Household margin insurance of agricultural sector in Indonesia using a farmer exchange rate index

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Atina Ahdika ◽  
Dedi Rosadi ◽  
Adhitya Ronnie Effendie ◽  
Gunardi
Keyword(s):  
Exchange Rate ◽  
Value At Risk ◽  
Agricultural Sector ◽  
Time Varying ◽  
Risk Modeling ◽  
Copula Models ◽  
Content Type ◽  
Premium Calculation ◽  
Index Ratio ◽  
Margin Insurance

PurposeFarmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.Design/methodology/approachThis research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.FindingsFirst, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.Originality/valueThis research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

Forecasting multivariate VaR and ES using MC-GARCH-Copula model

2020 ◽  
Vol 21 (5) ◽  
pp. 493-516 ◽  
Author(s):  
Hemant Kumar Badaye ◽  
Jason Narsoo
Keyword(s):  
Value At Risk ◽  
Garch Model ◽  
Dependence Structure ◽  
Copula Model ◽  
Copula Models ◽  
Content Type ◽  
Significance Level ◽  
Foreign Currencies ◽  
Out Of Sample ◽  
Weighted Portfolio

Purpose This study aims to use a novel methodology to investigate the performance of several multivariate value at risk (VaR) and expected shortfall (ES) models implemented to assess the risk of an equally weighted portfolio consisting of high-frequency (1-min) observations for five foreign currencies, namely, EUR/USD, GBP/USD, EUR/JPY, USD/JPY and GBP/JPY. Design/methodology/approach By applying the multiplicative component generalised autoregressive conditional heteroskedasticity (MC-GARCH) model on each return series and by modelling the dependence structure using copulas, the 95 per cent intraday portfolio VaR and ES are forecasted for an out-of-sample set using Monte Carlo simulation. Findings In terms of VaR forecasting performance, the backtesting results indicated that four out of the five models implemented could not be rejected at 5 per cent level of significance. However, when the models were further evaluated for their ES forecasting power, only the Student’s t and Clayton models could not be rejected. The fact that some ES models were rejected at 5 per cent significance level highlights the importance of selecting an appropriate copula model for the dependence structure. Originality/value To the best of the authors’ knowledge, this is the first study to use the MC-GARCH and copula models to forecast, for the next 1 min, the VaR and ES of an equally weighted portfolio of foreign currencies. It is also the first study to analyse the performance of the MC-GARCH model under seven distributional assumptions for the innovation term.

scholarly journals Impacts of inflation on gold price and exchange rate in Vietnam: time-varying vs fixed coefficient cointegrations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pham Dinh Long ◽  
Bui Quang Hien ◽  
Pham Thi Bich Ngoc
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Gold Price ◽  
Time Varying ◽  
Coefficient Estimates ◽  
Content Type ◽  
Exchange Rate Fluctuations ◽  
The Impact ◽  
Shed Light

PurposeThe paper aims to shed light on the effects of inflation on gold price and exchange rate in Vietnam by using time-varying cointegration.Design/methodology/approachUsing cointegration techniques with fixed coefficient and time-varying coefficient, the study exams the impacts of inflation in models and compares the results through coefficient estimates.FindingsA significant inflation impacts are found with the time-varying cointegration but not with the fixed coefficient cointegration models. Moreover, monetary policy affects exchange rate not only directly via its instruments as money supply and interest rate but indirectly via inflation. Also, interest rate is one of the determinants of gold price.Originality/valueTo the best of our knowledge, this paper is the first to use time-varying cointegration to analyze the impact of inflation on the gold price and exchange rate in Vietnam. Gold price and exchange rate fluctuations are always the essential and striking issues, which have been emphasized by economists and policymakers. In macroeconometric researches, cointegration models are often used to analyze the long-term relations between variables. Attentionally, applied models show a limitation when estimating coefficients are fixed. This characteristic might not really match with the data properties and the variation of the economy. Currently, time-varying cointegration models are emerging method to solve the above issue.

Efficiency performance of fertilizer use in arable agricultural production in China

2019 ◽  
Vol 11 (1) ◽  
pp. 52-69 ◽  
Author(s):  
Wei Huang ◽  
Li Jiang
Keyword(s):  
Agricultural Production ◽  
Design Methodology ◽  
Agricultural Sector ◽  
Positive Impact ◽  
Time Varying ◽  
Content Type ◽  
Fertilizer Use ◽  
Efficiency Performance ◽  
Use Efficiency ◽  
Almost All

PurposeFertilizer overuse is regarded as one of the main contributors to agricultural pollution and environmental problems in China. The purpose of this paper is to evaluate technical efficiency (TE) and fertilizer overuse index (FOI) with respect to China’s arable agricultural production and examine regional variations in terms of fertilizer overuse.Design/methodology/approachThe maximum likelihood random effects–time varying inefficiency effects model was applied to estimate TE, fertilizer use efficiency (FUE) and FOI.FindingsOver the study period (2011–2015), TE steadily increased for each individual province. Overall, mean annual TE was 0.811, implying that, on average, Chinese provinces could increase output by 18.9 per cent given unchanged levels of inputs and technology. Mean annual FOI ranged from 0.008 to 3.139, with a mean of 0.685, suggesting that there is fertilizer overuse in almost all provinces, and that large regional variation exists. Coastal provinces were found to have the highest TE scores, while the central region showed the highest degree of fertilizer overuse.Originality/valueThe results indicate that fertilizer use has had a significant positive impact on production in the China’s arable agricultural sector. High TE was not necessarily associated with low FUE.

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.

Optimization algorithms and investment portfolio analytics with machine learning techniques under time-varying liquidity constraints

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazin A.M. Al Janabi
Keyword(s):  
Machine Learning ◽  
Liquidity Constraints ◽  
Market Risk ◽  
Machine Learning Techniques ◽  
Time Varying ◽  
Risk Modeling ◽  
Investment Portfolio ◽  
Content Type ◽  
Learning Techniques ◽  
Portfolio Managers

Purpose This paper aims to examine from commodity portfolio managers’ perspective the performance of liquidity adjusted risk modeling in assessing the market risk parameters of a large commodity portfolio and in obtaining efficient and coherent portfolios under different market circumstances. Design/methodology/approach The implemented market risk modeling algorithm and investment portfolio analytics using reinforcement machine learning techniques can simultaneously handle risk-return characteristics of commodity investments under regular and crisis market settings besides considering the particular effects of the time-varying liquidity constraints of the multiple-asset commodity portfolios. Findings In particular, the paper implements a robust machine learning method to commodity optimal portfolio selection and within a liquidity-adjusted value-at-risk (LVaR) framework. In addition, the paper explains how the adapted LVaR modeling algorithms can be used by a commodity trading unit in a dynamic asset allocation framework for estimating risk exposure, assessing risk reduction alternates and creating efficient and coherent market portfolios. Originality/value The optimization parameters subject to meaningful operational and financial constraints, investment portfolio analytics and empirical results can have important practical uses and applications for commodity portfolio managers particularly in the wake of the 2007–2009 global financial crisis. In addition, the recommended reinforcement machine learning optimization algorithms can aid in solving some real-world dilemmas under stressed and adverse market conditions (e.g. illiquidity, switching in correlations factors signs, nonlinear and non-normal distribution of assets’ returns) and can have key applications in machine learning, expert systems, smart financial functions, internet of things (IoT) and financial technology (FinTech) in big data ecosystems.

scholarly journals Value-at-Risk Modeling with Conditional Copulas in Euclidean Space Framework

2019 ◽  
Vol 12 (1) ◽  
pp. 194-207 ◽  
Author(s):  
Vini Yves Bernadin Loyara ◽  
Diakarya Barro
Keyword(s):  
At Risk ◽  
Euclidean Space ◽  
Value At Risk ◽  
Scalar Product ◽  
Time Varying ◽  
Risk Modeling ◽  
Analytic Relation ◽  
Conditional Copula

This paper aims to establish an analytic relation between a time-varying conditional copula and the value at risk modeled by the underlying. Specically, under the asumption that the space is euclidean we use scalar product to clarify a link between the conditional copula varying with time and norms. It is then established a new expression on the geometric yield

Islamic hedging for pilgrimage funds: case of Indonesia

2019 ◽  
Vol 11 (3) ◽  
pp. 328-341
Author(s):  
Rifki Ismal ◽  
Nurul Izzati Septiana
Keyword(s):  
Exchange Rate ◽  
The Other ◽  
Exchange Rate Risk ◽  
Content Type ◽  
Other Hand ◽  
Rate Risk ◽  
Risk Identify ◽  
Hajj Season ◽  
The Right ◽  
Very High

Purpose The demand for Saudi Arabian real (SAR) is very high in the pilgrimage (hajj) season while the authority, unfortunately, does not hedge the hajj funds. As such, the hajj funds are potentially exposed to exchange rate risk, which can impact the value of hajj funds and generate extra cost to the pilgrims. The purpose of this paper is to conduct simulations of Islamic hedging for pilgrimage funds to: mitigate and minimize exchange rate risk, identify and recommend the ideal time, amount and tenors of Islamic hedging for hajj funds, estimate cost saving by pursuing Islamic hedging and propose technical and general recommendations for the authority. Design/methodology/approach Forward transaction mechanism is adopted to compute Islamic forward between SAR and Rupiah (Indonesian currency) or IDR. Findings – based on simulations, the paper finds that: the longer the Islamic hedging tenors, the better is the result of Islamic hedging, the decreasing of IDR/USD is the right time to hedge the hajj funds and, on the other hand, the IDR/SAR appreciation is not the right time to hedge the hajj funds. Findings Based on simulations, the paper finds that: the longer the Islamic hedging tenors, the better is the result of Islamic hedging, the decreasing of IDR/USD is the right time to hedge the hajj funds and, on the other hand, the IDR/SAR appreciation is not the right time to hedge the hajj funds. Research limitations/implications The research suggests the authority to (and not to) hedge the hajj fund, depending on economic conditions and market indicators. Even though the assessment is for the Indonesian case, other countries maintaining hajj funds might also learn from this paper. Originality/value To the best of author’s knowledge, this is the first paper in Indonesia that attempts to simulate the optimal hedging of hajj funds.

scholarly journals The Dynamic Impacts of the COVID-19 Pandemic on Log Prices in China: An Analysis Based on the TVP-VAR Model

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 449
Author(s):  
Chenlu Tao ◽  
Gang Diao ◽  
Baodong Cheng
Keyword(s):  
Exchange Rate ◽  
Structural Changes ◽  
Raw Materials ◽  
Supply And Demand ◽  
Oil Prices ◽  
Control Measures ◽  
Var Model ◽  
Time Varying ◽  
Wood Industry ◽  
The Impact

China’s wood industry is vulnerable to the COVID-19 pandemic since wood raw materials and sales of products are dependent on the international market. This study seeks to explore the speed of log price recovery under different control measures, and to perhaps find a better way to respond to the pandemic. With the daily data, we utilized the time-varying parameter autoregressive (TVP-VAR) model, which can incorporate structural changes in emergencies into the model through time-varying parameters, to estimate the dynamic impact of the pandemic on log prices at different time points. We found that the impact of the pandemic on oil prices and Renminbi exchange rate is synchronized with the severity of the pandemic, and the ascending in the exchange rate would lead to an increase in log prices, while oil prices would not. Moreover, the impulse response in June converged faster than in February 2020. Thus, partial quarantine is effective. However, the pandemic’s impact on log prices is not consistent with changes of the pandemic. After the pandemic eased in June 2020, the impact of the pandemic on log prices remained increasing. This means that the COVID-19 pandemic has long-term influences on the wood industry, and the work resumption was not smooth, thus the imbalance between supply and demand should be resolved as soon as possible. Therefore, it is necessary to promote the development of the domestic wood market and realize a “dual circulation” strategy as the pandemic becomes a “new normal”.

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