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.
Currently the most popular method of estimating volatility is the implied volatility. It is calculat...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
In this research, different models are used to construct volatility surfaces and these models are co...
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...
Volatility prediction, a central issue in financial econometrics, attracts increasing attention in ...
Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. ...
Extensive research has been done within the field of finance to better predict future volatility and...
This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and dail...
Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The ...
An appropriate calibration and forecasting of volatility and market risk are some of the main challe...
The ability to obtain accurate volatility forecasts is an important issue for the financial analyst....
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
This paper reviews the exciting and rapidly expanding literature on realized volatility. After prese...
In the last few decades, a broad strand of literature in finance has implemented artificial neural ...
Currently the most popular method of estimating volatility is the implied volatility. It is calculat...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
In this research, different models are used to construct volatility surfaces and these models are co...
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...
Volatility prediction, a central issue in financial econometrics, attracts increasing attention in ...
Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. ...
Extensive research has been done within the field of finance to better predict future volatility and...
This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite index returns and dail...
Accurately forecasting multivariate volatility plays a crucial role for the financial industry. The ...
An appropriate calibration and forecasting of volatility and market risk are some of the main challe...
The ability to obtain accurate volatility forecasts is an important issue for the financial analyst....
It is shown that time series about financial market variables are highly nonlinearly dependent on ti...
This paper reviews the exciting and rapidly expanding literature on realized volatility. After prese...
In the last few decades, a broad strand of literature in finance has implemented artificial neural ...
Currently the most popular method of estimating volatility is the implied volatility. It is calculat...
Cryptocurrencies are known for their high fluctuating prices. In order to minimize the risk for inve...
In this research, different models are used to construct volatility surfaces and these models are co...