Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model

This paper makes use of the novel Generalized Stochastic Unit Root (GSTUR) model, Bayesian model estimation and model comparison techniques to investigate the presence of a deterministic time trend in economic series. The model is specified to allow for changes in persistence over time, such as shifts from stationarity I(0) to nonstationarity I(1) or vice versa. This uncertainty raises the crucial question about how sure one can be that an economic time series has a deterministic trend when there is a change in the underlying properties. Empirical analysis indicates that the GSTUR model could provide new insights on time series studies.Stochastic Unit Root; MCMC; Bayesian

Have Betting Exchanges Corrupted Horse Racing?

Betting exchanges allow punters to bet on a horse to lose a race. This, many argue, has opened up the sport to a new form of corruption, where races will be deliberately lost in order to profit from betting. We examine whether anecdotal evidence of the fixing of horses to lose—of which there are many examples—is indicative of wider corruption. Following a “forensic economics” approach, we build an asymmetric information model of exchange betting and take it to betting data on 9,560 races run in 2013/2014. We find no evidence of the widespread corruption of horse racing by the betting exchanges

Bayesian Inference in a Stochastic Volatility Nelson-Siegel Model

In this paper, we develop and apply Bayesian inference for an extended Nelson- Siegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference. Applying the SVNS model to monthly U.S. zero-coupon yields, we find significant evidence for time-varying volatility in the yield factors. This is mostly true for the level and slope volatility revealing also the highest persistence. It turns out that the inclusion of stochastic volatility improves the model's goodness-of-fit and clearly reduces the forecasting uncertainty particularly in low-volatility periods. The proposed approach is shown to work efficiently and is easily adapted to alternative specifications of dynamic factor models revealing (multivariate) stochastic volatility.term structure of interest rates, stochastic volatility, dynamic factor model, Markov chain Monte Carlo

Bayesian Estimation and Model Selection in the Generalised Stochastic Unit Root Model

We develop Bayesian techniques for estimation and model comparison in a novel Generalised Stochastic Unit Root (GSTUR) model. This allows us to investigate the presence of a deterministic time trend in economic series, while allowing the degree of persistence to change over time. In particular the model allows for shifts from stationarity I(0) to nonstationarity I(1) or vice versa. The empirical analysis demonstrates that the GSTUR model provides new insights on the properties of some macroeconomic time series such as stock market indices, in ation and ex- change rates.Stochastic Unit Root, MCMC, Bayesian

Bayesian inference and forecasting in the stationary bilinear model

A stationary bilinear (SB) model can be used to describe processes with a time-varying degree of persistence that depends on past shocks. This study develops methods for Bayesian inference, model comparison, and forecasting in the SB model. Using monthly U.K. inflation data, we find that the SB model outperforms the random walk, first order autoregressive AR(1), and autoregressive moving average ARMA(1,1) models in terms of root mean squared forecast errors. In addition, the SB model is superior to these three models in terms of predictive likelihood for the majority of forecast observations

Selection and incentives in contests: evidence from horse racing

The designer of internal labour market promotion contests must balance the need to select the best candidate with the need to provide incentives for all candidates. We use an extensive data set from horse racing – where there is abundant variation in contest design features – to analyse if there are particular features that help to achieve these two objectives. We find that contests with higher prize money and fewer participants are the most successful at achieving the dual remit of selection and incentives

The shale revolution, geopolitical risk, and oil price volatility

The U.S. shale revolution, using new technologies to extract crude oil, has led to new dynamics in the supply side of the global oil market. We ask whether the shale revolution has dampened the role of geopolitical risk in oil price volatility. We extend a reduced form Structural Break Threshold Vector Autoregressive (SBT-VAR) model to a structural SBT- VAR model and identify the structural innovations by allowing conditional heteroskedasticity. Compared with the conventional reduced form VAR and TVAR models, an SBT-VAR with a constant threshold and a break in April 2014 are supported by the data. We then analyse the conditional (co)variance impulse response concerning two distinct shock scenarios, one with only a geopolitical risk shock, and the other with a simultaneous shale production shock and a geopolitical risk shock. The volatility responses are due to the identified contemporaneous relationships amongst geopolitical risk, shale production and oil prices, and are conditional on volatilities at the points in time. With the extra unit shale production shock, we find that the volatility response of oil prices to a geopolitical risk shock is higher, but the response is less correlated with the geopolitical risk factor

The wisdom of large and small crowds: Evidence from repeated natural experiments in sports betting

Prediction markets have proved excellent tools for forecasting, outperforming experts and polls in many settings. But do larger markets, with a wider participation, perform better than smaller markets? This paper analyses a series of repeated natural experiments in sports betting. The Queen’s Club Tennis Championships are held every year, but every other year the Championships clash with a major soccer tournament. We find that tennis betting prices become significantly less informative when participation rates are affected adversely by the clashing soccer tournament. This suggests that measures which increase prediction market participation may lead to a greater forecast accuracy