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 TIME-VARYING BETA AND VOLATILITY IN THE KUALA LUMPUR STOCK EXCHANGE

2004 ◽  
Vol 6 (1) ◽  
pp. 117 ◽  
Author(s):  
Mansor Ibrahim
Keyword(s):  
Stock Exchange ◽  
Abnormal Returns ◽  
Market Volatility ◽  
Market Model ◽  
Kuala Lumpur ◽  
Upward Trend ◽  
Daily Data ◽  
Beta Risk ◽  
The Relationship ◽  
Portfolio Investments

The paper analyzes the relationship between beta risk and aggregate market volatility for 12sized-based portfolios for the case of Malaysia using daily data from January 1988 to December 2000. The analysis is conducted for the entire sample as well as various sub-samples corresponding to (i)the upward trend in the market from January 1988-December 1992; (ii) the huge influx of portfolio investments from January 1993-June 1997, and (Hi) the Asian crisis and its aftermath from July 1997-December 2000. The results generally suggest instability in beta risk due to its significant response to aggregate market volatility. Additionally, we also note that the direction of relationship between beta risk and market volatility seems to depend on stock market conditions or sub-samples used. Namely, beta risk seems to decrease with increasing market volatility for the whole sample as well as the first and the third sub-samples. However, for the second sub-sample, their relationship turns to be positive. Lastly, the author have evidence for the Malaysian case that size does not play significant role in the way beta risk responds to aggregate market volatility. These results have important implications for investment decisions as well as for event analyses employing the market model to generate abnormal returns.

scholarly journals The Effect of the COVID-19 Vaccine on Daily Cases and Deaths Based on Global Vaccine Data

Vaccines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1328
Author(s):  
Zhiwei Li ◽  
Xiangtong Liu ◽  
Mengyang Liu ◽  
Zhiyuan Wu ◽  
Yue Liu ◽  
...  
Keyword(s):  
Random Effects ◽  
Generalized Additive Model ◽  
Meta Analysis ◽  
Additive Model ◽  
Global Pandemic ◽  
Daily Data ◽  
Fair Distribution ◽  
The Impact ◽  
The Relationship

Background: Coronavirus disease 2019 (COVID-19), a global pandemic, has caused over 216 million cases and 4.50 million deaths as of 30 August 2021. Vaccines can be regarded as one of the most powerful weapons to eliminate the pandemic, but the impact of vaccines on daily COVID-19 cases and deaths by country is unclear. This study aimed to investigate the correlation between vaccines and daily newly confirmed cases and deaths of COVID-19 in each country worldwide. Methods: Daily data on firstly vaccinated people, fully vaccinated people, new cases and new deaths of COVID-19 were collected from 187 countries. First, we used a generalized additive model (GAM) to analyze the association between daily vaccinated people and daily new cases and deaths of COVID-19. Second, a random effects meta-analysis was conducted to calculate the global pooled results. Results: In total, 187 countries and regions were included in the study. During the study period, 1,011,918,763 doses of vaccine were administered, 540,623,907 people received at least one dose of vaccine, and 230,501,824 people received two doses. For the relationship between vaccination and daily increasing cases of COVID-19, the results showed that daily increasing cases of COVID-19 would be reduced by 24.43% [95% CI: 18.89, 29.59] and 7.50% [95% CI: 6.18, 8.80] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Daily increasing deaths of COVID-19 would be reduced by 13.32% [95% CI: 3.81, 21.89] and 2.02% [95% CI: 0.18, 4.16] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Conclusions: These findings showed that vaccination can effectively reduce the new cases and deaths of COVID-19, but vaccines are not distributed fairly worldwide. There is an urgent need to accelerate the speed of vaccination and promote its fair distribution across countries.

scholarly journals Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 513
Author(s):  
Xerxes Seposo ◽  
Chris Fook Sheng Ng ◽  
Lina Madaniyazi
Keyword(s):  
Model Fitting ◽  
Additive Model ◽  
The Philippines ◽  
Air Pressure ◽  
Dew Point ◽  
Outcome Variable ◽  
Climatic Data ◽  
Time Varying ◽  
Rt Pcr ◽  
National Climatic Data Center

The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius; °C), dew point (degrees Celsius; °C), relative humidity (percent; %), air pressure (kilopascal; kPa), windspeed (meters per second; m/s) and visibility (kilometer; km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform.

scholarly journals Changes in ambient temperature increase hospital outpatient visits for allergic rhinitis in Xinxiang, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianhui Gao ◽  
Mengxue Lu ◽  
Yinzhen Sun ◽  
Jingyao Wang ◽  
Zhen An ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Ambient Temperature ◽  
Positive Association ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Hospital Outpatient ◽  
Distributed Lag ◽  
Outpatient Visits ◽  
Daily Data ◽  
The Relationship

Abstract Background The effect of ambient temperature on allergic rhinitis (AR) remains unclear. Accordingly, this study aimed to explore the relationship between ambient temperature and the risk of AR outpatients in Xinxiang, China. Method Daily data of outpatients for AR, meteorological conditions, and ambient air pollution in Xinxiang, China were collected from 2015 to 2018. The lag-exposure-response relationship between daily mean temperature and the number of hospital outpatient visits for AR was analyzed by distributed lag non-linear model (DLNM). Humidity, long-time trends, day of the week, public holidays, and air pollutants including sulfur dioxide (SO2), and nitrogen dioxide (NO2) were controlled as covariates simultaneously. Results A total of 14,965 AR outpatient records were collected. The relationship between ambient temperature and AR outpatients was generally M-shaped. There was a higher risk of AR outpatient when the temperature was 1.6–9.3 °C, at a lag of 0–7 days. Additionally, the positive association became significant when the temperature rose to 23.5–28.5 °C, at lag 0–3 days. The effects were strongest at the 25th (7 °C) percentile, at lag of 0–7 days (RR: 1.32, 95% confidence intervals (CI): 1.05–1.67), and at the 75th (25 °C) percentile at a lag of 0–3 days (RR: 1.15, 95% CI: 1.02–1.29), respectively. Furthermore, men were more sensitive to temperature changes than women, and the younger groups appeared to be more influenced. Conclusions Both mild cold and mild hot temperatures may significantly increase the risk of AR outpatients in Xinxiang, China. These findings could have important public health implications for the occurrence and prevention of AR.

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.

scholarly journals The relationship between SARS-COV-2 RNA positive duration and the risk of recurrent positive

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hong Zhao ◽  
Chi Zhang ◽  
Xian-Xiang Chen ◽  
Qi Zhu ◽  
Wen-Xiang Huang ◽  
...  
Keyword(s):  
Curve Fitting ◽  
Additive Model ◽  
Smooth Curve ◽  
Fold Increase ◽  
Monocyte Count ◽  
Increased Risk ◽  
Adjusted Logistic Regression ◽  
Multi Center Study ◽  
Adjusted Model ◽  
The Relationship

Abstract Background The management of discharge COVID-19 patients with recurrent positive SARS-CoV-2 RNA is challenging. However, there are fewer scientific dissertations about the risk of recurrent positive. The aim of this study was to explore the relationship between SARS-COV-2 RNA positive duration (SPD) and the risk of recurrent positive. Methods This case–control multi-center study enrolled participants from 8 Chinese hospital including 411 participants (recurrent positive 241). Using unadjusted and multivariate-adjusted logistic regression analyses, generalized additive model with a smooth curve fitting, we evaluated the associations between SPD and risk of recurrent positive. Besides, subgroup analyses were performed to explore the potential interactions. Results Among recurrent positive patients, there were 121 females (50.2%), median age was 50 years old [interquartile range (IQR): 38–63]. In non-adjusted model and adjusted model, SPD was associated with an increased risk of recurrent positive (fully-adjusted model: OR = 1.05, 95% CI: 1.02–1.08, P = 0.001); the curve fitting was not significant (P = 0.286). Comparing with SPD < 14 days, the risk of recurrent positive in SPD > 28 days was risen substantially (OR = 3.09, 95% CI: 1.44–6.63, P = 0.004). Interaction and stratified analyses showed greater effect estimates of SPD and risk of recurrent positive in the hypertension, low monocyte count and percentage patients (P for interaction = 0.008, 0.002, 0.036, respectively). Conclusion SPD was associated with a higher risk of recurrent positive and especially SPD > 28 day had a two-fold increase in the relative risk of re-positive as compared with SPD < 14 day. What’s more, the risk may be higher among those with hypertension and lower monocyte count or percentage.

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