The Economic Value of Volatility Timing

Numerous studies report that standard volatility models have low explanatory power, leading some researchers to question whether these models have economic value. We examine this question by using conditional mean-variance analysis to assess the value of volatility timing to short-horizon investors. We find that the volatility timing strategies outperform the unconditionally efficient static portfolios that have the same target expected return and volatility. This finding is robust to estimation risk and transaction costs.     

Talk as Technique – A Critique of the Words and Deeds Distinction in the Diffusion of Customer Service Cultures in Call Centres

ABSTRACT   This paper critically explores the common distinction made between words and deeds (or ideas and techniques) in the diffusion of management knowledge literature. The concern with whether management ideas are really being implemented in an organizational context intuitively points to the possibility of a contrast between simply talking about a practice or 'hype' and practical implementation. Drawing on empirical research on the diffusion of customer service culture in two call centres where 'verbal labour' predominates, it is argued that this distinction is important, but overdrawn. Eschewing discursive reductionism, the concepts of 'talk in work' and 'talk about work' are developed to illustrate how talk can be a technique of implementation in its own right.     

A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility

We show that, for three common SARV models, fitting a minimummean square linear filter is equivalent to fitting a GARCH model.This suggests that GARCH models may be useful for filtering,forecasting, and parameter estimation in stochastic volatilitysettings. To investigate, we use simulations to evaluate howthe three SARV models and their associated GARCH filters performunder controlled conditions and then we use daily currency andequity index returns to evaluate how the models perform in arisk management application. Although the GARCH models produceless precise forecasts than the SARV models in the simulations,it is not clear that the performance differences are large enoughto be economically meaningful. Consistent with this view, wefind that the GARCH and SARV models perform comparably in testsof conditional value-at-risk estimates using the actual data.     

Can experimental measures of sensitivity to social pressure predict public good contribution?

Public good contributions may be affected by the social demand to contribute that is implicit in them. Sensitivity to social pressure predicts behavior in paired dictator and money burning games; the evidence for effects on public good contribution is mixed.     

The impact of climate change on labour demand in the plantation sector: the case of tea production in Sri Lanka

Limited opportunities for crop switching and lengthy preharvesting periods make the plantation sector particularly vulnerable to climate change. Surprisingly, however, the economic consequences of climate change on plantation crops are seldom analysed. Drawing on a unique primary panel data set from a representative cross section of 35 tea estates in Sri Lanka over the period 2002–2014, this study implements a structural model of estate profit maximisation to estimate the elasticity of labour demand with respect to different components of weather. Results indicate a negative relationship between labour demand and rainfall in the south‐west monsoon, the north‐east monsoon and the second inter‐monsoon. A positive relationship is found between labour demand and rainfall in the first inter‐monsoon. Overall, predicted changes in rainfall by 2050 are anticipated to reduce labour demand by approximately 1,175,000 person‐days per year across Sri Lanka's tea plantation sector. This is likely to have considerable social and welfare implications, particularly for the Indian Tamil women who comprise the majority of the sector's workforce.     

Unspoken demands for farm technology

For three years in Bolivia (2002–2005) the INNOVA Project finished researching several technologies for sustainable agriculture, started by earlier DFID-funded projects. Before INNOVA started critics suggested that these technologies should be discarded in favour of a demand survey. Instead, INNOVA kept the existing technologies, but judged the demand for them with several methods (CIAL, sondeo technology fair, and others). INNOVA found that there was demand for some of the technologies, but that a survey would have missed much of the demand, which is implicit. That is, people are not initially aware of all their problems or of all the possible solutions. Over the years, farmers made more specific, sophisticated demands on the technologies, which evolved as a result. Demand and supply of farm technology are like two sides of an unfolding conversation.     

A Comparison of EPI Sampling, Probability Sampling, and Compact Segment Sampling Methods for Micro and Small Enterprises

Finding an efficient method for sampling micro- and small-enterprises (MSEs) for research and statistical reporting purposes is a challenge in developing countries, where registries of MSEs are often nonexistent or outdated. This lack of a sampling frame creates an obstacle in finding a representative sample of MSEs. This study uses computer simulations to draw samples from a census of businesses and non-businesses in the Tshwane Municipality of South Africa, using three different sampling methods: the traditional probability sampling method, the compact segment sampling method, and the World Health Organization's Expanded Programme on Immunization (EPI) sampling method. Three mechanisms by which the methods could differ are tested, the proximity selection of respondents, the at-home selection of respondents, and the use of inaccurate probability weights. The results highlight the importance of revisits and accurate probability weights, but the lesser effect of proximity selection on the samples' statistical properties.     

Price Formation and Liquidity in the U.S. Treasury Market: The Response to Public Information

The arrival of public information in the U.S. Treasury market sets off a two-stage adjustment process for prices, trading volume, and bid-ask spreads. In a brief first stage, the release of a major macroeconomic announcement induces a sharp and nearly instantaneous price change with a reduction in trading volume, demonstrating that price reactions to public information do not require trading. The spread widens dramatically at announcement, evidently driven by inventory control concerns. In a prolonged second stage, trading volume surges, price volatility persists, and spreads remain moderately wide as investors trade to reconcile residual differences in their private views.