Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

Convolution Properties I

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

ChatMDV: Reducing Technical Barriers in Bioinformatics Analysis using Large Language Models.

GigaScience·2026

Same author

Understanding stock market instability via graph auto-encoders.

EPJ data science·2025

Same author

Asynchronous Deep Double Dueling Q-learning for trading-signal execution in limit order book markets.

Frontiers in artificial intelligence·2023

Same author

On Sequential Bayesian Inference for Continual Learning.

Entropy (Basel, Switzerland)·2023

Same author

Time-series forecasting with deep learning: a survey.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2021

Same author

Sentiment correlation in financial news networks and associated market movements.

Scientific reports·2021

Same journal

Deep limit order book forecasting: a microstructural guide.

Quantitative finance·2025

Same journal

Cross-section without factors: a string model for expected returns.

Quantitative finance·2025

Same journal

Are missing values important for earnings forecast? a machine learning perspective.

Quantitative finance·2022

Same journal

Sparse Index Clones via the sorted <i>ℓ</i> <sub>1</sub> - Norm.

Quantitative finance·2022

Same journal

Calibration to American options: numerical investigation of the de-Americanization method.

Quantitative finance·2018

Same journal

A semiparametric graphical modelling approach for large-scale equity selection.

Quantitative finance·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

DeepVol: volatility forecasting from high-frequency data with dilated causal convolutions.

Fernando Moreno-Pino^1,2, Stefan Zohren^1,3

¹Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxford, UK.

Quantitative Finance

|October 16, 2024

Summary

This summary is machine-generated.

DeepVol, a novel deep learning model, forecasts equity volatility using high-frequency financial data. This approach enhances prediction accuracy by effectively utilizing intraday information, outperforming traditional methods for better risk management.

Keywords:

Deep learning Dilated causal convolutions High-frequency data Realised volatility Volatility forecasting

More Related Videos

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Related Experiment Videos

Last Updated: Jun 10, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

Area of Science:

Quantitative Finance
Machine Learning
Financial Econometrics

Background:

Volatility forecasting is crucial for equity risk assessment.
Traditional statistical models and machine learning techniques are used for daily time-series volatility prediction.
High-frequency intraday data can improve volatility predictions.

Purpose of the Study:

To propose DeepVol, a novel model using Dilated Causal Convolutions for day-ahead volatility forecasting.
To leverage high-frequency intraday data for enhanced volatility prediction accuracy.
To demonstrate the effectiveness of deep learning in capturing predictive information from financial time-series.

Main Methods:

Utilized Dilated Causal Convolutions for time-series analysis.
Employed high-frequency intraday financial data from NASDAQ-100 over two years.
Evaluated DeepVol's performance against traditional methodologies.

Main Results:

Dilated convolutional filters effectively extract relevant information from intraday financial time-series.
DeepVol successfully leverages predictive information present in high-frequency data.
The model avoids limitations of daily data models, such as model misspecification and handcrafted features.

Conclusions:

DeepVol, a deep learning-based approach, accurately learns global features from high-frequency data.
The proposed model yields more accurate volatility predictions compared to traditional methods.
DeepVol contributes to producing more reliable equity risk measures.