Fiscal imbalances, inflation and sovereign default dynamics

Resumen: La cuestión central que este artículo busca responder es como la política monetaria puede afectar el comportamiento de equilibrio de primas por riesgo soberano y cesación de pagos. El artículo se basa en el modelo de “una-tasa-interés”. La deuda pública se hace riesgosa a causa de una política fiscal activa, como en Uribe (2006), reflejando la habilidad limitada de la autoridad fiscal para controlar el superávit primario. El problema de insolvencia es debido a una oleada de mala suerte (shocks negativos que afectan el superávit primario). Pero en contraste a los resultados de Uribe, a medida que aumenta el costo de la deuda soberana (que resulta de un excedente primario débil), la cesación de pagos se anticipa y es reflejada por una creciente prima de riesgo en el país y una probabilidad de cesación de pagos. La cesación de pagos se define como un incumplimiento de un acuerdo contractual y por ende la decisión es tomada por la autoridad fiscal. Mientras tanto, objetivos conflictivos entre la autoridad monetaria y fiscal juegan un rol importante en llevar a la autoridad fiscal a la cesación de pagos sobre sus pasivos. La característica de la política del gobierno necesaria para restaurar el equilibrio después de la cesación de pagos también es analizada.

Los determinantes de la rentabilidad primaria de los bonos de titulización hipotecaria: la influencia de las estructuras multitramo

[ES] Este artículo analiza los determinantes de la rentabilidad primaria de los bonos de titulización hipotecaria (conocidos en la literatura como mortgage backed securities, o MBS) emitidos en España durante el periodo 1993-2011, periodo en el que el mercado español llegó a convertirse en el más importante de Europa continental. Los resultados obtenidos sobre el análisis de la población completa de MBS emitidos (262 tramos configurados sobre 94 fondos de titulización) indican que la estructuración multitramo de los MBS ha ayudado a reducir el riesgo percibido global de las emisiones de MBS, mediante la generación de mercados más completos y la reducción de los problemas derivados de la existencia de asimetrías informativas implícitas en el proceso de selección de los activos transmitidos por parte de la entidad cedente. Esta reducción del riesgo percibido ha tenido un efecto directo sobre la rentabilidad ofrecida por los bonos de titulización emitidos. Además, no se encuentran evidencias de que la emisión de MBS persiga la transmisión efectiva de riesgos, más bien al contrario. Las Entidades de crédito, por lo general, han retenido los tramos de primeras pérdidas, lo que ha contribuido a mantener en niveles muy bajos (por debajo de la rentabilidad de la deuda soberana) la rentabilidad ofrecida por los MBS. Precisamente, el escaso diferencial ofrecido por los bonos de titulización se debe a que los tramos retenidos no han ofrecido primas de rentabilidad ajustadas al riesgo inherente.

Wealth Wise and Culture Kind

Meyrick, Robert, 'Wealth Wise and Culture Kind', In: 'Things of Beauty: What Two Sisters did for Wales', (Cardiff: National Museum Wales Books), pp.96-111, 2007 RAE2008

Developing the untapped wealth of Britain's ?Celtic Fringe? : Water engineering and the Welsh landscape, 1870 ? 1960

Roberts, O. (2006). Developing the untapped wealth of Britain's ?Celtic fringe?: water engineering and the Welsh landscape, 1870-1960. Landscape Research. 31(2), pp.121-133. RAE2008

Jewish Top Wealth-Holders in Britain, 1809-1909

Rubinstein, W. (2002). Jewish Top Wealth-Holders in Britain, 1809-1909. Jewish Historical Studies. 37, pp.133-161. RAE2008

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.

SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequence to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Investment funds: What next?

Investment funds provide a low cost method of sharing in the rewards from capitalism. Recently “alternative investments” such as hedge funds have grown rapidly and the trading strategies open to hedge funds are now becoming available to mutual funds and even to ordinary retail investors. In this paper we analyze problems in assessing fund performance and the prospects for investment fund sectors. Choosing genuine outperformers among top funds requires a careful assessment of non-normality, order statistics and the possibility of false discoveries. The risk adjusted performance of the average hedge fund over the last 10-15 is actually not that impressive, although the “top” funds do appear to have statistically significant positive alphas.

Liquidity risk and the performance of UK mutual funds

We examine the role of liquidity risk, both as a stock characteristic as well as systematic liquidity risk, in UK mutual fund performance for the first time. Using four alternative measures of stock liquidity we extract principal components across stocks in order to construct systematic or market liquidity factors. We find that on average UK mutual funds are tilted towards liquid stocks (except for small stock funds as might be expected) but that, counter-intuitively, liquidity as a stock characteristic is positively priced in the cross-section of fund performance. We find that systematic liquidity risk is positively priced in the cross-section of fund performance. Overall, our results reveal a strong role for stock liquidity level and systematic liquidity risk in fund performance evaluation models.