Rational speculative bubbles: an empirical investigation of the London Stock Exchange

In recent years, a sharp divergence of London Stock Exchange equity prices from dividends has been noted. In this paper, we examine whether this divergence can be explained by reference to the existence of a speculative bubble. Three different empirical methodologies are used: variance bounds tests, bubble specification tests, and cointegration tests based on both ex post and ex ante data. We find that, stock prices diverged significantly from their fundamental values during the late 1990's, and that this divergence has all the characteristics of a bubble.

The effect of asymmetries on stock index return value-at-risk estimates

It is widely accepted that equity return volatility increases more following negative shocks rather than positive shocks. However, much of value-at-risk (VaR) analysis relies on the assumption that returns are normally distributed (a symmetric distribution). This article considers the effect of asymmetries on the evaluation and accuracy of VaR by comparing estimates based on various models.

The impact of news on measures of undiversifiable risk: evidence from the UK stock market

Using UK equity index data, this paper considers the impact of news on time varying measures of beta, the usual measure of undiversifiable risk. The empirical model implies that beta depends on news about the market and news about the sector. The asymmetric response of beta to news about the market is consistent across all sectors considered. Recent research is divided as to whether abnormalities in equity returns arise from changes in expected returns in an efficient market or over-reactions to new information. The evidence suggests that such abnormalities may be due to changes in expected returns caused by time-variation and asymmetry in beta.

Can we explain the dynamics of the UK FTSE 100 stock and stock index futures markets?

If stock and stock index futures markets are functioning properly price movements in these markets should best be described by a first order vector error correction model with the error correction term being the price differential between the two markets (the basis). Recent evidence suggests that there are more dynamics present than should be in effectively functioning markets. Using self-exciting threshold autoregressive (SETAR) models, this study analyses whether such dynamics can be related to different regimes within which the basis can fluctuate in a predictable manner without triggering arbitrage. These findings reveal that the basis shows strong evidence of autoregressive behaviour when its value is between the two thresholds but that the extra dynamics disappear once the basis moves above the upper threshold and their persistence is reduced, although not eradicated, once the basis moves below the lower threshold. This suggests that once nonlinearity associated with transactions costs is accounted for, stock and stock index futures markets function more effectively than is suggested by linear models of the pricing relationship.

Mountain ranges favour vigorous Atlantic Meridional Overturning

We use a global Ocean-Atmosphere General Circulation Model (OAGCM) to show that the major mountain ranges of the world have a significant role in maintenance of the Atlantic Meridional Overturning Circulation (AMOC). A simulation with mountains has a maximum AMOC of 18 Sv (1 Sv=106 m3 s-1) compared with ~0 Sv for a simulation without mountains. Atlantic heat transport at 25N is 1.1 PW with mountains compared to 0.2 PW without. The difference in AMOC is due to major changes in surface heat and freshwater (FW) fluxes over the Atlantic. In the Pacific changed surface fluxes lead to a meridional overturning circulation of 10 Sv. Our results suggest that the effects of mountains on the large-scale atmospheric circulation is to force the ocean towards a state with a vigorous AMOC and with no overturning in the Pacific.

A data framework for measuring the energy consumption of the non-domestic building stock

The transition to a low-carbon economy urgently demands better information on the drivers of energy consumption. UK government policy has prioritized energy efficiency in the built stock as a means of carbon reduction, but the sector is historically information poor, particularly the non-domestic building stock. This paper presents the results of a pilot study that investigated whether and how property and energy consumption data might be combined for non-domestic energy analysis. These data were combined in a ‘Non-Domestic Energy Efficiency Database’ to describe the location and physical attributes of each property and its energy consumption. The aim was to support the generation of a range of energy-efficiency statistics for the industrial, commercial and institutional sectors of the non-domestic building stock, and to provide robust evidence for national energy-efficiency and carbon-reduction policy development and monitoring. The work has brought together non-domestic energy data, property data and mapping in a ‘data framework’ for the first time. The results show what is possible when these data are integrated and the associated difficulties. A data framework offers the potential to inform energy-efficiency policy formation and to support its monitoring at a level of detail not previously possible.