The “Financial Resilience” research group within the Birmingham Business School hosted a workshop on 6 June 2018 in JG Smith Room 111. We had several speakers presenting on a variety of topics as follows:
Prof. Dr. Abdinardo Moreira Barreto de Oliveira
Universidade Federal do Vale do São Francisco (UNIVASF)
"Weather derivatives for the Brazilian agricultural commodities: a proposal for pricing model and hedging effectiveness"
The aim of this ongoing project is to propose a model for pricing weather derivatives for the Brazilian agricultural commodities, as well as to evaluate its hedging effectiveness. There are at least two reasons that highlight the importance of this project: 1) none of the Brazilian food industries (publicly traded on stock markets or private) makes hedge contracts in order to protect themselves from climate variations, although their financial performance is affected by weather events; 2) the Brazilian Stock Exchange (BM&FBOVESPA) does not offer weather derivatives contracts, as already are offered by the Chicago Mercantile Exchange (CME) in the USA. In order to price weather derivatives (temperature and rainfall), this study proposes to test models based on the standard stochastic Brownian motion with Monte Carlo simulation and models focused on time series analysis. In order to evaluate hedging effectiveness, this study will employ both linear (for futures) and nonlinear (for options) hedging strategies, focused on the Minimum Variance approach. The weather data (temperature and rainfall) will be collected from the Meteorological Database for Teaching and Research (BDMEP), sited in the Brazilian Meteorology Institute (INMET). The annual crop yield will be collected from IBGE Automatic Recovery System (SIDRA), sited in the Brazilian Institute of Geography and Statistics (IBGE).
Keywords: Weather Derivatives. Agriculture. Pricing Models. Hedging Effectiveness.
Lecturer in Financial Economics, Department of Economics, Birmingham Business School
“An Experimental Study on Financial Market Transparency”
It is common knowledge that the characteristics of a market contribute to its better performance. One of the most important and most controversial aspects of any financial market is transparency. Changing the level of pre-and post-trade transparency may considerably influence information asymmetry in the markets. This may alter the behaviour of market participants substantially and, as a consequence, may equally enhance or harm market liquidity and efficiency. We run an experiment to test these hypotheses.
Keywords: Market Microstructure, Pre-and Post-trade Transparency, Market Liquidity
Teaching Fellow, Department of Finance, Birmingham Business School
“Mining for Signals of Future Consumer Expenditure on Facebook, Twitter and Google Trends”
Consumer expenditure constitutes the largest component of Gross Domestic Product in developed countries, and forecasts of consumer spending are therefore an important tool that governments and central bank use in their policy-making. In this paper we examine methods to forecast consumer spending from user-generated content, such as search engine queries and social media data, which hold the promise to produce forecasts much more efficiently than traditional surveys. Specifically, the aim of the paper is to study the relative utility of evidence about purchase intentions found in Google Trends versus those found in Facebook and Twitter posts, for the problem of forecasting consumer expenditure. Our main findings are that the Google Trends indicators and indicators extracted from social media are both beneficial for the forecasts: adding them as exogenous variables into regression model produces improvements on the pure AR baseline, consistently across different forecast horizons.
Keywords: Consumer behaviour; Consumer expenditure; Social media; Google Trends; Machine learning
Jane M Binner
Chair of Finance, Department of Finance, Birmingham Business School
“A New Risky Money for the USA & UK "
We extend the scope of monetary aggregation beyond capital certain assets that make up central bank data sets and identify groups of assets that form monetary aggregates composed of both capital certain and risky, capital uncertain, assets. We construct monetary aggregates for the US and UK using a superlative index and relax a key assumption of the Consumption Capital Asset Pricing Model (CCAPM), a one year planning horizon, to introduce forecasted returns on risky assets. Our new risky monetary aggregates perform well in VAR tests. Economists are recommended to explore risky assets as providers of liquidity services in future research.
Keywords: Risk, Capital Asset Pricing Model, Liquidity, Divisia Money