Monte Carlo derivative pricing with partial information in a class of doubly stochastic Poisson processes with marks

To model intraday stock price movements we propose a class of marked doubly stochastic Poisson processes, whose intensity process can be interpreted in terms of the effect of information release on market activity. Assuming a partial information setting in which market agents are restricted to observe only the price process, a filtering algorithm is applied to compute, by Monte Carlo approximation, contingent claim prices, when the dynamics of the price process is given under a martingale measure. In particular, conditions for the existence of the minimal martingale measure Q are derived, and properties of the model under Q are studied.Minimal martingale measure, News arrival, Marked point process, Nonlinear filtering, Reversible jump Markov chain Monte Carlo, Ultra high frequency data

Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news

Este documento estudia las reacciones de los mercados financieros de Estados Unidos a nuevas noticias de la prensa desde enero de 2019 hasta el primero de mayo de 2020. Con este fin, construimos medidas del contenido y del sentimiento de las noticias mediante el desarrollo de índices apropiados a partir de los titulares y fragmentos de The New York Times, utilizando técnicas de aprendizaje automático no supervisado. En particular, usamos el modelo Asignación Latente de Dirichlet para inferir el contenido (temas) de los artículos, y Word Embedding (implementado con el modelo Skip-gram) y K-Medias para medir su sentimiento (incertidumbre). De esta forma, elaboramos un conjunto de índices de incertidumbre temáticos diarios. Estos índices se utilizan luego para explicar el comportamiento de los mercados financieros de Estados Unidos mediante la implementación de un conjunto de modelos EGARCH. En conclusión, encontramos que dos de los índices de incertidumbre temáticos (uno relacionado con noticias del COVID-19 y otro con noticias de la guerra comercial) explican gran parte de los movimientos en los mercados financieros desde principios de 2019 hasta los cuatro primeros meses de 2020. Además, encontramos que el índice de incertidumbre temático relacionado con la economía y la Reserva Federal está positivamente relacionado con los mercados financieros, capturando las acciones de la Reserva Federal durante períodos de incertidumbre.This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and sentiment of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning techniques. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding (implemented with the Skip-gram model) and K-Means to measure their sentiment (uncertainty). In this way, we arrive at the definition of a set of daily topic-specific uncertainty indices. These indices are then used to find explanations for the behaviour of the US financial markets by implementing a batch of EGARCH models. In substance, we find that two topic-specific uncertainty indices, one related to COVID-19 news and the other to trade war news, explain the bulk of the movements in the financial markets from the beginning of 2019 to end-April 2020. Moreover, we find that the topic-specific uncertainty index related to the economy and the Federal Reserve is positively related to the financial markets, meaning that our index is able to capture actions of the Federal Reserve during periods of uncertainty

“Making text talk”: the minutes of the Central Bank of Brazil and the real economy

Este documento investiga la relación entre las opiniones expresadas en las minutas de las reuniones del Comité de Política Monetaria del Banco Central de Brasil (COPOM) y la economía real. Construimos medidas de las minutas del COPOM utilizando varios algoritmos de aprendizaje automático. En primer lugar, creamos medidas del contenido de los párrafos de las minutas, utilizando el algoritmo Asignación Latente de Dirichlet (LDA, por sus siglas en inglés). En segundo lugar, construimos un índice de incertidumbre para las minutas, utilizando los modelos Word Embedding y K-Medias. Combinando los anteriores índices, creamos dos índices de incertidumbre temáticos. El primero de estos se construye con los párrafos que tienen una mayor probabilidad de temas relacionados con las «condiciones económicas generales». El segundo índice de incertidumbre temático se construye con los párrafos que tienen una mayor probabilidad de temas relacionados con la «inflación» y la «discusión de política monetaria». Finalmente, a través de un modelo Estructural de Vectores Autorregresivos, estudiamos los efectos de estos índices de incertidumbre en algunas variables macroeconómicas de Brasil. Nuestros resultados muestran que una mayor incertidumbre conduce a un descenso de la inflación, del tipo de cambio, de la producción industrial y del comercio al por menor en el período comprendido entre enero de 2000 y julio de 2019.This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019

Monetary policy uncertainty in Mexico: an unsupervised approach

En este documento se estudia y cuantifica la incertidumbre en las minutas de las reuniones de la Junta de Gobierno del Banco de México y su relación con las variables de política monetaria. En particular, construimos dos índices de incertidumbre para la versión en español de las minutas utilizando técnicas de aprendizaje automático no supervisado. El primer índice de incertidumbre es construido utilizando la técnica Asignación Latente de Dirichlet (LDA por sus siglas en inglés), mientras que el segundo índice de incertidumbre se construye utilizando los modelos Skip-Gram y K-Medias. También elaboramos índices de incertidumbre para las tres secciones principales de las minutas. Encontramos que una mayor incertidumbre en las minutas está relacionada con un aumento de la inflación y de la oferta monetaria. Nuestros resultados también muestran que un aumento de la incertidumbre conduce a cambios del mismo signo —pero de diferente magnitud— de la tasa de interés interbancaria y de la tasa de interés objetivo. También encontramos que un incremento de la incertidumbre conduce a una depreciación del peso mexicano con respecto del dólar estadounidense en el mismo período del shock, seguido de una apreciación en el período siguiente.We study and measure uncertainty in the minutes of the meetings of the board of governors of the Central Bank of Mexico and relate it to monetary policy variables. In particular, we construct two uncertainty indices for the Spanish version of the minutes using unsupervised machine learning techniques. The first uncertainty index is constructed exploiting Latent Dirichlet Allocation (LDA), whereas the second uses the Skip-Gram model and K-Means. We also create uncertainty indices for the three main sections of the minutes. We find that higher uncertainty in the minutes is related to an increase in inflation and money supply. Our results also show that a unit shock in uncertainty leads to changes of the same sign but different magnitude in the inter-bank interest rate and the target interest rate. We also find that a unit shock in uncertainty leads to a depreciation of the Mexican peso with respect to the US dollar in the same period of the shock, which is followed by appreciation in the subsequent period

Experimental tests of hidden variable theories from dBB to Stochastic Electrodynamics

In this paper we present some of our experimental results on testing hidden variable theories, which range from Bell inequalities measurements to a conclusive test of stochastic electrodynamics

Force produced after stretch in sarcomeres and half-sarcomeres isolated from skeletal muscles

The goal of this study was to evaluate if isolated sarcomeres and half-sarcomeres produce a long-lasting increase in force after a stretch is imposed during activation. Single and half-sarcomeres were isolated from myofibrils using micro-needles, which were also used for force measurements. After full force development, both preparations were stretched by different magnitudes. The sarcomere length (SL) or half-sarcomere length variations (HSL) were extracted by measuring the initial and final distances from the Z-line to the adjacent Z-line or to a region externally adjacent to the M-line of the sarcomere, respectively. Half-sarcomeres generated approximately the same amount of isometric force (29.0 ± SD 15.5 nN·μm(−2)) as single sarcomeres (32.1 ± SD 15.3 nN·μm(−2)) when activated. In both cases, the steady-state forces after stretch were higher than the forces during isometric contractions at similar conditions. The results suggest that stretch-induced force enhancement is partly caused by proteins within the half-sarcomere

Purely game-theoretic random sequences: I. Strong law of large numbers and law of the iterated logarithm

Random sequences are usually defined with respect to a probability distribution P (a sigma-additive set function, normed to one, defined over a sigma-algebra) assuming Kolmogorov's axioms for probability theory. In this paper, without using this axiomatics, we give a definition of random (typical) sequences taking as primitive the notion of a martingale and using the principle of the excluded gambling strategy. In this purely game-theoretic framework, no probability distribution or, partially or fully specified, system of conditional probability distributions need to be introduced. For these typical sequences, we prove direct algorithmic versions of Kolmogorov's strong law of large numbers and of the upper half of Kolmogorov's law of the iterated logarithm