The Impact of Variable Renewable Energy Penetration on Wholesale Electricity Prices in Japan Between FY 2016 and 2019

The merit order effect (MOE), which renewable energy sources can decrease wholesale electricity prices, plays an important role in establishing low-carbon societies. After the liberalization of the electricity market, the trade volume of the Japan Electric Power Exchange (JEPX) day-ahead spot market drastically increased between 2016 and 2019; however, price spikes still occur often. Ordinary least squares and quantile regression analyses were applied in this study to investigate how wind and solar photovoltaics (PV) energy generation affect the JEPX day-ahead spot price by time, price range, and area, and we concluded that the MOE of wind increased between 2016 and 2019 while that of PV decreased during this time. In regard to the high price ranges, although wind generation is not significant in terms of reducing price spikes, PV had this effect in 2016 and 2017 but not during the other years covered. The study area was divided into four regions, and each area followed trends that were different from those of the national analysis. Overall, the key finding of our study is that wind power has more potential to reduce electricity prices than PV.

Electricity Market Liquidity and Price Spikes: Evidence from Hungary

This article examines how electricity market liquidity, renewable production and cross-border activity together in combination explain price spikes in the Hungarian Power Exchange day-ahead auctions. In the applied logit model, the dependent variable representing the price spike is binary, and the key explanatory variable is a modified bid-ask spread depicting liquidity. Weather-dependent renewable production and the difference between exports and imports appear as control variables in the model. The empirical analysis was based on data from 2017 and 2018. The results show that the control variables have no effect on the bid-ask spread and that the model explains 96 per cent of the spikes well, with an AUC-ROC of 0.75 and a Gini coefficient of 0.5. Based on the results, it may be worthwhile for traders to incorporate their data from sales and purchase curves into their forecasts, as this will improve their chances of successfully predicting extreme prices.

Smallholder Farmers’ Challenges of Coping with COVID-19 Containments: Insights from two food regions in Indonesia

The COVID-19 pandemic has forced governments around the world to impose containment measures to prevent the rapid spread of the corona virus. The Indonesian government implemented “large-scale social restrictions,” which have impacted farming and farmers’ food security. Farmers are both producers and consumers of food and, therefore, have been facing new challenges due to transport restrictions, price spikes for inputs, price drops for their produce, or conditions which aggravated cooperation, such as social distancing. This study aims at analysing the challenges of the containments from a smallholder farmer perspective and examining farmers’ coping potential. A digital survey with 323 farmers has been designed as comparative observational research in Toraja, South Sulawesi, and selected regions of Java. The Bonferroni Multiple Comparison Test was used to test for significance regarding socio-economic factors and space. A logistic regression model extracted determinants for crisis coping. Results reveal, that female farmers worry more about COVID-19 outbreak compared to men at a significant level. In contrast, male farmers, particularly in Java, are more concerned about social restrictions due to limited mobility. Food price spikes were reported in both regions, with sharp increases for fish, fruits, and vegetables in Java, for staples in Toraja, and for meat and sugar in both regions. Food groups, that trade through agents and brokers or are transported longer distances were affected most due to their complex and long supply chains that were disrupted during the restrictions. In Java, farmers face multiple shocks, of which climate change was reported even more often than the pandemic related shocks. Not being able to help each other on the farm due to social distancing is a significant concern of farmers in Toraja. As a result of food market disturbances, farmers began to grow and eat more vegetables and fruits. In conclusion, food security for farmers slightly decreased due to affordability, and market disruptions already point to long-term income losses. The study team recommends to promote smallholders’ healthy food production, value addition and direct end-consumer linkages to build back better their livelihoods post-COVID-19.

Unlearned lessons of food price spikes

The article substantiates the author’s hypothesis of the fundamental reasons for periodic «ups» in prices for essential food products, including the most recent price jump in the second half of 2020. Both the official assessments of the causes of recurring food price surges and the measures taken by the executive branch to stop and prevent them are subjected to critical analysis. Conclusions and fundamental proposals are formulated aimed at eradicating the systemic causes of price volatility in the domestic food market.

Forecasting Price Spikes in Electricity Markets

Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model to forecast the occurrence of price spikes in the German intraday electricity market. As price spikes, we define the prices that lie above the 95th quantile estimated by fitting a Generalized Pareto distribution in the innovation distribution of an AR-EGARCH model. The generalization ability of the model is tested in an out-of-the-sample dataset consisting of 4080 hours. Furthermore, we compare the performance of our best SVM model against Neural Networks (NNs) and Gradient Boosted Machines (GBMs).

On agricultural commodities’ extreme price risk

We show how fat tails in agricultural commodity returns arise endogenously from productivity shocks in a standard macroeconomic model. Using nearly ninety years of data, we show that the eight agricultural commodities in our sample exhibit fat-tailed return distributions. Statistical tests confirm the heavy-tailedness of price spikes for agricultural commodities. We apply extreme value theory to estimate the size and likelihood of price spikes in agricultural commodities. Back-testing verifies the validity of our risk assessment methodology.