ABSTRACT
This paper presents a new methodology and a comparative study using past stock market data that can help businesses take investing or divesting decisions in critical situations in the future. These may be like the COVID-19 pandemic, where market volatility is extremely high, thus creating an urgent need for better decision support systems to minimise loss and ensure better profits. The results of the study are based on the comparison of different configurations of ARIMAX, Prophet, LSTM and Bidirectional LSTM Models trained on historical NSE data. By understanding the correlation and variations in the data processing and model training parameters, we have successfully proposed a LSTM neural network model training and optimising method which could successfully help businesses take both long and short term profitable decisions before and after big financial and market crises with a respective accuracy of 98.60 percent and 96.97 percent. © 2022 IEEE.
ABSTRACT
This paper compares several methods for constructing weekly nowcasts of recession probabilities in Italy, with a focus on the most recent period of the Covid-19 pandemic. The common thread of these methods is that they use, in different ways, the information content provided by financial market data. In particular, a battery of probit models are estimated after extracting information from a large dataset of more than 130 financial market variables observed at a weekly frequency. The accuracy of these models is explored in a pseudo out-of-sample nowcasting exercise. The results demonstrate that nowcasts derived from probit models estimated on a large set of financial variables are, on average, more accurate than those delivered by standard probit models estimated on a single financial covariate, such as the slope of the yield curve. The proposed approach performs well even compared with probit models estimated on single time series of real economic activity variables, such as industrial production, business tendency survey data or composite PMI indicators. Overall, the financial indicators used in this paper can be easily updated as soon as new data become available on a weekly basis, thus providing reliable early estimates of the Italian business cycle. © 2023 John Wiley & Sons Ltd.
ABSTRACT
Oceans are very vital for humans because holding 97% of water, 70% of the oxygen we breathe, ecosystem, food, energy, trade and leisure. The globalized maritime industry with more than 74,000 merchant ships transporting 90% of the world's cargo with around 1.89 million seafarers. The world will be experiencing a few megatrends demanding high skilled workforce. Sustainable development is impossible without upskilled force. LMD is always changing, attributable to demand and supply, matching efficiency, innovations, high-tech systems, education level, productivity, unemployment etc. Maritime labour market data shows a decline in job offers. Supply and demand affected during recent times due to Covid -19 pandemic and the Russian/Ukraine conflict. This paper highlights MLMD and SGML and suggests a futuristic approach for remodeling maritime labour skills. A survey through IAMU member universities will present a very clear picture of the issue. Paper suggests approaching IMO/IAMU to introduce MLMS Course in collaboration with the ILO and other maritime stakeholders. It also suggests IAMU Maritime Skilled Labour Data Program (MSLDP), IAMU Maritime Labour Market Data Program (MLMDP), and IAMU Maritime Skilled Labour Standards (MSLS) according to maritime industry requirements. © 2022 IAMUC. All Rights Reserved.
ABSTRACT
Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models is more accurate than others. The focus is on the German intraday market for electricity. We want to find out whether the COVID-19 crisis has an influence on the price development. This paper includes a comprehensive review between Germany, France and Norway (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyse the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions (i) we reviewed the market data linked to COVID-19. We expected a higher volatility in the lockdowns than before and therefore decrease in efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer the question (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis we use data visualization and statistical models as well as sample and out-of-sample data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
During the SARS-CoV-2 (COIVD-19) outbreak, China repeatedly stressed that the response to the pandemic required action at all levels of government, including the issuance of Pandemic Bonds to help the country return to work and production. However, studies on the effectiveness of Pandemic Bonds during that period are rare. Starting with China's national financial bond market data after COVID-19 in 2020, this paper focuses on the correlation between the Credit Spreads of the relevant bonds and the corresponding bond market rate of return, based on the Copula model. The empirical analysis is also carried out for multiple dimensional groupings such as enterprises, industries, provinces, and bond maturities. The results show that there is a significant positive correlation between the Credit Spreads of Pandemic Bonds and market returns. In addition, the market correlation is higher for Pandemic Bonds issued in Hubei Province, which is at the center of the 2020 pandemic, and the shorter the maturity of the Pandemic Bond issued, the stronger the relationship with market returns. Finally, this paper provides recommendations for financial regulators and policy makers to consider in their decisions on how to build a more resilient financial system under heavy economic, fiscal, and social pressures. © 2020 Tsinghua University Press.
ABSTRACT
Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
ABSTRACT
This article provides a comprehensive summary of selected macroeconomic impacts of the COVID-19 pandemic in the Czech Republic, including an assessment of certain implemented fiscal and monetary policies, using data from 2019 (to compare the development of the economic situation during the COVID-19 pandemic with the period before the onset of the pandemic), 2020 and 2021 on a monthly or quarterly basis. Particular attention is paid to monetary policy effects, which, unlike fiscal policy, the Mundell-Fleming model considers effective in a small open economy with a freely floating exchange rate. The article also investigates the volume of fiscal measures taken to mitigate COVID-19 pandemic effects, the restrictive measures introduced to Czech households and firms as well as labour market developments during the period of 2019-2021, including quantification of the aggregate labour productivity index. The conclusions of the article are that, during the COVID-19 pandemic, macroeconomic indicators in the Czech Republic acted in accordance with the established partial hypotheses of the Mundell-Fleming model and in accordance with the hypothesis of the modified Phillips curve. Possible causes of the significant increase in inflation since September 2021 include 2020 nominal public and private sector salary growth, which showed faster growth than aggregate labour productivity, and the highly expansionary fiscal policy that characterized the 2021 pre-election period.
ABSTRACT
Because macroeconomic data is published with a substantial delay, assessing the health of the economy during the rapidly evolving COVID-19 crisis is challenging. We develop a fever curve for the Swiss economy using publicly available daily financial market and news data. The indicator can be computed with a delay of 1 day. Moreover, it is highly correlated with macroeconomic data and survey indicators of Swiss economic activity. Therefore, it provides timely and reliable warning signals if the health of the economy takes a turn for the worse.