Derivative trading and structural breaks in volatility in India: an ICSS approach

Researchers argue that ignoring the structural breaks in the time-series variance can cause significant upward biases in the degree of persistence in estimated GARCH models. Against this backdrop, the present study empirically examines the effect of stock futures on the underlying stock’s volatility in India by incorporating the structural breaks with the help of ICSS test and AR (1)-GARCH (1, 1) model for 30 most liquid and actively traded underlying stocks and their associated futures contracts. The study period ranges from the 1st January 2000 or the listing date of the particular stock (whichever is prior) till 31st March 2019. The study contributes to the on-going debate regarding the effect of derivatives on the underlying stock market’s volatility in two ways. Firstly, by taking into consideration the breaks in the volatility and, secondly, studying the effect of single stock futures will allow us to evaluate company-specific response to futures trading directly. The study offers a mixed outcome for the stocks under consideration. However, there is evidence of a decline in unconditional volatility for the majority of the stocks. The overall findings indicate that trading in stock futures may not have any detrimental effect on the underlying stock’s volatility.

Inventory statistics meet big data: complications for estimating numbers of species

We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of units of inventory effort (e.g., days of inventory effort) in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, NJ, USA), and outline the circumstances under which these problems may be expected to emerge.

Inventory statistics meet big data: Complications for estimating numbers of species

We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of samples in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, New Jersey, USA), and outline the circumstances under which these problems may be expected to emerge.

Inventory statistics meet big data: Complications for estimating numbers of species

We point out complications inherent in biodiversity inventory metrics when applied to large-scale datasets. The number of samples in which a species is detected saturates, such that crucial numbers of detections of rare species approach zero. Any rare errors can then come to dominate species richness estimates, creating upward biases in estimates of species numbers. We document the problem via simulations of sampling from virtual biotas, illustrate its potential using a large empirical dataset (bird records from Cape May, New Jersey, USA), and outline the circumstances under which these problems may be expected to emerge.

Differing Views of Lodging Reality: Airdna, STR, and Airbnb

Airbnb is an Internet-based firm that connects potential short-term renters with hosts who own or control rental properties. Its rapidly expanding activities are tracked by Airdna, an independent firm that generates seemingly conventional performance metrics describing Airbnb. These metrics include occupancy rates, average daily rates, and revenue per available room. However, Airdna does not adhere to long-established STR definitions for these variables. Using data from Virginia Beach, Virginia, we demonstrate that Airdna’s performance metrics exhibit notable upward biases vis-á-vis STR’s metrics. Potential rental hosts, hoteliers, tax collectors, and investors are at risk if they act on the assumption that Airdna’s metrics are comparable with widely understood measures used by STR and tourism experts.

Effects of Terms of Trade Gains and Tariff Changes on the Measurement of US Productivity Growth

The acceleration in US productivity growth since 1995 is often attributed to declining prices for information technology (IT) goods, and therefore enhanced productivity growth in that sector. We investigate an alternative explanation for these IT price movements: gains in the US terms of trade and tariff reductions, especially for IT products, which led to greater gains than shown by official indexes. We do not, however, investigate the indexes used to deflate the domestic absorption components of GDP, and if upward biases are present in those indexes that could offset some of the effects of mismeasured export and import indexes. (JEL C43, E23, F13, F14, J24)