Using VIX Entropy Indicators for Style Rotation Timing

Efremidze, Levan; DiLellio, James; Stanley, Darrol J.

doi:10.3905/joi.2014.23.3.130

Cited by 10 publications

(2 citation statements)

References 15 publications

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“…Moreover, transfer entropybased methods yield state-of-the-art results in detecting information flows in computational neuroscience, bioinformatics, and financial economics [37]. Specific financial applications include estimation of serial irregularities and risk in time series or returns and inference of the global dependence networks of financial indices [20,[38][39][40][41][42]. We expand on previous results by investigating the evolution of dynamic causal networks (sometimes referred to as information flow networks) through time and analyzing their association with systemic risk in the market.…”

Section: Introductionmentioning

confidence: 99%

Information Feedback in Temporal Networks as a Predictor of Market Crashes

et al. 2018

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In complex systems, statistical dependencies between individual components are often considered one of the key mechanisms which drive the system dynamics observed on a macroscopic level. In this paper, we study cross-sectional time-lagged dependencies in financial markets, quantified by nonparametric measures from information theory, and estimate directed temporal dependency networks in financial markets. We examine the emergence of strongly connected feedback components in the estimated networks, and hypothesize that the existence of information feedback in financial networks induces strong spatiotemporal spillover effects and thus indicates systemic risk. We obtain empirical results by applying our methodology on stock market and real estate data, and demonstrate that the estimated networks exhibit strongly connected components around periods of high volatility in the markets. To further study this phenomenon, we construct a systemic risk indicator based on the proposed approach, and show that it can be used to predict future market distress. Results from both the stock market and real estate data suggest that our approach can be useful in obtaining early-warning signals for crashes in financial markets.

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Section: Introductionmentioning

confidence: 99%

Information Feedback in Temporal Networks as a Predictor of Market Crashes

et al. 2018

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“…Recent financial markets research has focused on applying markers of complex systems to different segments of financial markets. Entropy is a statistical measure of randomness in a complex system and it shows reasonable effectiveness in timing a stock market index (Efremidze, Stanley, & Kinsman, 2015) and style rotation strategies (Efremidze, DeLillio, & Stanley, 2014). Entropy based methods have proven to be a powerful tool in physics, astronomy, biology, and medicine (Pincus, 2008).…”

Section: Introductionmentioning

confidence: 99%

Entropy Risk Factor Model of Exchange Rate Prediction

Stanley¹,

Efremidze²,

Rossouw³

2017

IJFR

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We investigate the predictability of an exchange rate with entropy risk factor model, as there is growing evidence that financial markets behave as complex systems. The model is tested on the data of South African Rand (ZAR) exchange rate for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. The results raise the potential attractiveness of complex systems analyses, especially the methods of entropy, for foreign exchange market research and applications.

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Profits Are in the Eyes of the Beholder: Entropy-Based Volatility Indicators and Portfolio Rotation Strategies

Chandra

Jadhao

2021

Computational Management

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Using VIX Entropy Indicators for Style Rotation Timing

Cited by 10 publications

References 15 publications

Information Feedback in Temporal Networks as a Predictor of Market Crashes

Information Feedback in Temporal Networks as a Predictor of Market Crashes

Entropy Risk Factor Model of Exchange Rate Prediction

Profits Are in the Eyes of the Beholder: Entropy-Based Volatility Indicators and Portfolio Rotation Strategies

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