An analysis on the performance of the forward and return interaction channels in DVB-RCS

The next generation consumer level interactive services require reliable and constant communication for both mobile and static users. The Digital Video Broadcasting ( DVB) group has exploited the rapidly increasing satellite technology for the provision of interactive services and launched a standard called Digital Video Broadcast through Return Channel Satellite (DYB-RCS). DVB-RCS relies on DVB-Satellite (DVB-S) for the provision of forward channel. The Digital Signal processing (DSP) implemented in the satellite channel adapter block of these standards use powerful channel coding and modulation techniques. The investigation is concentrated towards the Forward Error Correction (FEC) of the satellite channel adapter block, which will help in determining, how the technology copes with the varying channel conditions and user requirements(1).

Pricing inefficiencies in private real estate markets using total return swaps

Efficient markets should guarantee the existence of zero spreads for total return swaps. However, real estate markets have recorded values that are significantly different from zero in both directions. Possible explanations might suggest non-rational behaviour by inexperienced market players or unusual features of the underlying asset market. We find that institutional characteristics in the underlying market lead to market inefficiencies and, hence, to the creation of a rational trading window with upper and lower bounds within which transactions do not offer arbitrage opportunities. Given the existence of this rational trading window, we also argue that the observed spreads can substantially be explained by trading imbalances due to the limited liquidity of a newly formed market and/or to the effect of market sentiment, complementing explanations based on the lag between underlying market returns and index returns.

The serialization and publication of 'The return of the native': a new Thomas Hardy letter

This article presents and contextualises a newly-discovered letter by Thomas Hardy, housed in the Chatto & Windus archive at the University of Reading. The letter sheds new light on the publishing history of Hardy's novel 'The return of the native'

Case studies in interannual to decadal climate predictability

The predictability of ocean and climate variables is investigated, using a perfect model-based case study approach that recognises that predictability is dependent on the initial climate state. In line with previous studies, large scale ocean variables, show predictability for several years or more; by contrast, the predictability of climate variables is generally limited to, 2 years at most. That predictability shows high sensitivity to the initial state is demonstrated by predictable climate signals, arising in different regions, variables and seasons for different initial conditions. The predictability of climate variables, in the second year is of particular interest, because this is beyond the timescale that is usually considered to be the limit, of seasonal predictability. For different initial conditions, second year predictability is found in: temperatures in southeastern, North America (winter) and western Europe (winter and summer), and precipitation in India (summer monsoon) and in the tropical, South Atlantic. Second year predictability arises either from persistence of large-scale sea surface temperature (SST) and, related ocean heat content anomalies, particularly in regions such as the North Atlantic and Southern Ocean, or from mechanisms, that involve El Nino Southern Oscillation (ENSO) dynamics.

Time series multistep-ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices.

Processes governing the predictability of the Atlantic meridional overturning circulation in a coupled GCM

The processes that govern the predictability of decadal variations in the North Atlantic meridional overturning circulation (MOC) are investigated in a long control simulation of the ECHO-G coupled atmosphere–ocean model. We elucidate the roles of local stochastic forcing by the atmosphere, and other potential ocean processes, and use our results to build a predictive regression model. The primary influence on MOC variability is found to come from air–sea heat fluxes over the Eastern Labrador Sea. The maximum correlation between such anomalies and the variations in the MOC occurs at a lead time of 2 years, but we demonstrate that the MOC integrates the heat flux variations over a period of 10 years. The corresponding univariate regression model accounts for 74.5% of the interannual variability in the MOC (after the Ekman component has been removed). Dense anomalies to the south of the Greenland-Scotland ridge are also shown to precede the overturning variations by 4–6 years, and provide a second predictor. With the inclusion of this second predictor the resulting regression model explains 82.8% of the total variance of the MOC. This final bivariate model is also tested during large rapid decadal overturning events. The sign of the rapid change is always well represented by the bivariate model, but the magnitude is usually underestimated, suggesting that other processes are also important for these large rapid decadal changes in the MOC.

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

Real estate investment in global financial centers: risk, return and contagion

Global financial activity is heavily concentrated in a small number of world cities –international financial centers. The office markets in those cities receive significant flows of investment capital. The growing specialization of activity in IFCs and innovations in real estate investment vehicles lock developer, occupier, investment, and finance markets together, creating common patterns of movement and transmitting shocks from one office market throughout the system. International real estate investment strategies that fail to recognize this common source of volatility and risk may fail to deliver the diversification benefits sought.

Depreciation and its impact on the total return of UK commercial real estate, 1994-2003

Depreciation is a key element of understanding the returns from and price of commercial real estate. Understanding its impact is important for asset allocation models and asset management decisions. It is a key input into well-constructed pricing models and its impact on indices of commercial real estate prices needs to be recognised. There have been a number of previous studies of the impact of depreciation on real estate, particularly in the UK. Law (2004) analysed all of these studies and found that the seemingly consistent results were an illusion as they all used a variety of measurement methods and data. In addition, none of these studies examined impact on total returns; they examined either rental value depreciation alone or rental and capital value depreciation. This study seeks to rectify this omission, adopting the best practice measurement framework set out by Law (2004). Using individual property data from the UK Investment Property Databank for the 10-year period between 1994 and 2003, rental and capital depreciation, capital expenditure rates, and total return series for the data sample and for a benchmark are calculated for 10 market segments. The results are complicated by the period of analysis which started in the aftermath of the major UK real estate recession of the early 1990s, but they give important insights into the impact of depreciation in different segments of the UK real estate investment market.

The underlying return generating factors for REIT returns: an application of independent component analysis

Multi-factor approaches to analysis of real estate returns have, since the pioneering work of Chan, Hendershott and Sanders (1990), emphasised a macro-variables approach in preference to the latent factor approach that formed the original basis of the arbitrage pricing theory. With increasing use of high frequency data and trading strategies and with a growing emphasis on the risks of extreme events, the macro-variable procedure has some deficiencies. This paper explores a third way, with the use of an alternative to the standard principal components approach – independent components analysis (ICA). ICA seeks higher moment independence and maximises in relation to a chosen risk parameter. We apply an ICA based on kurtosis maximisation to weekly US REIT data using a kurtosis maximising algorithm. The results show that ICA is successful in capturing the kurtosis characteristics of REIT returns, offering possibilities for the development of risk management strategies that are sensitive to extreme events and tail distributions.

Non-normal real estate return distributions by property type in the UK

Investment risk models with infinite variance provide a better description of distributions of individual property returns in the IPD UK database over the period 1981 to 2003 than normally distributed risk models. This finding mirrors results in the US and Australia using identical methodology. Real estate investment risk is heteroskedastic, but the characteristic exponent of the investment risk function is constant across time – yet it may vary by property type. Asset diversification is far less effective at reducing the impact of non‐systematic investment risk on real estate portfolios than in the case of assets with normally distributed investment risk. The results, therefore, indicate that multi‐risk factor portfolio allocation models based on measures of investment codependence from finite‐variance statistics are ineffective in the real estate context

Individual assets, market structure and the drivers of return

Much prior research on the structure and performance of UK real estate portfolios has relied on aggregated measures for sector and region. For these groupings to have validity, the performance of individual properties within each group should be similar. This paper analyses a sample of 1,200 properties using multiple discriminant analysis and cluster analysis techniques. It is shown that conventional property type and spatial classifications do not capture the variation in return behaviour at the individual building level. The major feature is heterogeneity - but there may be distinctions between growth and income properties and between single and multi-let properties that could help refine portfolio structures.