Multimobility and Sharing Economy: Shaping the Future Market Through Policy and Research

2016 ◽  
Author(s):  
Susan Shaheen ◽  
Adam Stocker ◽  
Abhinav Bhattacharyya ◽  
◽  
◽  
...  
Keyword(s):  
Sharing Economy ◽  
Future Market ◽  
The Future

scholarly journals Agricultural Commodity Trading: Is it Destabilizing Spot Markets?

2018 ◽  
Vol 43 (1) ◽  
pp. 47-57 ◽  
Author(s):  
C. P. Gupta ◽  
Sanjay Sehgal ◽  
Sahaj Wadhwa
Keyword(s):  
Causal Relationship ◽  
Trading Volume ◽  
Spot Market ◽  
Market Volatility ◽  
Future Market ◽  
Soy Oil ◽  
Open Interest ◽  
Future Trading ◽  
The Future ◽  
Spot Volatility

Executive Summary The future trading has been held responsible by certain political and interest groups of enhancing speculative trading activities and causing volatility in the spot market, thereby further spiralling up inflation. This study examines the effect of future of trading activity on spot market volatility. The study first determined the Granger causal relationship between unexpected future trading volume and spot market volatility. It then examined the Granger causal relationship between unexpected open interest and spot market volatility. The spot volatility and liquidity was modelled using EGARCH and unexpected trading volume. The expected trading volume and open interest was calculated by using the 21-day moving average, and the difference between actual and expected component was treated as the unexpected trading volume and unexpected open interest. Empirical results confirm that for chickpeas ( channa), cluster bean ( guar seed), pepper, refined soy oil, and wheat, the future (unexpected) liquidity leads spot market volatility. The causal relationship implies that trading volume, which is a proxy for speculators and day traders, is dominant in the future market and leads volatility in the spot market. The results are in conformity with earlier empirical findings — Yang, Balyeat and Leathan (2005) and Nath and Lingareddy (2008) —that future trading destabilizes the spot market for agricultural commodities. Results show that there is no causal relationship between future open interest and spot volatility for all commodities except refined soy oil and wheat. The findings imply that open interest, which is a proxy of hedging activity, is leading to volatility in spot market for refined soy oil and wheat. The results are in conformity to earlier empirical studies that there is a weak causal feedback between future unexpected open interest and volatility in spot market ( Yang et al., 2005 ). For chickpeas (channa), the increase in volatility in the spot market increases trading activity in the future market. The findings are contrary to earlier empirical evidence ( Chatrath, Ramchander, & Song, 1996 ; Yang et al., 2005 ) that increase in spot volatility reduces future trading activity. However, they are in conformity to Chen, Cuny and Haugen (1995) that increase in spot volatility increases future open interest. The results reveal that the future market has been unable to engage sufficient hedging activity. Thereby, a causal relationship exists only for future trading volume and spot volatility, and not for future open interest and spot volatility. The results have major implications for policymakers, investment managers, and for researchers as well. The study contributes to literature on price discovery, spillovers, and price destabilization for Indian commodity markets.

scholarly journals The Analysis of Blocking Behavior and Pricing: The Case of Airbnb

2020 ◽  
pp. 1-28
Author(s):  
Yifeng Peng
Keyword(s):  
Empirical Analysis ◽  
Rapid Growth ◽  
Profit Maximization ◽  
Sharing Economy ◽  
Pricing Strategy ◽  
Pricing Strategies ◽  
Product Pricing ◽  
Starting Point ◽  
The Future ◽  
Resources Sharing

Over the years, as people's lives have improved, our need for transportation and accommodation has increased, driving the rapid growth of the sharing economy. Some well-known network sharing platforms, such as Uber, Drip and Airbnb, provide a large number of convenient options for users with transactional needs, make more use of idle tourism, accommodation and other resources. Sharing economy platforms continue to improve the content and format of their products, but at the same time, the future of sharing platforms and the difficulty of competition is a concern as more platform companies become involved and prices become more transparent. Under this circumstance, optimizing product pricing has become an urgent need for many sharing economy platforms. In this paper, we take Airbnb as the starting point and conduct an empirical analysis of the blocking behavior of homeowners based on proprietary data to explore the factors that affect their product supply. We find that price, number of beds, and listing type all have a significant impact on blocking houses. After that, we conducted further research on price factors and developed a model aiming at profit maximization to obtain the best pricing range for the region and provide suggestions for pricing strategies. Keywords: Sharing Economy, Blocking behavior, Pricing Strategy, Airbnb

The sharing economy: a hedge against recession setbacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Reza Habibi ◽  
Chiranjeev S. Kohli
Keyword(s):  
Best Practices ◽  
Design Methodology ◽  
Data Science ◽  
Science And Technology ◽  
Sharing Economy ◽  
Economic Sector ◽  
Content Type ◽  
Key Characteristics ◽  
The Future ◽  
Conceptual Paper

Purpose This paper aims to provide lessons from the emergence of the sharing economy after the 2008 recession and helps managers prepare more effectively for recessions in the future. Design/methodology/approach In this conceptual paper, the authors build on research on the sharing economy and study the best practices contributing to the sharing economy’s emergence and growth after the 2008 recession. The authors identify the key characteristics of this new economic sector and share lessons that can be used by other companies. Findings The authors recommend five major takeaways: seeking a more flexible supply; actively watching the trends; leveraging customers like employees; using advanced data science and technology like the sharing economy companies; and proactively avoiding panicked responses. This will help companies succeed during recessionary times – and the boom times that follow. Originality/value This is the first paper that, to the best of the authors’ knowledge, investigates the interplay between the sharing economy and recession and highlights practical lessons.

Top three LNG buyers to search for alternative LNG pricing structure after paying a total cost premium of $56 billion for oil-indexed LNG contracts in 2019 and 2020 – how will this affect Australia as the number one supplier to this market?

2021 ◽  
Vol 61 (2) ◽  
pp. 412
Author(s):  
Sindre Knutsson
Keyword(s):  
Natural Gas ◽  
South Korea ◽  
Spot Market ◽  
Liquefied Natural Gas ◽  
The Other ◽  
Future Market ◽  
Total Cost ◽  
The Future

Increasing spreads between spot liquefied natural gas (LNG) and oil-indexed contracts have resulted in the world’s top three LNG buyers paying a cost premium of $33 billion in 2019 and 23 billion in 2020. The top three buyers are Japan, China and South Korea, which had a combined 151Mt of long-term LNG contracts indexed to oil in 2020. This cost premium shows what top Asian buyers are currently paying for the security of LNG supply through long-term oil-indexed contracts. However, it also shows the potential reward Asian buyers have if they manage to develop a liquid LNG pricing hub in Asia to which they can index their contracts. Japanese buyers’ efforts of increasing flexibility in contracts, both through take-or-pay agreements and destination flexibility and aims of growing the spot market, will increasingly support the liquidity of the LNG market. However, there will be resistance from the other side of the table, for where someone is paying a premium, or making a loss, someone is making money. 2020 was another year of plenty for LNG producers selling oil-indexed volumes to Asian markets. Australia is the largest seller of LNG to Japan, China and South Korea with over 60Mt of long-term LNG contracts indexed to oil in 2020. Australia has benefited from having their contracts indexed to oil, but what’s next? In this paper, Rystad Energy will discuss the future market for Australian LNG exports including development in LNG demand, contract trends and price spreads.

scholarly journals Negation of Pythagorean Fuzzy Number Based on a New Uncertainty Measure Applied in a Service Supplier Selection System

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 195
Author(s):  
Haiyi Mao ◽  
Rui Cai
Keyword(s):  
Service Quality ◽  
Fuzzy Number ◽  
Supplier Selection ◽  
Future Market ◽  
Selection System ◽  
Uncertainty Measure ◽  
Good Ability ◽  
Relative Closeness ◽  
The Future ◽  
Market Trends

The Pythagorean fuzzy number (PFN) consists of membership and non-membership as an extension of the intuitionistic fuzzy number. PFN has a larger ambiguity, and it has a stronger ability to express uncertainty. In the multi-criteria decision-making (MCDM) problem, it is also very difficult to measure the ambiguity degree of a set of PFN. A new entropy of PFN is proposed based on a technique for order of preference by similarity to ideal solution (Topsis) method of revised relative closeness index in this paper. To verify the new entropy with a good performance in uncertainty measure, a new Pythagorean fuzzy number negation approach is proposed. We develop the PFN negation and find the correlation of the uncertainty measure. Existing methods can only evaluate the ambiguity of a single PFN. The newly proposed method is suitable to systematically evaluate the uncertainty of PFN in Topsis. Nowadays, there are no uniform criteria for measuring service quality. It brings challenges to the future development of airlines. Therefore, grasping the future market trends leads to winning with advanced and high-quality services. Afterward, the applicability in the service supplier selection system with the new entropy is discussed to evaluate the service quality and measure uncertainty. Finally, the new PFN entropy is verified with a good ability in the last MCDM numerical example.

scholarly journals Assessing Monstrous Fan in Malaysia: Present and Future

2019 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Raja Raja Othman ◽  
Nurfaezah Abdullah ◽  
Amiruddin Ahamat ◽  
Nor Md Zuki ◽  
Fairul Abdul Shukor ◽  
...  
Keyword(s):  
Online Survey ◽  
Future Market ◽  
Market Demand ◽  
Specific Knowledge ◽  
Large Space ◽  
Policy Makers ◽  
Future Scenario ◽  
The Future ◽  
Product Technology

In the last few years, the monstrous fan has gained attention in this country for large space buildings and areas. The continuity of this product technology is important to allow it to be able to survive in the current and future market. However, there are limited studies on the present and future scenario of the monstrous fan, especially in Malaysia. Thus, the objective of this paper is to forecast its present technology, evaluating the market demand and future of the monstrous fan. For these reasons, an online survey was used to obtain feedback from suppliers and manufacturers to forecast the future of this product. In conclusion, the monstrous fan has been discussed and predicted, which can be beneficial for various parties including policy makers, government, business and technology players by representing a specific knowledge on the technical specificities of monstrous fans in Malaysia.

scholarly journals The Impact of Restrictive Measures on the Price Discovery Function of Stock Index Futures – Evidence From CSI 500 Stock Index Futures

2018 ◽  
Vol 9 (4) ◽  
pp. 117
Author(s):  
Maoguo Wu ◽  
Zhehao Zhu
Keyword(s):  
Price Discovery ◽  
Spot Market ◽  
Stock Index ◽  
Stock Index Futures ◽  
Future Market ◽  
Index Futures ◽  
The Future ◽  
The Difference ◽  
Restrictive Measures ◽  
The Impact

Restrictive measures implemented by governments have a great impact on the price discovery function of stock index futures. This study compares the price discovery function of CSI 500 stock index futures and CSI 500 stock index before and after the implementation of restrictive measures based on the reaction speed to new information, the price ratio of new information and the price contribution of both future market and spot market. It also analyzes the difference between the price discovery function of the future market and that of the spot market and thus proposes policy implications accordingly.Utilizing data of CSI 500 stock index futures in the period of the stock market crash, this study compares the price discovery function before and after the implementation of restrictive measures. By means of the VECM model and common factor analysis, it further investigates the difference in the price contribution of the two markets. Contributing to existing literature on the relationship between the future market and the spot market, this study explores the change in the price contribution of the two markets and therein studies the impact of restrictive measures on the price discovery function. Empirical evidence finds that before the implementation of restrictive measures, the price discovery function worked more efficiently, while, however, after the implementation of restrictive measures, the price discovery function did not work. Hence, stock index futures do assist in the price discovery of the spot market. In some special time periods, however, due to the impact of restrictive policies, the price contribution of the spot market exceeded that of the future market, implying that the price discovery function of the CSI 500 stock index future market is unstable.

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