Canonical term-structure models with observable factors and the dynamics of bond risk premiums

We study the dynamics of risk premiums on the German bond market, employing no-arbitrage term-structure models with both observable and unobservable state variables, recently popularized by Ang and Piazzesi (2003). We conduct a specification analisys based on a new canonical representation for this class of models. We find that risk premiums display a considerable variability over time, are strongly counter-cyclical and bear no significant relation to inflation.term structure models, yield curve, risk premium

A specification analysis of discrete-time no-arbitrage term structure models with observable and unobservable factors

We derive a canonical representation for the no-arbitrage discrete-time term structure models with both observable and unobservable state variables, popularized by Ang and Piazzesi (2003). We conduct a specification analysis based on this canonical representation. We show that some of the restrictions commonly imposed in the literature, most notably that of independence between observable and unobservable variables, are not necessary for identification and are rejected by formal statistical tests. Furthermore, we show that there are important differences between the estimated risk premia, impulse response functions and variance decomposition of unrestricted models, parametrized according to our canonical representation, and those of models with overidentifying restrictions.Term structure; canonical models

Bond risk premia, macroeconomic fundamentals and the exchange rate

We introduce a two-country no-arbitrage term-structure model to analyse the joint dynamics of bond yields, macroeconomic variables, and the exchange rate. The model allows to understand how exogenous shocks to the exchange rate affect the yield curves, how bond yields co-move in different countries, and how the exchange rate is influenced by the interactions between macroeconomic variables and time-varying bond risk premia. Estimating the model with US and German data, we obtain an excellent fit of the yield curves and we are able to account for up to 75 per cent of the variability of the exchange rate. We find that time-varying risk premia play a non-negligible role in exchange rate fluctuations due to the fact that a currency tends to appreciate when risk premia on long-term bonds denominated in that currency rise. A number of other novel empirical findings emerge.exchange rate, term structure, UIP

Value-at-Risk time scaling for long-term risk estimation

In this paper we discuss a general methodology to compute the market risk measure over long time horizons and at extreme percentiles, which are the typical conditions needed for estimating Economic Capital. The proposed approach extends the usual market-risk measure, ie, Value-at-Risk (VaR) at a short-term horizon and 99% confidence level, by properly applying a scaling on the short-term Profit-and-Loss (P&L) distribution. Besides the standard square-root-of-time scaling, based on normality assumptions, we consider two leptokurtic probability density function classes for fitting empirical P&L datasets and derive accurately their scaling behaviour in light of the Central Limit Theorem, interpreting time scaling as a convolution problem. Our analyses result in a range of possible VaR-scaling approaches depending on the distribution providing the best fit to empirical data, the desired percentile level and the time horizon of the Economic Capital calculation. After assessing the different approaches on a test equity trading portfolio, it emerges that the choice of the VaR-scaling approach can affect substantially the Economic Capital calculation. In particular, the use of a convolution-based approach could lead to significantly larger risk measures (by up to a factor of four) than those calculated using Normal assumptions on the P&L distribution.Comment: Pre-Print version, submitted to The Journal of Risk. 18 pages, 17 figure

Attracted but Unsatisfied: The Effects of Arousing Content on Television Consumption Choices

This paper investigates experimentally the effects of arousing content on viewing choices and satisfaction in television consumption. We test the hypothesis that the portrayal of arousing content combines high attraction and low satisfaction and is thus responsible for suboptimal choices. In our experiment, subjects can choose among three programs during a viewing session. In the experimental condition, one of the three programs portrays a violent verbal conflict, whereas in the control condition the same program portrays a calm debate. A post-experimental questionnaire is used to assess subjects' satisfaction with the programs and the overall viewing experience. The results support the hypothesis: the presence of arousing content causes sub- jects to watch more of a given program, although they experience lower content-specific and overall satisfaction. Arousing contents also significantly increase the discrepancy between actual and desired viewing.Rational Choice, Audience, Television, Satisfaction, Arousing content, Laboratory Experiments.

Hairy AdS black holes with a toroidal horizon in 4D Einstein-nonlinear σ\sigma -model system

An exact hairy asymptotically locally AdS black hole solution with a flat horizon in the Einstein-nonlinear sigma model system in (3+1) dimensions is constructed. The ansatz for the nonlinear $SU(2)$ field is regular everywhere and depends explicitly on Killing coordinates, but in such a way that its energy-momentum tensor is compatible with a metric with Killing fields. The solution is characterized by a discrete parameter which has neither topological nor Noether charge associated with it and therefore represents a hair. A $U(1)$ gauge field interacting with Einstein gravity can also be included. The thermodynamics is analyzed. Interestingly, the hairy black hole is always thermodynamically favored with respect to the corresponding black hole with vanishing Pionic field.Comment: 15 pages, 1 figure. Accepted for publication in Physics Letters

Linguistically Motivated Vocabulary Reduction for Neural Machine Translation from Turkish to English

The necessity of using a fixed-size word vocabulary in order to control the model complexity in state-of-the-art neural machine translation (NMT) systems is an important bottleneck on performance, especially for morphologically rich languages. Conventional methods that aim to overcome this problem by using sub-word or character-level representations solely rely on statistics and disregard the linguistic properties of words, which leads to interruptions in the word structure and causes semantic and syntactic losses. In this paper, we propose a new vocabulary reduction method for NMT, which can reduce the vocabulary of a given input corpus at any rate while also considering the morphological properties of the language. Our method is based on unsupervised morphology learning and can be, in principle, used for pre-processing any language pair. We also present an alternative word segmentation method based on supervised morphological analysis, which aids us in measuring the accuracy of our model. We evaluate our method in Turkish-to-English NMT task where the input language is morphologically rich and agglutinative. We analyze different representation methods in terms of translation accuracy as well as the semantic and syntactic properties of the generated output. Our method obtains a significant improvement of 2.3 BLEU points over the conventional vocabulary reduction technique, showing that it can provide better accuracy in open vocabulary translation of morphologically rich languages.Comment: The 20th Annual Conference of the European Association for Machine Translation (EAMT), Research Paper, 12 page

LQG Online Learning

Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical Linear Quadratic Gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a non-trivial variation as it involves random matrices, are investigated. The obtained optimal solutions are compared with the Kalman-filter estimate of the parameter vector to be learned. It is shown that the proposed algorithm is less sensitive to outliers with respect to the Kalman estimate (thanks to the presence of the regularization term), thus providing smoother estimates with respect to time. The basic formulation of the proposed online-learning framework refers to a discrete-time setting with a finite learning horizon and a linear model. Various extensions are investigated, including the infinite learning horizon and, via the so-called "kernel trick", the case of nonlinear models