scholarly journals Asymptotic Inference about Predictive Accuracy Using High Frequency Data

2013 ◽  
Author(s):  
Jia Li ◽  
Andrew J. Patton
Keyword(s):  
High Frequency ◽  
Predictive Accuracy ◽  
High Frequency Data ◽  
Frequency Data ◽  
Asymptotic Inference

scholarly journals Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data

2018 ◽  
Vol 9 (2) ◽  
pp. 18-34 ◽  
Author(s):  
Josip Arnerić ◽  
Tea Poklepović ◽  
Juin Wen Teai
Keyword(s):  
High Frequency ◽  
Predictive Accuracy ◽  
Sampling Frequency ◽  
High Frequency Data ◽  
Nonlinear Behaviour ◽  
Optimal Sampling ◽  
Frequency Data ◽  
Realized Variance ◽  
Neural Network Approach ◽  
Out Of Sample

Abstract Background: Since high-frequency data have become available, an unbiased volatility estimator, i.e. realized variance (RV) can be computed. Commonly used models for RV forecasting suffer from strong persistence with a high sensitivity to the returns distribution assumption and they use only daily returns. Objectives: The main objective is measurement and forecasting of RV. Two approaches are compared: Heterogeneous AutoRegressive model (HAR-RV) and Feedforward Neural Networks (FNNs). Even though HAR-RV-type models describe RV stylized facts very well, they ignore its nonlinear behaviour. Therefore, FNN-HAR-type models are developed. Methods/Approach: Firstly, an optimal sampling frequency with application to the DAX index is chosen. Secondly, in and out of sample predictions within HAR models and FNNs are compared using RMSE, AIC, the Wald test and the DM test. Weights of FNN-HAR-type models are estimated using the BP algorithm. Results: The optimal sampling frequency of RV is 10 minutes. Within HAR-type models, HAR-RV-J has better, but not significant, forecasting performances, while FNN-HAR-J and FNNLHAR- J have significantly better predictive accuracy in comparison to the FNN-HAR model. Conclusions: Compared to the traditional ones, FNN-HAR-type models are better in capturing nonlinear behaviour of RV. FNN-HAR-type models have better accuracy compared to traditional HAR-type models, but only on the sample data, whereas their out-of-sample predictive accuracy is approximately equal.

scholarly journals Predictive accuracy of option pricing models considering high-frequency data

2021 ◽  
Vol 34 (1) ◽  
pp. 131-144
Author(s):  
Josip Arnerić ◽  
Maria Čuljak
Keyword(s):  
Option Pricing ◽  
Probability Density ◽  
Density Function ◽  
High Frequency ◽  
Predictive Accuracy ◽  
High Frequency Data ◽  
Frequency Data ◽  
True Density ◽  
Pricing Models ◽  
Out Of Sample

Purpose: Recently, considerable attention has been given to forecasting, not only the mean and the variance, but also the entire probability density function (pdf) of the underlying asset. These forecasts can be obtained as implied moments of future distribution originating from European call and put options. However, the predictive accuracy of option pricing models is not so well established. With this in mind, this research aims to identify the model that predicts the entire pdf most accurately when compared to the ex-post “true” density given by high-frequency data at expiration date. Methodology: The methodological part includes two steps. In the first step, several probability density functions are estimated using different option pricing models, considering the values of major market indices with different maturities. These implied probability density functions are risk neutral. In the second step, the implied pdfs are compared against the “true” density obtained from the high-frequency data to examine which one gives the best fit out-of-sample. Results: The results support the idea that a “true” density function, although unknown, can be estimated by employing the kernel estimator within high-frequency data and adjusted for risk preferences. Conclusion: The main conclusion is that the Shimko model outperforms the Mixture Log-Normal model as well as the Edgeworth expansion model in terms of out-of-sample forecasting accuracy. This study contributes to the existing body of research by: i) establishing the benchmark of the “true” density function using high-frequency data, ii) determining the predictive accuracy of the option pricing models and iii) providing applicative results both for market participants and public authorities.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data
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