Looking Beyond Wine Risk-Adjusted Performance

2020 ◽  
Vol 15 (2) ◽  
pp. 229-259 ◽  
Author(s):  
Frantz Maurer ◽  
Jean-Marie Cardebat ◽  
Linda Jiao
Keyword(s):  
Extreme Events ◽  
Stock Markets ◽  
Secondary Market ◽  
Garch Models ◽  
Time Varying ◽  
Daily Data ◽  
Volatility Asymmetry ◽  
Stock Indexes ◽  
Jel Classifications

AbstractIn this paper, we use copula-GARCH models applied to daily data from March 2010 to March 2018 to test the time-varying dependence of the Liv-ex 50, a secondary market fine wine index comprised of the ten most recent vintages of the five Bordeaux First Growths, with a portfolio composed of the six main stock markets (S&P 500, CAC 40, DAX 30, FTSE 100, and Hang Seng). Our results suggest that the Liv-ex 50 underperforms the six stock indexes, but provides diversification benefits in terms of volatility, asymmetry, and extreme events. (JEL Classifications: G110, G120, Q14)

scholarly journals Do Islamic stock indexes outperform conventional stock indexes? A state space modeling approach

2019 ◽  
Vol 28 (3) ◽  
pp. 301-322 ◽  
Author(s):  
Aymen Ben Rejeb ◽  
Mongi Arfaoui
Keyword(s):  
State Space ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Financial Instability ◽  
Informational Efficiency ◽  
Time Varying ◽  
Content Type ◽  
Volatility Spillovers ◽  
Space Modeling ◽  
Stock Indexes

Purpose The purpose of this paper is to investigate whether Islamic stock indexes outperform conventional stock indexes, in terms of informational efficiency and risk, during the recent financial instability period. Design/methodology/approach The paper uses a state space model combined with a standard GARCH(1,1) specification while taking into account structural breakpoints. The authors allow for efficiency and volatility spillovers to be time-varying and consider break dates to locate periods of financial instability. Findings Empirical results show that Islamic stock indexes are more volatile than their conventional counterparts and are not totally immune to the global financial crisis. As regards of the informational efficiency, the results show that the Islamic stock indexes are more efficient than the conventional stock indexes. Practical implications Resulting evidence of this paper has several implications for international investors who wish to invest in Islamic and/or conventional stock markets. Policy makers and even academics and Sharias researchers should as well take preventive measures in order to ensure the stability of Islamic stock markets during turmoil periods. Overall, prudent risk management and precocious financial practices are relevant and crucial for both Islamic and conventional financial markets. Originality/value The originality of this study is performed by the use of time-varying models for volatility spillovers and informational efficiency. It considers structural break dates that think about the dynamic effect of informational flows on stock markets. The study was developed in a global framework using international data. The global analysis allows avoiding country specific effects.

scholarly journals Testing the conditional correlations and volatility spillovers between US and ASEAN Islamic stock markets: A Multivariate GARCH Analysis

2015 ◽  
Vol 2 (1) ◽  
pp. 029
Author(s):  
Muhammad Rizky Prima Sakti
Keyword(s):  
Stock Markets ◽  
Multivariate Garch ◽  
Garch Models ◽  
Time Varying ◽  
Foreign Investors ◽  
Volatility Spillovers ◽  
The Us ◽  
Conditional Correlations ◽  
Multivariate Garch Models ◽  
Empirical Design

This study examines the conditional correlations and volatility spillovers between the US and ASEAN Islamic stock markets. The empirical design uses MSCI (Morgan Stanley Capital International) Islamic indexes as it adopted stringent restriction to include companies in sharia list. By using a three multivariate GARCH models (BEKK, diagonal VECH, and CCC model), we find evidence of returns and volatility spillovers from the US to the ASEAN Islamic stock markets. However, as the estimated time-varying conditional correlations and volatilities indicate there is still a room for diversification benefits, particularly in the single markets. The Islamic MSCI of Thailand, Indonesia, and Singapore are less correlate to the US MSCI Islamic index. The implication is that foreign investors may benefit from the reduction of risk by adding the Islamic stocks in those countries.

scholarly journals Modeling Mediterranean Stock Markets Volatility with Univariate and Multivariate Approaches

2021 ◽  
Vol 16 ◽  
pp. 457-468
Author(s):  
Saoussan Bouchareb ◽  
Mohamed Salah Chiadmi ◽  
Fouzia Ghaiti
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Stock Markets ◽  
Multivariate Garch ◽  
Garch Models ◽  
Volatility Spillovers ◽  
Daily Data ◽  
Multivariate Approaches ◽  
Multivariate Garch Models

In our study we use the univariate and multivariate GARCH models to analyze the volatility behavior of the daily data of four Mediterranean stock markets (Morocco, Turkey, Spain, and France) spanning the period 2000-2020. We find a strong evidence of persisting of volatility in each of these markets. Results also indicate that both the univariate and the multivariate approaches capture well the ARCH and GARCH effects. We analyze the conditional covariances, and co-volatility spillovers between the Moroccan stock market and the three other Mediterranean stock markets. In order to study co-volatility spillovers, our work is built on the diagonal BEKK model especially the conditional covariances.

IMPROVED DETECTION OF RARE-EVENT RISK OF A PORTFOLIO WITH U.S. REITs

2015 ◽  
Vol 10 (02) ◽  
pp. 1550015 ◽  
Author(s):  
LEH-CHYAN SO ◽  
JUN-YANG YU
Keyword(s):  
Real Estate ◽  
Value At Risk ◽  
Rare Event ◽  
Garch Models ◽  
Time Varying ◽  
Copula Models ◽  
Market Risks ◽  
Event Risk ◽  
Daily Data ◽  
Market Situation

In measuring the market risks of a portfolio, value-at-risk (VaR) is one of the most commonly used tools. In this paper, the copula-generalized autoregressive conditional heteroskedasticity (GARCH) method is used to determine whether it is a better alternative for estimating the VaR of portfolios containing U.S. real estate investment trusts (REITs). The FTSE NAREIT US Real Estate Index, all REITs and the S&P 500 index are used to construct a portfolio. In total, 2800 daily data covering the period of the subprime mortgage crisis of 2007–2009 are used in this paper. We used six constant and two time-varying copula models combined with two GARCH models to form sixteen copula-GARCH models to depict the joint distribution of the two assets in the portfolio. We then computed corresponding one-day VaRs. Compared with the traditional VaR models, our results showed that the time-varying symmetrized Joe–Clayton (SJC) copula model combined with the GARCH Student-t innovation (tvSJC-copula–GARCHt) performed the best, regardless of the market situation. Hence, it could be served as a better way of detecting rare-event risk.

Re-examining covariance risk dynamics in international stock markets using quantile regression analysis

2011 ◽  
Vol 61 (1) ◽  
pp. 33-59 ◽  
Author(s):  
M. Li ◽  
S. Yen
Keyword(s):  
Quantile Regression ◽  
Stock Markets ◽  
Markov Switching ◽  
Garch Models ◽  
Time Varying ◽  
Bear Market ◽  
Markov Switching Models ◽  
International Stock Markets ◽  
Switching Models ◽  
Risk Dynamics

This investigation is one of the first to adopt quantile regression (QR) technique to examine covariance risk dynamics in international stock markets. Feasibility of the proposed model is demonstrated in G7 stock markets. Additionally, two conventional random-coefficient frameworks, including time-varying betas derived from GARCH models and state-varying betas implied by Markov-switching models, are employed and subjected to comparative analysis. The empirical findings of this work are consistent with the following notions. First, the beta smile (beta skew) curve for the Italian, U.S. and U.K. (Canadian, French and German) markets. That is, covariance risk among global stock markets in extremely bull and/or bear market states is significantly higher than in stable periods. Additionally, the Japanese market provides a special case, and its beta estimate at extremely bust state is significantly lower, not higher than that at the middle region. Second, the quantile-varying betas are identified as possessing two key advantages. Specifically, the comparison of the system with quantile-varying betas against that with time-varying betas implied by GARCH models provides meaningful implications for correlation-volatility relationship among international stock markets. Furthermore, the quantile-varying beta design in this study relaxes a simple dual beta setting implied by Markov-switching models of Ramchand — Susmel (1998) and can identify dynamics of asymmetry in betas.

POWER LAW AND STRETCHED EXPONENTIAL EFFECTS OF EXTREME EVENTS IN CHINESE STOCK MARKETS

2010 ◽  
Vol 09 (02) ◽  
pp. 203-217 ◽  
Author(s):  
XIAOJUN ZHAO ◽  
PENGJIAN SHANG ◽  
YULEI PANG
Keyword(s):  
Correlation Analysis ◽  
Power Law ◽  
Extreme Events ◽  
Stock Markets ◽  
Cross Correlation ◽  
Extreme Values ◽  
Cumulative Distribution ◽  
Stretched Exponential ◽  
Cross Correlation Analysis ◽  
Chinese Stock Markets

This paper reports the statistics of extreme values and positions of extreme events in Chinese stock markets. An extreme event is defined as the event exceeding a certain threshold of normalized logarithmic return. Extreme values follow a piecewise function or a power law distribution determined by the threshold due to a crossover. Extreme positions are studied by return intervals of extreme events, and it is found that return intervals yield a stretched exponential function. According to correlation analysis, extreme values and return intervals are weakly correlated and the correlation decreases with increasing threshold. No long-term cross-correlation exists by using the detrended cross-correlation analysis (DCCA) method. We successfully introduce a modification specific to the correlation and derive the joint cumulative distribution of extreme values and return intervals at 95% confidence level.

scholarly journals Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Serdar Neslihanoglu
Keyword(s):  
State Space Model ◽  
Additive Model ◽  
Market Model ◽  
Time Varying ◽  
Daily Data ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Crypto Currency ◽  
Beta Risk ◽  
The Relationship

AbstractThis research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

scholarly journals The time-varying effects of oil prices on oil–gas stock returns of the fragile five countries

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Begüm Yurteri Kösedağlı ◽  
Gül Huyugüzel Kışla ◽  
A. Nazif Çatık
Keyword(s):  
Stock Returns ◽  
Positive Impact ◽  
Structural Break ◽  
Oil Prices ◽  
Time Varying ◽  
Asset Pricing Model ◽  
Daily Data ◽  
Structural Break Test ◽  
The Impact ◽  
Oil Gas

AbstractThis study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study. Moreover, the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market. The results further suggest that, except for Indonesia, oil prices have a positive impact on the sectoral returns of all markets, whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

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