How can an economic scenario generation model cope with abrupt changes in financial markets?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Hsi Lee ◽  
Ming-Hua Hsieh ◽  
Weiyu Kuo ◽  
Chenghsien Jason Tsai
Keyword(s):  
Financial Markets ◽  
Large Scale ◽  
Moving Average ◽  
Insurance Companies ◽  
Scenario Generation ◽  
Autoregressive Moving Average ◽  
Generation Model ◽  
Content Type ◽  
Abrupt Changes ◽  
Key Issues

PurposeIt is quite possible that financial institutions including life insurance companies would encounter turbulent situations such as the COVID-19 pandemic before policies mature. Constructing models that can generate scenarios for major assets to cover abrupt changes in financial markets is thus essential for the financial institution's risk management.Design/methodology/approachThe key issues in such modeling include how to manage the large number of risk factors involved, how to model the dynamics of chosen or derived factors and how to incorporate relations among these factors. The authors propose the orthogonal ARMA–GARCH (autoregressive moving-average–generalized autoregressive conditional heteroskedasticity) approach to tackle these issues. The constructed economic scenario generation (ESG) models pass the backtests covering the period from the beginning of 2018 to the end of May 2020, which includes the turbulent situations caused by COVID-19.FindingsThe backtesting covering the turbulent period of COVID-19, along with fan charts and comparisons on simulated and historical statistics, validates our approach.Originality/valueThis paper is the first one that attempts to generate complex long-term economic scenarios for a large-scale portfolio from its large dimensional covariance matrix estimated by the orthogonal ARMA–GARCH model.

scholarly journals Research on forest insurance policy simulation in China

2019 ◽  
Vol 1 (1) ◽  
pp. 82-95
Author(s):  
Ning Ma ◽  
Can Li ◽  
Yang Zuo
Keyword(s):  
Large Scale ◽  
Forest Area ◽  
Insurance Companies ◽  
Content Type ◽  
Government Subsidies ◽  
Multi Agent ◽  
The Government ◽  
The Impact ◽  
Decision Making Rule ◽  
Forest Insurance

Purpose Forest insurance is a popular way to reduce the loss of forest disasters, so it is necessary to actively involve stakeholders. In the multi-agent simulation model, the government, insurance companies and forest farmers participate as three main stakeholders. The purpose of this paper is to mainly simulate the behavior of forest farmers under different environmental variables in order to find the important factors affecting the coverage of forest insurance, so as to improve the ability of forest farmers to resist risks in the face of disasters. Design/methodology/approach In the simulation process, the decision-making rule of a forest farmer’s purchasing behavior is a binary selection chain, which is created at random. Forest farmer agents who adapt to the environment will remain; on the contrary, those will be eliminated. The eliminated agents will renew their behavior selection chains through learning others’ successful behavior based on genetic algorithm. The multi-agent mode is set up on the Eclipse platform by using Java language. Findings The adjustment simulation experiments of insurance premium, insurance subsidy and forest area were carried out. According to the result, conclusions and suggestions are as follows: at present, government subsidies are necessary for the implementation of forest insurance; in the future, with the expansion of the insured forest area and the upgrading and large-scale operation of forest farms, forest farmers will be more willing to join forest insurance program, and, then, the implementation of forest insurance no longer requires government subsidies for forest insurance premiums. Originality/value This paper explores the impact of three important factors on the implementation of forest insurance.

A study on volatility and return spillover of exchange-traded funds and their benchmark indices in India

2019 ◽  
Vol 46 (1) ◽  
pp. 19-39 ◽  
Author(s):  
Buvanesh Chandrasekaran ◽  
Rajesh H. Acharya
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Exchange Traded Funds ◽  
Volatility Spillover ◽  
Conditional Heteroscedasticity ◽  
Content Type ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Practical Implications

Purpose The purpose of this paper is to empirically examine the volatility and return spillover between exchange-traded funds (ETFs) and their respective benchmark indices in India. The paper uses time series data which consist of equity ETF and respective index returns. Design/methodology/approach The study uses autoregressive moving average–generalized autoregressive conditional heteroscedasticity and autoregressive moving average–exponential generalized autoregressive conditional heteroscedasticity models. The study uses data from the inception date of each ETF to December 2016. Findings The findings of the paper confirm that there is unidirectional return spillover from the benchmark index to ETF returns in most of the ETFs. Furthermore, ETF and benchmark index return have volatility persistence and show the presence of asymmetric volatility wherein a negative news has more influence on volatility compared to a positive news. Finally, unlike unidirectional return spillover, there is a bidirectional volatility spillover between ETF and benchmark index return. Practical implications The study has several practical implications for investors and regulators. A positive daily mean return over a fairly long period of time indicates that the passive equity ETFs can be a viable long-term investment option for ordinary investors. A bidirectional volatility spillover between the ETFs and benchmark index returns calls for the attention of the market regulators to examine the reasons for the same. Originality/value ETFs have seen fast growth in the Indian market in recent years. The present study considers the longest period data possible.

Conceptualizing sources, key concerns and critical factors for manufacturing flexibility adoption

2016 ◽  
Vol 27 (3) ◽  
pp. 379-407 ◽  
Author(s):  
Ruchi Mishra ◽  
Ashok K. Pundir ◽  
L. Ganapathy
Keyword(s):  
Quantitative Research ◽  
Large Scale ◽  
Second Phase ◽  
Manufacturing Flexibility ◽  
Critical Factors ◽  
Focus Group Interviews ◽  
Content Type ◽  
Key Issues ◽  
Group Interviews

Purpose – The purpose of this paper is to investigate factors influencing manufacturing flexibility adoption and simultaneously explores some of the key issues prevailing in manufacturing flexibility adoption in Indian context. The study also stratifies critical factors for successful manufacturing flexibility adoption. Design/methodology/approach – Using exploratory sequential design, a series of focus group interviews were conducted with Indian manufacturing professionals and these interviews were supplemented by 127 follow-up structured questionnaires. Findings – Two major themes emerged from the first phase of the study – role played by some of the unexplored antecedents of manufacturing flexibility and key issues in manufacturing flexibility adoption. In the second phase, a list of factors was categorized based on their degree of importance in manufacturing flexibility adoption. Research limitations/implications – Being qualitative in nature, the study suffers from inherent risk of subjectivity associated with manufacturing practitioners. A large-scale survey and rigorous quantitative analysis would be helpful to further validate the list of factors and underlying relationships among proposed factors. Practical implications – The identified list of factors and some of the key issues in manufacturing flexibility adoption can be of great help to practitioners. The stratified list of factors can be further used by academicians to develop an instrument for manufacturing flexibility adoption. Originality/value – The paper identifies a set of factors that affects manufacturing flexibility adoption. It offers a basis for instrument development for manufacturing flexibility adoption and provides direction for future quantitative research in manufacturing flexibility area.

Academic video lectures over named data networking

2020 ◽  
Vol 48 (4) ◽  
pp. 165-177
Author(s):  
Dimitris Kanellopoulos
Keyword(s):  
Large Scale ◽  
Future Internet ◽  
Security And Privacy ◽  
Named Data Networking ◽  
Future Internet Architecture ◽  
Content Type ◽  
Video Lecture ◽  
Video Lectures ◽  
Key Issues ◽  
Data Networking

Purpose Information-centric networking (ICN) is an innovative paradigm for the future internet architecture. This paper aims to provide a view on how academic video lectures can exploit the ICN paradigm. It discusses the design of academic video lectures over named data networking (NDN) (an ICN architecture) and speculates their future development. To the best of author’s knowledge, a similar study has not been presented. Design/methodology/approach The paper is a visionary essay that introduces the background, elaborates the basic concepts and presents the author’s views and insights into academic video lectures that exploit the latest development of NDN approach and its applications. Findings The ICN paradigm is closely related to the levels of automation and large-scale uptake of multimedia applications that provide video lectures. Academic video lectures over NDN have: improved efficiency, better scalability with respect to information/bandwidth demand and better robustness in challenging communication scenarios. A framework of academic video lectures over NDN must take into account various key issues such as naming (name resolution), optimized routing, resource control, congestion control, security and privacy. The size of the network in which academic video lectures are distributed, the content location dynamics and the popularity of the stored video lectures will determine which routing scheme must be selected. If semantic information is included into academic video lectures, the network dynamically may assist video (streaming) lecture service by permitting the network to locate the proper version of the requested video lecture that can be better delivered to e-learners and/or select the appropriate network paths. Practical implications The paper helps researchers already working on video lectures in finding a direction for designing and deploying platforms that will provide content-centric academic video lectures. Originality/value The paper pioneers the investigation of academic video lecture distribution in ICN and presents an in-depth view to its potentials and research trends.

Forecasting performance of time series models on electricity spot markets

2018 ◽  
Vol 12 (4) ◽  
pp. 617-640 ◽  
Author(s):  
Marc Gürtler ◽  
Thomas Paulsen
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Specific Model ◽  
Spot Price ◽  
Autoregressive Moving Average ◽  
Time Series Models ◽  
Testing Time ◽  
Detailed Model ◽  
Content Type ◽  
Forecasting Performance

Purpose Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance. Design/methodology/approach The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market. Findings The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data. Originality/value The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

scholarly journals Design of conventional and neural network based controllers for a single-shaft gas turbine

2017 ◽  
Vol 89 (1) ◽  
pp. 52-65 ◽  
Author(s):  
Hamid Asgari ◽  
Mohsen Fathi Jegarkandi ◽  
XiaoQi Chen ◽  
Raazesh Sainudiin
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Pid Controller ◽  
Feedback Linearization ◽  
Moving Average ◽  
Superior Performance ◽  
Autoregressive Moving Average ◽  
Content Type ◽  
And Performance

Purpose The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design/methodology/approach Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices. Findings It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller. Practical implications This study aims at using artificial intelligence in gas turbine control systems. Originality/value This paper provides a novel methodology for control of gas turbines.

Capacity value estimation of wind power incorporating hourly wind speed

2017 ◽  
Vol 14 (6) ◽  
pp. 497-502 ◽  
Author(s):  
Ashwini Kumar Nayak ◽  
Kanungo Barada Mohanty ◽  
Vinaya Sagar Kommukuri ◽  
Kishor Thakre
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Reliability Index ◽  
Moving Average ◽  
Clear Correlation ◽  
Autoregressive Moving Average ◽  
Content Type ◽  
The Third ◽  
Before And After ◽  
Value Estimation

Purpose The purpose of this paper is to show the effect of randomness of wind speed on the capacity value estimation of wind power. Three methods that incorporate hourly wind speed have been evaluated. Design/methodology/approach Wind speed is simulated using autoregressive moving average method and is included in the calculation of reliability index as a negative load on an hourly basis. The reliability index is calculated before and after the addition of wind capacity. Increment of load or alteration of conventional capacity will lead to capacity estimation. Findings Among the aforementioned three methods, the former two exclude the availability rate and give the exact value for wind capacity addition. The third method is based on the availability rate and provides a little higher capacity value, indicating a clear correlation between availability and capacity value. Originality/value The methods that exclude the availability rate show consistent results. By including the availability rate, the third method predicts the inverse relation between the availability rate and the capacity value.

What does Google recommend when you want to compare insurance offerings?

2019 ◽  
Vol 71 (3) ◽  
pp. 310-324
Author(s):  
Dirk Lewandowski ◽  
Sebastian Sünkler
Keyword(s):  
Data Analysis ◽  
Empirical Study ◽  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Insurance Companies ◽  
Content Type ◽  
Search Queries ◽  
Large Scale Analysis ◽  
Search Engine Result

Purpose The purpose of this paper is to describe a new method to improve the analysis of search engine results by considering the provider level as well as the domain level. This approach is tested by conducting a study using queries on the topic of insurance comparisons. Design/methodology/approach The authors conducted an empirical study that analyses the results of search queries aimed at comparing insurance companies. The authors used a self-developed software system that automatically queries commercial search engines and automatically extracts the content of the returned result pages for further data analysis. The data analysis was carried out using the KNIME Analytics Platform. Findings Google’s top search results are served by only a few providers that frequently appear in these results. The authors show that some providers operate several domains on the same topic and that these domains appear for the same queries in the result lists. Research limitations/implications The authors demonstrate the feasibility of this approach and draw conclusions for further investigations from the empirical study. However, the study is a limited use case based on a limited number of search queries. Originality/value The proposed method allows large-scale analysis of the composition of the top results from commercial search engines. It allows using valid empirical data to determine what users actually see on the search engine result pages.

Asymmetric price transmission in the Chinese pork and pig market

2018 ◽  
Vol 120 (1) ◽  
pp. 120-132 ◽  
Author(s):  
Xiaoxia Dong ◽  
Colin Brown ◽  
Scott Waldron ◽  
Jing Zhang
Keyword(s):  
Moving Average ◽  
Price Transmission ◽  
Policy Implications ◽  
Autoregressive Moving Average ◽  
Key Factors ◽  
Content Type ◽  
Threshold Autoregressive ◽  
Asymmetric Price Transmission ◽  
Moving Average Models ◽  
The Impact

Purpose The purpose of this paper is to analyze price transmission in the Chinese pork market between 1994 and 2016 and examine any incidence and causes of asymmetric price transmission. Design/methodology/approach The approach uses threshold autoregressive models, asymmetric error correction models and autoregressive moving average models to examine the price transmission using monthly pig and pork prices from 1994 to 2016. Findings While a symmetric price transmission between pork and pig prices was identified for the period between June 1994 and June 2007, an asymmetric price transmission response between pork and pig prices was found for the period July 2007 to June 2016. Key factors behind the asymmetric price transmission include the chicken price and China’s provisional purchasing and stockpiling policy which is having a counter-productive impact on prices. Originality/value The paper contributes to the literature by examining price transmission in two different periods: 1994 to 2007 where prices are lower and more stable; and 2007 to 2016 where prices are higher and volatile. The paper examines the impact of production and market policies on price transmission in the Chinese pork and pig market, with several policy implications.

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