is an important task in financial markets. It has held the most attention among ...
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting common...
In this work, neural networks are used to forecast daily Realized Volatility of the EUR/USD, GBP/USD...
This paper investigates the use of a flexible forecasting method based on non-linear Markov modellin...
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post vo...
Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows o...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
In this paper we consider a nonlinear model based on neural networks as well as linear models to for...
This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and dail...
Extensive research has been done within the field of finance to better predict future volatility and...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the int...
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
In this research, different models are used to construct volatility surfaces and these models are co...
Volatility forecast is an important task in financial markets. It has held the most attention among ...
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting common...
In this work, neural networks are used to forecast daily Realized Volatility of the EUR/USD, GBP/USD...
This paper investigates the use of a flexible forecasting method based on non-linear Markov modellin...
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post vo...
Volatility is a measurement of the risk of financial products. A stock will hit new highs and lows o...