New Economics Papers
on Market Microstructure
Issue of 2013‒11‒14
three papers chosen by
Thanos Verousis

  1. Dodging the Steamroller: Fundamentals versus the Carry Trade By Copeland, Laurence; Lu, Wenna
  2. Uncertainty and bank wholesale funding By Dinger, Valeriya; Craig, Ben
  3. There is a VaR Beyond Usual Approximations By Marie Kratz

  1. By: Copeland, Laurence (Cardiff Business School); Lu, Wenna
    Abstract: Although, according to uncovered interest rate parity, exchange rates should move so as to prevent the carry trade being systematically profitable, there is a vast empirical literature demonstrating the opposite. High interest currencies more often tend to appreciate rather than depreciate, as noted by Fama (1983). In this paper, we treat volatility as the critical state variable and show that positive returns to the carry trade are overwhelmingly generated in the low-volatility “normal” state, whereas the high-volatility state is associated with lower returns or with losses as currencies revert to the long run level approximated by their mean real exchange rate – in other words, purchasing-power parity (PPP) tends to reassert itself, at least to some extent, during periods of turbulence. We confirm these results by comparing the returns from three possible monthly trading strategies.
    Keywords: carry trade; trading strategies; currency portfolios
    JEL: F3 G1
    Date: 2013–11
  2. By: Dinger, Valeriya; Craig, Ben
    Abstract: In this paper we relate a bank's choice between retail and wholesale liabilities to real economic uncertainty and the resulting volatility of bank loan volumes. We argue that since the volume of retail deposits is slow and costly to adjust to shocks in the volume of bank assets, banks facing more intense uncertainty and more volatile loan demand tend to employ more wholesale liabilities rather than retail deposits. We empirically confirm this argument using a unique dataset constructed from the weekly reports of the 122 largest U.S. commercial banks. The high frequency of the data allows us to employ dynamic identification schemes. Given the evidence presented in this paper we argue that regulatory measures targeting a cap on wholesale funding would limit funding uncertainty but will increase the exposure to asset-side shocks. --
    Keywords: wholesale funding,retail deposits,bank uncertainty,loan volume volatility
    JEL: G21 E44
    Date: 2013
  3. By: Marie Kratz (SID - Information Systems / Decision Sciences Department - ESSEC Business School, MAP5 - Mathématiques appliquées Paris 5 - CNRS : UMR8145 - Université Paris V - Paris Descartes)
    Abstract: Basel II and Solvency 2 both use the Value-at Risk (VaR) as the risk measure to compute the Capital Requirements. In practice, to calibrate the VaR, a normal approximation is often chosen for the unknown distribution of the yearly log returns of financial assets. This is usually justified by the use of the Central Limit Theorem (CLT), when assuming aggregation of independent and identically distributed (iid) observations in the portfolio model. Such a choice of modeling, in particular using light tail distributions, has proven during the crisis of 2008/2009 to be an inadequate approximation when dealing with the presence of extreme returns; as a consequence, it leads to a gross underestimation of the risks. The main objective of our study is to obtain the most accurate evaluations of the aggregated risks distribution and risk measures when working on financial or insurance data under the presence of heavy tail and to provide practical solutions for accurately estimating high quantiles of aggregated risks. We explore a new method, called Normex, to handle this problem numerically as well as theoretically, based on properties of upper order statistics. Normex provides accurate results, only weakly dependent upon the sample size and the tail index. We compare it with existing methods.
    Keywords: Aggregated risk ; (refined) Berry-Esséen Inequality ; (generalized) Central Limit Theorem ; Conditional (Pareto) Distribution ; Conditional (Pareto) Moment ; Convolution ; Expected Short Fall ; Extreme Values ; Financial Data ; High Frequency Data ; Market Risk ; Order Statistics ; Pareto Distribution ; Rate of Convergence ; Risk Measures ; Stable Distribution ; Value-at-Risk
    Date: 2013–11

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