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

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

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

Structural properties of optimal coordinate-convex policies for CAC with nonlinearly-constrained feasibility regions

Necessary optimality conditions for Call Admission Control (CAC) problems with nonlinearly-constrained feasibility regions and two classes of users are derived. The policies are restricted to the class of coordinate-convex policies. Two kinds of structural properties of the optimal policies and their robustness with respect to changes of the feasibility region are investigated: 1) general properties not depending on the revenue ratio associated with the two classes of users and 2) more specific properties depending on such a ratio. The results allow one to narrow the search for the optimal policies to a suitable subset of the set of coordinate-convex policies

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

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

High-power collective charging of a solid-state quantum battery

Quantum information theorems state that it is possible to exploit collective quantum resources to greatly enhance the charging power of quantum batteries (QBs) made of many identical elementary units. We here present and solve a model of a QB that can be engineered in solid-state architectures. It consists of $N$ two-level systems coupled to a single photonic mode in a cavity. We contrast this collective model ("Dicke QB"), whereby entanglement is genuinely created by the common photonic mode, to the one in which each two-level system is coupled to its own separate cavity mode ("Rabi QB"). By employing exact diagonalization, we demonstrate the emergence of a quantum advantage in the charging power of Dicke QBs, which scales like $\sqrt{N}$ for $N\gg 1$.Comment: 8 pages, 5 figures. Version v2 supersedes version v1 where a technical mistake was done in using the Holstein-Primakoff transformation. The quantum advantage in the maximum charging power discussed in version v1 has been found to be robust. We have also updated the list of author