Vulnerability to food insecurity in rural Punjab, Pakistan

Purpose The purpose of this paper is to measure the vulnerability to food insecurity in rural Punjab, Pakistan. Design/methodology/approach Primary data of 1,152 households were collected. The extent of food deficiency was measured using dietary intake assessment method (seven days). Value at Risk (VaR) and conditional Value at Risk (cVaR), a method widely used for risk analysis in financial institutes, were applied to assess the vulnerability to food insecurity. Findings In total, 23 percent of the sample households were measured as food deficient. The VaR and cVaR results identified that the lowest 3 percentiles (up to 30 percent) were at risk to become food deficient without any seasonal shortages. In case of shocks, up till sixth percentiles (60 percent) will be as at risk of food deficiency. This study suggests that multi-period data, at least quarterly, are required to predict vulnerability. It is suggested that a blanket policy is not a good approach. Once the most vulnerable households are identified, a targeted approach must be opted. Originality/value Generalizing the results of one week’s calorie calculations may produce biased results that may mislead the policy process. A multi-period data collection is costly and cumbersome. The application of VaR and cVaR helps overcome this issue. Furthermore, this is one of the initial studies to apply these methods to food security analysis.

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Examining the evidence of risk spillovers between Shanghai and London non-ferrous futures markets: a dynamic Copula-CoVaR approach

International Journal of Emerging Markets ◽

10.1108/ijoem-04-2020-0355 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hong Shen ◽

Yue Tang ◽

Ying Xing ◽

Pin Ng

Keyword(s):

At Risk ◽

Value At Risk ◽

Global Economy ◽

Futures Markets ◽

Ferrous Metal ◽

Conditional Value At Risk ◽

Metal Exchange ◽

London Metal Exchange ◽

Content Type ◽

Dynamic Copula

PurposeThis paper aims to examine the evidence of risk spillovers between Shanghai and London non-ferrous futures markets using a dynamic Copula-CoVaR approach.Design/methodology/approachWith daily data, the marginal distributions and optimal Copula functions are determined using the kernel estimation method and squared Euclidean distance test. The conditional value-at-risk and the conditional value-at-risk spillover rate are computed from the Copula estimated parameters based on the Copula-CoVaR model. Also, the dynamic correlation coefficient between the two futures markets is investigated.FindingsThe empirical results are as follows: overall, the risk spillover effect exerted by the London Metal Exchange on the Shanghai Futures Exchange is more significant than vice versa. Moreover, the degree of risk spillovers exerted by the London Metal Exchange on the Shanghai Futures Exchange for zinc and copper are more significant when they are depressed in the London Metal Exchange. Moreover, the dynamic of the correlation between the Shanghai and London futures markets is attributed to be largely due to changes in the global economy.Research limitations/implicationsThe Copula-CoVaR model used in this paper is suitable for measuring the risk spillovers between two different markets, while the risk spillovers across multiple markets or the consideration of multiple risk factors cannot be accurately captured using this framework. Multiple state variables to capture time variation in the conditional moments of return series will be a topic in future research.Practical implicationsThe results provide theoretical support for risk management and monitoring of the non-ferrous futures markets.Originality/valueThe ability of the Copula function to accurately describe a nonlinear relationship and tail correlation is harnessed to measure the risk spillovers, explore the degree and direction of risk spillovers and identify the source of risk spillovers. The global economy is incorporated as a macro factor to explore its inner connection with the dynamic of risk spillovers in the non-ferrous metal futures market.

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A risk model to compute the volatility and the need for collateral margins in energy futures contracts in Brazil

International Journal of Energy Sector Management ◽

10.1108/ijesm-02-2021-0008 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Pedro Argento ◽

Marcelo Cabus Klotzle ◽

Antonio Carlos Figueiredo Pinto ◽

Leonardo Lima Gomes

Keyword(s):

At Risk ◽

Value At Risk ◽

Risk Model ◽

Extreme Values ◽

Safety Margin ◽

Conditional Value At Risk ◽

Futures Contracts ◽

Content Type ◽

Energy Futures ◽

Risk Dimensions

Purpose Brazil is characterized by the inexistence of a more robust system of guarantees and rules to minimize risks and protect agents in energy futures contracts. In this sense, this study aims to answer the question of how a centralized clearing agent can compute safety margin requirements to help reduce the systemic risk of the energy futures contracts market in Brazil. Design/methodology/approach The intermediate steps and specific objectives are to analyze the volatility behavior, identify the autoregressive conditional heteroscedasticity effects and model the variance of the return series. Based on this, the authors calculate the value-at-risk and conditional value-at-risk metrics for the energy futures contracts. As a robustness test, the authors added a peak over threshold methodology from extreme values theory. Findings In general, monthly products require margins because of their higher variance. With the asymmetrical distribution of returns, the authors needed to consider different maintenance margins for the long and short positions. It was also shown that two guarantee margins were required to secure the contracts as follows: the initial margin and the maintenance margin. The three factors that defined the size of the maintenance margin the volatility, skewness and kurtosis of the return series. Originality/value The contribution of this study lies in promoting the understanding of the risk dimensions of the energy derivatives market in Brazil and it offers concrete recommendations for how to mitigate this risk through market mechanisms and structures. Similar arrangements can be applied to other emerging markets.

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