scholarly journals Modelling and Forecasting the Volatility of Cryptocurrencies: A Comparison of Nonlinear GARCH-Type Models

2020 ◽  
Vol 11 (4) ◽  
pp. 346
Author(s):  
Huthaifa Alqaralleh ◽  
Alaa Adden Abuhommous ◽  
Ahmad Alsaraireh
Keyword(s):  
Heavy Tails ◽  
Risk Return ◽  
High Volatility ◽  
Mean Reverting Process ◽  
Modelling And Forecasting

This study is set out to model and forecast the cryptocurrency market by concentrating on several stylized features of cryptocurrencies. The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies. Finally, it is found that, given the high volatility dynamics present in all cryptocurrencies, correct forecasting could help investors to assess the unique risk-return characteristics of a cryptocurrency, thus helping them to allocate their capital.

scholarly journals Volatility Regime and Equity Portfolio Return: Evidence from Europe

2018 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Chikashi Tsuji
Keyword(s):  
Markov Switching ◽  
Investment Portfolio ◽  
Risk Return ◽  
Markov Switching Models ◽  
Switching Models ◽  
High Volatility ◽  
Volatility Regimes ◽  
Equity Portfolios ◽  
Finance Theory ◽  
European Equity

This paper examines four European equity portfolios sorted by size, book-to-market (B/M) ratios, operating profitability, investment, and momentum by using Markov switching models with high and low volatility regimes. Our empirical analyses derive the following interesting findings. First, in four European equity portfolios, the smallest and the strongest momentum portfolio yields the highest return. In addition, the second smallest and the highest B/M portfolio, the second smallest and the highest operating profitability portfolio, and the second smallest and the second lowest investment portfolio also yield higher returns than the overall equity market in Europe. Further, our analyses using Markov switching models also reveal that for all the four European equity portfolios, the higher returns are obtained not in high volatility regimes but in low volatility regimes, and this evidence is against the assumption of risk-return trade off advocated in standard finance theory. Finally, our Markov switching analyses also suggest that for all the four European portfolios, staying probabilities in the same regimes are high and switching probabilities between two different regimes are generally low. In particular, staying probabilities in low volatility regimes are rather high, thus, all the four European equity portfolios yield high returns very stably by staying high return regimes.

scholarly journals An Investigation of Low Volatility Anomaly in Indian Stock Market

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Sitaram Pandey ◽  
Amitava Samanta
Keyword(s):  
Stock Market ◽  
Direct Relationship ◽  
Empirical Studies ◽  
International Markets ◽  
Risk Return ◽  
Indian Context ◽  
Normal Behavior ◽  
High Volatility ◽  
Capm Model ◽  
Indian Stock Market

<div><p><em>The objective of this paper is to investigate the presence of low volatility anomaly in Indian stock Market. Anomaly occurs due to the deviation in normal behavior of stocks with respect to risk-return relationship as suggested by CAPM theory. The low volatility anomaly means that portfolio of low volatility stocks provides better returns than the portfolio of high volatility stocks. The anomaly under study is one of the most common technical anomalies detected in various International markets but very few studies are there in Indian context. CAPM theory suggest that there is direct relationship between risk &amp; return but various empirical studies finds that portfolio of low volatility stocks outperforms portfolio of high volatility stocks. In this study, returns of volatility sorted portfolios are used to analyze the low volatility anomaly. The study uses constituent stocks of S&amp;P CNX 100 index of NSE. The data is taken from period January 2001 to December 2014.  The results of this study did not found   any significant low volatility anomaly in India rather results are supporting the CAPM model and thus found that high volatility quintile gives high returns in India and vice-versa.</em></p></div>

Explaining the Risk/Return Mismatch of the MSCI China Index: A Systematic Risk Analysis

2007 ◽  
Vol 10 (01) ◽  
pp. 63-80 ◽  
Author(s):  
Priscilla Liang
Keyword(s):  
Risk Analysis ◽  
Systematic Risk ◽  
Global Level ◽  
Total Risk ◽  
Risk Decomposition ◽  
Risk Return ◽  
Systematic Risks ◽  
High Volatility ◽  
Global Investors ◽  
Country Specific

This study examines a risk/return mismatch of the MSCI China Index, which has offered investors low returns and high volatility, yet remains a favorite within the global investors' portfolio. The paper suggests several insights, both from behavioral and traditional finance perspectives, to explain this mismatch. An international risk decomposition model is applied to separate the total risk of China's index return into global systematic risks, regional systematic risks and country specific risks. It suggests the index's lower than average systematic risk might be one of the explanations for its risk/return mismatch. The study also finds that the China Index's systematic risks, both global and regional, have been increasing, but more so at the global level.

Heavy Tails of Discounted Aggregate Claims in the Continuous-Time Renewal Model

2007 ◽  
Vol 44 (02) ◽  
pp. 285-294 ◽  
Author(s):  
Qihe Tang
Keyword(s):  
Asymptotic Formula ◽  
Continuous Time ◽  
Heavy Tails ◽  
Tail Behavior ◽  
Renewal Model ◽  
Discounted Aggregate Claims ◽  
Aggregate Claims ◽  
Tail Asymptotic

We study the tail behavior of discounted aggregate claims in a continuous-time renewal model. For the case of Pareto-type claims, we establish a tail asymptotic formula, which holds uniformly in time.

Risk, Return, and Regulation

CFA Magazine ◽  
2017 ◽  
Vol 28 (4) ◽  
pp. 28-29
Author(s):  
Ralph Wanger
Keyword(s):  
Risk Return

There Is a Risk–Return Trade-Off After All

CFA Digest ◽  
2005 ◽  
Vol 35 (4) ◽  
pp. 71-72
Author(s):  
Frank T. Magiera
Keyword(s):  
Trade Off ◽  
Risk Return

Estimation of Risk-Return Relation Parameters in the Context of the Apt Model

2009 ◽  
Vol 4 (1) ◽  
pp. 26-38
Author(s):  
Małgorzata Kobylińska ◽  
Lesław Markowski
Keyword(s):  
Risk Return ◽  
Estimation Of Risk

Volatility Transmission in Indian Banking Sector

2019 ◽  
Vol 118 (7) ◽  
pp. 161-165
Author(s):  
Cyano Prem ◽  
Dr M. Babu ◽  
C. Hariharan ◽  
R. Muneeswaran
Keyword(s):  
Financial Markets ◽  
Banking Sector ◽  
Bad News ◽  
Private Bank ◽  
Volatility Transmission ◽  
The People ◽  
New Information ◽  
High Volatility ◽  
The Government ◽  
Awareness Programme

Any new information about the economy is transmitted fast and it may influence the financial markets, positively or negatively. The present study used GARCH (1, 1) and EGARCH models, to investigate the volatility of Indian banking sectors indices, namely, Nifty PSU Index and Nifty Private Bank Index of NSE India Ltd. The result of the study confirmed that the high volatility was found in both the bank indices. At the same time, negative information about Indian economics did affect the PSU and Private Bank Sector indices during the study period. Finally, the study concluded that bad news travels fast and it increased volatility more than good. Hence the Government should give more information and awareness programme to the people before the implementation of any economic policy.

A Single Period Stochastic Model for Maximising Firms Value

GIS Business ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. 39-45
Author(s):  
J. P. Singh
Keyword(s):  
Capital Market ◽  
Firm Value ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Asset Pricing Model ◽  
Value Maximization ◽  
Risk Return ◽  
Bankruptcy Costs ◽  
Single Period ◽  
Debt Capacity

This article sets up a single period value maximization model for the firm based on stochastic end-of-period cash inflows, stochastic bankruptcy costs and taxes based on income rather than wealth. The risk-return trade-off is captured in the Capital Asset Pricing Model. Thus, the model also assumes a perfect capital market and market equilibrium. The model establishes the existence of a unique optimal financial leverage at which the firm value is maximized, this leverage being less than the maximum debt capacity of the firm.

Sign in / Sign up

Export Citation Format

Share Document