nep-fmk New Economics Papers
on Financial Markets
Issue of 2015‒10‒04
four papers chosen by

  1. Aggregate Risk and Efficiency of Mutual Funds By Simas Kucinskas
  2. Structure and Liquidity in Treasury Markets : A speech at the Brookings Institution, Washington, D.C., August 3, 2015. By Powell, Jerome H.
  3. UK Term Structure Decompositions at the Zero Lower Bound By Andrea Carriero; Sarah Mouabbi; Elisabetta Vangelista
  4. Modeling Latin-American Stock Markets Volatility: Varying Probabilities and Mean Reversion in a Random Level Shifts Model By Gabriel Rodríguez

  1. By: Simas Kucinskas (VU University Amsterdam, the Netherlands)
    Abstract: I analyze welfare properties of mutual funds in the Diamond-Dybvig model with two sources of aggregate risk: undiversifiable interest rate risk and shocks to aggregate liquidity demand. Mutual funds are inefficient when the economy faces undiversifiable interest rate risk. However, if only aggregate liquidity demand is stochastic, mutual funds can implement the social optimum even when liquidity demand is not directly observed.
    Keywords: Mutual funds; equity contracts; liquidity creation; liquidity insurance; aggregate risk
    JEL: D91 E61 G21 G23 G28
    Date: 2015–09–24
  2. By: Powell, Jerome H. (Board of Governors of the Federal Reserve System (U.S.))
    Date: 2015–08–03
  3. By: Andrea Carriero (Queen Mary University of London); Sarah Mouabbi (Banque de France); Elisabetta Vangelista (UK Debt Management Office)
    Abstract: This paper employs a Zero Lower Bound (ZLB) consistent shadow-rate model to decompose UK nominal yields into expectation and term premia components. Compared to a standard affine term structure model, it performs relatively better in a ZLB setting and effectively captures the countercyclical nature of term premia. The ZLB model is then exploited to estimate inflation expectations and risk premia. This entails jointly pricing and decomposing nominal and real UK yields. We find evidence that medium- and long-term inflation expectations are contained within narrower bounds since the early 1990s, suggesting monetary policy credibility improved after the introduction of inflation targeting.
    Keywords: No-arbitrage, Term structure, Zero-lower bound, Risk premia, Inflation Expectations
    JEL: E31 E43 E52 E58 G12
    Date: 2015–09
  4. By: Gabriel Rodríguez (Departamento de Economía de la PUC del Perú)
    Abstract: Following Xu and Perron (2014), we applied the extended RLS model to the daily stock market returns of Argentina, Brazil, Chile, Mexico and Peru. This model replaces the constant probability of level shifts for the entire sample with varying probabilities that record periods with extremely negative returns; and furthermore, it incorporates a mean reversion mechanism with which the magnitude and the sign of the level shift component will vary in accordance with past level shifts that deviate from the long-term mean. Therefore, four RLS models are estimated: the basic RLS, the RLS with varying probabilities, the RLS with mean reversion, and a combined RLS model with mean reversion and varying probabilities. The results show that the estimated parameters are highly signiÖcant, especially that of the mean reversion model. An analysis is also performed of ARFIMA and GARCH models in the presence of level shifts, which shows that once these shifts are taken into account in the modeling, the long memory characteristics and GARCH e§ects disappear. Our forecasting analysis conÖrms that the RLS models are more accurate than other classic long-memory models. Resumen Siguiendo el trabajo de Xu y Perron (2014), en este documento se aplica el modelo extendido de cambios de nivel aleatorios (RLS) a los retornos diarios de los mercados bursátiles de Argentina, Brasil, Chile, Mexico y Perú. A diferencia del modelo RLS básico, en este modelo se usan probabilidades cambiantes asociadas a periodos de retornos extremadamente negativos y además se incorpora un mecanismo de reversión a la media el cual depende de los cambios de nivel pasados y de las desviaciones de la media de largo plazo. Así, se estiman cuatro modelos de cambios de nivel aleatorios: el modelos RLS básico, el modelo RLS con probabilidades variantes, el modelo RLS con reversión a la media y …finalmente, el modelo RLS que combina los dos aspectos ya mencionados. Los resultados muestran que los coe…cientes estimados son signi…cativos, en especial cuando se usa el modelo RLS con reversión a la media. Asimismo, se realizan estimaciones de modelos ARFIMA y GARCH a las series de volatilidad a las cuales se le ha sustraído el componente de cambios de nivel. Los resultados, muestran que una vez que dichos componentes son tomados en cuenta, las características de larga memoria y efectos GARCH desaparecen. Finalmente, un análisis de predicción es proporcionado el cual confi…rma que los modelos RLS son más e…ficientes que otros modelos clásicos de larga memoria. JEL Classification-JEL:
    Keywords: Random Level Shifts Model, Volatility, Long Memory, GARCH, Latin-American Stock Markets, Varying Probabilities, Mean Reversion, Forecasting, Larga Memoria, Mercados Bursátiles de América Latina, modelo con Cambios de Nivel Aleatorios, Predicción, Probabilidades Variantes, Reversión a la Media, Volatilidad
    Date: 2015

General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.