Instantaneous Volatility Seasonality of Bitcoin in Directional-Change Intrinsic Time

2018 ◽  
Author(s):  
Vladimir Petrov ◽  
Anton Golub ◽  
Richard B. Olsen
Keyword(s):  
Intrinsic Time ◽  
Directional Change ◽  
Instantaneous Volatility

scholarly journals Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time

2019 ◽  
Vol 12 (2) ◽  
pp. 54 ◽  
Author(s):  
Vladimir Petrov ◽  
Anton Golub ◽  
Richard Olsen
Keyword(s):  
High Frequency ◽  
Estimation Method ◽  
Management Tool ◽  
Stock Market Index ◽  
Intrinsic Time ◽  
Market Index ◽  
Relative Value ◽  
Directional Change ◽  
High Frequency Activity ◽  
Instantaneous Volatility

We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneous volatility computed in directional-change intrinsic time. The provided volatility estimation method can be adapted as a universal multiscale risk-management tool independent of the discreteness and the type of analysed high-frequency data.

Agent-Based Model in Directional-Change Intrinsic Time

2018 ◽  
Author(s):  
Vladimir Petrov ◽  
Anton Golub ◽  
Richard B. Olsen
Keyword(s):  
Agent Based Model ◽  
Agent Based ◽  
Intrinsic Time ◽  
Directional Change

Agent-based modelling in directional-change intrinsic time

2019 ◽  
Vol 20 (3) ◽  
pp. 463-482
Author(s):  
V. Petrov ◽  
A. Golub ◽  
R. Olsen
Keyword(s):  
Agent Based ◽  
Agent Based Modelling ◽  
Intrinsic Time ◽  
Directional Change

Intrinsic Time Directional-Change Methodology in Higher Dimensions

2019 ◽  
Author(s):  
Vladimir Petrov ◽  
Anton Golub ◽  
Richard B. Olsen
Keyword(s):  
Higher Dimensions ◽  
Intrinsic Time ◽  
Directional Change

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

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