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
We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Val...
This paper compares Higher Order Neural Networks (HONN) with Neural Networks, and linear regression ...
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatili...
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...
In this research, different models are used to construct volatility surfaces and these models are co...
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
Volatility forecast is an important task in financial markets. It has held the most attention among ...
This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the int...
Extensive research has been done within the field of finance to better predict future volatility and...
In the area of financial stock market forecasting, many studies have focused on application of Artif...
In this paper, we compare three methods of estimating the volatility of daily SBP 100 Index for opti...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting common...
We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Val...
This paper compares Higher Order Neural Networks (HONN) with Neural Networks, and linear regression ...
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatili...
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...
In this research, different models are used to construct volatility surfaces and these models are co...
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
Volatility forecast is an important task in financial markets. It has held the most attention among ...
This paper uses Long Short Term Memory Recurrent Neural Networks to extract information from the int...
Extensive research has been done within the field of finance to better predict future volatility and...
In the area of financial stock market forecasting, many studies have focused on application of Artif...
In this paper, we compare three methods of estimating the volatility of daily SBP 100 Index for opti...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting common...
We develop a LSTM neural network for the joint prediction of volatility, realized volatility and Val...
This paper compares Higher Order Neural Networks (HONN) with Neural Networks, and linear regression ...
This study uses the fourteen stock indices as the sample and then utilizes eight parametric volatili...