A Stochastic Analysis of the Effect of Trading Parameters on the Stability of the Financial Markets Using a Bayesian Approach
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Mensi, Walid & Sensoy, Ahmet & Aslan, Aylin & Kang, Sang Hoon, 2019. "High-frequency asymmetric volatility connectedness between Bitcoin and major precious metals markets," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
- Young Bin Kim & Jun Gi Kim & Wook Kim & Jae Ho Im & Tae Hyeong Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
- Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018.
"Informational efficiency of Bitcoin—An extension,"
Economics Letters, Elsevier, vol. 163(C), pages 106-109.
- Aviral Kumar Tiwari & R.K. Jana & Debojyoti Das & David Roubaud, 2018. "Informational efficiency of Bitcoin—An extension," Post-Print hal-02091763, HAL.
- Sensoy, Ahmet, 2019. "The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies," Finance Research Letters, Elsevier, vol. 28(C), pages 68-73.
- Lahmiri, Salim & Bekiros, Stelios, 2020. "Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
- Mnif, Emna & Jarboui, Anis & Mouakhar, Khaireddine, 2020. "How the cryptocurrency market has performed during COVID 19? A multifractal analysis," Finance Research Letters, Elsevier, vol. 36(C).
- Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
- Gil Cohen, 2021. "Trading Cryptocurrencies Using Second Order Stochastic Dominance," Mathematics, MDPI, vol. 9(22), pages 1-10, November.
- Zi Ye & Yinxu Wu & Hui Chen & Yi Pan & Qingshan Jiang, 2022. "A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin," Mathematics, MDPI, vol. 10(8), pages 1-21, April.
- Νikolaos A. Kyriazis & Paraskevi Prassa, 2019. "Which Cryptocurrencies Are Mostly Traded in Distressed Times?," JRFM, MDPI, vol. 12(3), pages 1-12, August.
- Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Larissa Batrancea, 2021. "The Influence of Liquidity and Solvency on Performance within the Healthcare Industry: Evidence from Publicly Listed Companies," Mathematics, MDPI, vol. 9(18), pages 1-15, September.
- Yi, Eojin & Ahn, Kwangwon & Choi, M.Y., 2022. "Cryptocurrency: Not far from equilibrium," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
- Aamir Aijaz Syed & Farhan Ahmed & Muhammad Abdul Kamal & Assad Ullah & Jose Pedro Ramos-Requena, 2022. "Is There an Asymmetric Relationship between Economic Policy Uncertainty, Cryptocurrencies, and Global Green Bonds? Evidence from the United States of America," Mathematics, MDPI, vol. 10(5), pages 1-15, February.
- Mircea Constantin Șcheau & Simona Liliana Crăciunescu & Iulia Brici & Monica Violeta Achim, 2020. "A Cryptocurrency Spectrum Short Analysis," JRFM, MDPI, vol. 13(8), pages 1-16, August.
- Nader Trabelsi, 2018. "Are There Any Volatility Spill-Over Effects among Cryptocurrencies and Widely Traded Asset Classes?," JRFM, MDPI, vol. 11(4), pages 1-17, October.
- Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
- Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
- Larissa Batrancea & Marcel Ciprian Pop & Malar Maran Rathnaswamy & Ioan Batrancea & Mircea-Iosif Rus, 2021. "An Empirical Investigation on the Transition Process toward a Green Economy," Sustainability, MDPI, vol. 13(23), pages 1-12, November.
- Larissa Batrancea, 2021. "Empirical Evidence Regarding the Impact of Economic Growth and Inflation on Economic Sentiment and Household Consumption," JRFM, MDPI, vol. 14(7), pages 1-16, July.
- Jinxin Cui & Aktham Maghyereh, 2022. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
- Mark A. Beaumont & Jean-Marie Cornuet & Jean-Michel Marin & Christian P. Robert, 2009. "Adaptive approximate Bayesian computation," Biometrika, Biometrika Trust, vol. 96(4), pages 983-990.
- David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Artur Meynkhard, 2019. "Energy Efficient Development Model for Regions of the Russian Federation: Evidence of Crypto Mining," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 16-21.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
- Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
- Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
- Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
- Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
- Nikolaos A. Kyriazis, 2019. "A Survey on Empirical Findings about Spillovers in Cryptocurrency Markets," JRFM, MDPI, vol. 12(4), pages 1-17, November.
- Irena Barjav{s}i'c & Nino Antulov-Fantulin, 2020. "Time-varying volatility in Bitcoin market and information flow at minute-level frequency," Papers 2004.00550, arXiv.org, revised Jan 2021.
- Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
- Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021.
"Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis,"
Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
- Aurelio F. Bariviera & Ignasi Merediz-Sol`a, 2020. "Where do we stand in cryptocurrencies economic research? A survey based on hybrid analysis," Papers 2003.09723, arXiv.org.
- Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
- Böyükaslan, Adem & Ecer, Fatih, 2021. "Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework," Technology in Society, Elsevier, vol. 67(C).
- Ángeles Cebrián-Hernández & Enrique Jiménez-Rodríguez, 2021. "Modeling of the Bitcoin Volatility through Key Financial Environment Variables: An Application of Conditional Correlation MGARCH Models," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
- Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
- Akyildirim, Erdinc & Corbet, Shaen & Lucey, Brian & Sensoy, Ahmet & Yarovaya, Larisa, 2020. "The relationship between implied volatility and cryptocurrency returns," Finance Research Letters, Elsevier, vol. 33(C).
- Troster, Victor & Tiwari, Aviral Kumar & Shahbaz, Muhammad & Macedo, Demian Nicolás, 2019. "Bitcoin returns and risk: A general GARCH and GAS analysis," Finance Research Letters, Elsevier, vol. 30(C), pages 187-193.
- Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2019.
"Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
- Walther, Thomas & Klein, Tony & Bouri, Elie, 2018. "Exogenous Drivers of Bitcoin and Cryptocurrency Volatility – A Mixed Data Sampling Approach to Forecasting," QBS Working Paper Series 2018/02, Queen's University Belfast, Queen's Business School.
- Yi, Eojin & Ahn, Kwangwon & Choi, M.Y., 2022. "Cryptocurrency: Not far from equilibrium," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
- Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
- Nils Bundi & Marc Wildi, 2019. "Bitcoin and market-(in)efficiency: a systematic time series approach," Digital Finance, Springer, vol. 1(1), pages 47-65, November.
More about this item
Keywords
cryptocurrencies; econometric models; stochastic processes; Bayesian analysis; market efficiency; entropy;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2527-:d:1160603. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.