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.

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 An Option Pricing Model Using High Frequency Data

2016 ◽  
Vol 91 ◽  
pp. 175-179
Author(s):  
Saebom Jeon ◽  
Won Chang ◽  
Yousung Park
Keyword(s):  
Option Pricing ◽  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Pricing Model ◽  
Option Pricing Model

scholarly journals Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

2006 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Marcelo C. Carvalho ◽  
Marco Aurélio S. Freire ◽  
Marcelo Cunha Medeiros ◽  
Leonardo R. Souza
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Value At Risk ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Normality Assumption ◽  
Out Of Sample ◽  
Management Area

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

Direct Versus Iterated Multiperiod Volatility Forecasts

2019 ◽  
Vol 11 (1) ◽  
pp. 173-195
Author(s):  
Eric Ghysels ◽  
Alberto Plazzi ◽  
Rossen Valkanov ◽  
Antonio Rubia ◽  
Asad Dossani
Keyword(s):  
High Frequency ◽  
Mixed Data ◽  
High Frequency Data ◽  
Data Sampling ◽  
Frequency Data ◽  
Major Focus ◽  
Out Of Sample ◽  
Mixed Data Sampling ◽  
Short Horizon ◽  
The Empirical Analysis

Multiperiod-ahead forecasts of returns’ variance are used in most areas of applied finance where long-horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this review, we compare several approaches of producing multiperiod-ahead forecasts within the generalized autoregressive conditional heteroscedastic (GARCH) and realized volatility (RV) families—iterated, direct, and scaled short-horizon forecasts. We also consider the newer class of mixed data sampling (MIDAS) methods. We carry the comparison on 30 assets, comprising equity, Treasury, currency, and commodity indices. While the underlying data are available at high frequency (5 minutes), we are interested in forecasting variances 5, 10, 22, 44, and 66 days ahead. The empirical analysis, which is performed in sample and out of sample with data from 2005 to 2018, yields the following results: Iterated GARCH dominates the direct GARCH approach, and the direct RV is preferred to the iterated RV. This dichotomy of results emphasizes the need foran approach that uses the richness of high-frequency data and, at the same time, produces a direct forecast of the variance at the desired horizon, without iterating. The MIDAS is such an approach, and unsurprisingly, it yields the most precise forecasts of variance both in and out of sample. More broadly, our study dispels the notion that volatility is not forecastable at long horizons and offers an approach that delivers accurate out-of-sample predictions.

Sign in / Sign up

Export Citation Format

Share Document