scholarly journals Bitcoin as an Investment and Hedge Alternative. A DCC MGARCH Model Analysis

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 154
Author(s):  
Karl Oton Rudolf ◽  
Samer Ajour El Zein ◽  
Nicola Jackman Lansdowne
Keyword(s):  
Applied Mathematics ◽  
Investor Sentiment ◽  
Garch Model ◽  
Model Analysis ◽  
Stock Index ◽  
Adoption Rate ◽  
Digital Currency ◽  
Return Levels ◽  
Mathematics Research ◽  
Strong Ground

Volatility and investor sentiment have been factors for the slow adoption rate of Bitcoin (BTC) that was first recognized in 2008 as a potential store of value, investment vehicle and a hedge alternative to gold during a recession. The purpose of this applied mathematics study will use a multivariate DCC GARCH model. Bitcoin holds its ground in volatility. This study examines Bitcoin as an investment and hedge alternative to gold as well as the major stock index. To perform the research to explore the viability of Bitcoin as an investment and hedge alternative to gold, the authors conducted a DCC GARCH model analysis. The findings of this research paper confirm Bitcoin’s cyclical performance between volatility and adoption. The findings give a strong ground for Bitcoin as the new digital currency, store of value, medium of exchange, and a unit of account and incentivize further research by theorists, scholars and examiners. The significance of this applied mathematics research and analysis will allow an unstoppable, incorruptible, and uncontrollable store of value, and investment vehicle, without governmental or institutional intervention. This study contributes by comparing and contrasting volatility stability based on the return levels of each Bitcoin on major indexes traded with BTC (based on fiat currencies) and gold.

scholarly journals Stochastic Block Model Reveals Maps of In Applied Mathematics Studies Using VOS Viewer

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Arifin Karim ◽  
Joko Soebagyo ◽  
Sigid Edy Purwanto
Keyword(s):  
Applied Mathematics ◽  
Publishing Research ◽  
Research Trends ◽  
Journal Ranking ◽  
Problem Solution ◽  
Research Results ◽  
Publish Or Perish ◽  
File Formats ◽  
Number Of Publications ◽  
Mathematics Research

Bibliometric analysis is the mapping of research research trends by processing metadata from Google Scolar. The aim is to find out research trends in applied mathematics. The research was conducted on April 30, 2021 through searching the Google Scholar database with the keywords applied mathematics with the publication name journal and the maximum number of results is 500 journals as a sample. Metadata retrieval using the Publish or Perish (POP) application version 7.31. The PoP data were then analyzed descriptively based on the publication year, publisher name, researcher productivity, and journal ranking. To get an accurate map of research developments, PoP data is exported to Exel CSV and Result as RIS file formats. The CSV data was created in a pivot table and the RIS data was analyzed using the VOSViewer (VV) application. The results of the research show that the number of publications of research results in the years 2005-2021 has fluctuated and is mostly published in Elsevier. The most prolific foreign researcher in publishing research results is Biher Bist with 14 articles. The VV visualization shows that the map of the development of applied mathematics research is divided into 5 clusters. Cluster 1 consists of 27 topics, cluster 2 consists of 15 topics, cluster 3 consists of 10 topics, cluster 4 consists of 7 topics, and cluster 5 consists of 3 topics with the most research covering mathematics, paper, problem, solution, system, university, department, science.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

JUMP, NON-NORMAL ERROR DISTRIBUTION AND STOCK PRICE VOLATILITY — A NONPARAMETRIC SPECIFICATION TEST

2009 ◽  
Vol 54 (01) ◽  
pp. 101-121
Author(s):  
MOHAMMAD MASUDUR RAHMAN ◽  
LAILA ARJUMAN ARA ◽  
ZHENLONG ZHENG
Keyword(s):  
Stock Price ◽  
Stock Return ◽  
Garch Model ◽  
Price Volatility ◽  
Stock Index ◽  
Specification Test ◽  
Significance Level ◽  
Student T Distribution ◽  
T Distribution ◽  
Distribution Assumption

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student t-distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from 20 November 1999 to 9 October 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after 27 November 2001. The model introducing GARCH jump effect with normal and Student t-distribution assumption can better fit the volatility characteristics. We find that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

IV. Training In Applied Mathematics Research

SIAM Review ◽  
1967 ◽  
Vol 9 (2) ◽  
pp. 347-365 ◽  
Author(s):  
George F. Carrier
Keyword(s):  
Applied Mathematics ◽  
Mathematics Research
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