Analyzing fish price volatility dynamics is essential to understand the impact of aquaculture growth in Bangladesh, a developing country where much of the animal source protein is obtained through fish consumption. In this article, we use both descriptive statistics and econometric modeling to understand the price volatility of four major aquaculture products at three important retail markets of Bangladesh as well as the efficiency of markets to adjust to a price shock. We utilize publicly available monthly retail fish price data of the Department of Agricultural Marketing (DAM) from January 2007 to December 2019. This period is characterized by a rapid transformation of the inland aquaculture sector in Bangladesh from largely low-intensity farming methods towards commercialization. Our results show that Bangladesh is experiencing lower fish price volatility during recent years for almost all the fish species analyzed in the study which may be the result of substantial growth in aquaculture production. The article also sheds light on the volatility persistence and finds almost all the markets are inefficiently adjusting to price shock which hints that there is more room for improving the functioning of fish markets in Bangladesh.
Lumber is one of the most essential forest products in the United States. During the first year of the COVID-19 pandemic, lumber prices almost quadrupled, and fluctuations reached record levels. Although market experts have pointed to various drivers of such high price volatility, no firm conclusions have been drawn yet. Using the generalized autoregressive conditional heteroskedasticity-mixed data sampling (GARCH-MIDAS) framework, this study assesses the potential drivers of lumber price volatility, with predictors including the Google Trends Web Search Index, housing starts, US lumber production quantity, and VIX index, representing public attention, housing demand, lumber supply, and macroeconomic concerns, respectively. We have found that housing demand is the key driver of lumber price volatility, followed by public attention. It is worth noting that US lumber supply and macroeconomic concerns have played a modest role in explaining lumber price volatility. Also, forecasting lumber price by using the housing demand variable substantially outperforms others. Market participants, including lumber mills, wholesalers, and home builders can get valuable information from the housing market to manage lumber price risk.
Study Implications: The findings of this study can be used to improve hedging strategies, design option pricing formulas, and setting margin requirements. Critical information for price risk management on the lumber market can be gained by lumber market participants from the housing market. For forest management decisions by landowners, giving close attention to housing market would provide valuable information on the appropriate time for timber harvesting, because changes in the housing market affect lumber price that will indirectly affect the demand for timber, which is the most important factor of production for lumber mills.
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