New Economics Papers
on Financial Markets
Issue of 2008‒08‒06
eight papers chosen by

  1. Predicting global stock returns By Erik Hjalmarsson
  2. Local volatility calibration using an adjoint proxy By Gabriel Turinici
  3. Clustering Mutual Funds by Return and Risk Levels By Francesco Lisi; Edoardo Otranto
  4. The Dynamics of Speculative Markets: The Case of Portugal’s PSI20 By Tanya Araújo; Francisco Louçã
  5. Information Shocks, Jumps, and Price Discovery -- Evidence from the U.S. Treasury Market By George J. Jiang; Ingrid Lo; Adrien Verdelhan
  6. Modelling financial time series with SEMIFAR-GARCH model By Yuanhua Feng; Jan Beran; Keming Yu
  7. Practical Volatility Modeling for Financial Market Risk Management By Shamiri, Ahmed; Shaari, Abu Hassan; Isa, Zaidi
  8. The Oil Price Really Is A Speculative Bubble By R.S. Eckaus

  1. By: Erik Hjalmarsson
    Abstract: I test for stock return predictability in the largest and most comprehensive data set analyzed so far, using four common forecasting variables: the dividend- and earnings-price ratios, the short interest rate, and the term spread. The data contain over 20,000 monthly observations from 40 international markets, including 24 developed and 16 emerging economies. In addition, I develop new methods for predictive regressions with panel data. Inference based on the standard fixed effects estimator is shown to suffer from severe size distortions in the typical stock return regression, and an alternative robust estimator is proposed. The empirical results indicate that the short interest rate and the term spread are fairly robust predictors of stock returns in developed markets. In contrast, no strong or consistent evidence of predictability is found when considering
    Date: 2008
  2. By: Gabriel Turinici (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - CNRS : UMR7534 - Université Paris Dauphine - Paris IX)
    Abstract: We document the calibration of the local volatility in a frame- work similar to Coleman, Li and Verma. The quality of a surface is assessed through a functional to be optimized; the specificity of the approach is to separate the optimization (performed with any suitable optimization algorithm) from the computation of the functional where we use an adjoint (as in L. Jiang et. al.) to obtain an approximation; moreover our main calibration variable is the implied volatility (the procedure can also accommodate the Greeks). The procedure per- forms well on benchmarks from the literature and on FOREX data.
    Date: 2008–07–25
  3. By: Francesco Lisi; Edoardo Otranto
    Abstract: Mutual funds classifications, often made by rating agencies, are very common and sometimes criticized. In this work, a three-step statistical procedure for mutual funds classification is proposed. In the first step time series funds are characterized in terms of returns. In the second step, a clustering analysis is performed in order to obtain classes of homogeneous funds with respect to the risk levels. In particular, the risk is defined starting from an Asymmetric Threshold-GARCH model aimed to describe minimum, normal and turmoil risk. The third step merges the previous two. An application to 75 European funds belonging to 5 different categories is given.
    Keywords: Cluster, distance, GARCH models, risk
    JEL: C22 G11 G23
    Date: 2008
  4. By: Tanya Araújo; Francisco Louçã
    Date: 2008–06
  5. By: George J. Jiang; Ingrid Lo; Adrien Verdelhan
    Abstract: We examine large price changes, known as jumps, in the U.S. Treasury market. Using recently developed statistical tools, we identify price jumps in the 2-, 3-, 5-, 10-year notes and 30-year bond during the period of 2005-2006. Our results show that jumps mostly occur during prescheduled macroeconomic announcements or events. Nevertheless, market surprise based on preannouncement surveys is an imperfect predictor of bond price jumps. We find that a macroeconomic news announcement is often preceeded by an increase in market volatility and a withdrawal of liquidity, and that liquidity shocks play an important role for price jumps in U.S. Treasury market. More importantly, we present evidence that jumps serve as a dramatic form of price discovery in the sense that they help to quickly incorporate market information into bond prices.
    Keywords: Financial markets
    JEL: G12 G14
    Date: 2008
  6. By: Yuanhua Feng (Heriot-Watt University, Edinburgh); Jan Beran; Keming Yu
    Abstract: A class of semiparametric fractional autoregressive GARCH models (SEMIFARGARCH), which includes deterministic trends, difference stationarity and stationarity with short- and long-range dependence, and heteroskedastic model errors, is very powerful for modelling financial time series. This paper discusses the model fitting, including an efficient algorithm and parameter estimation of GARCH error term. So that the model can be applied in practice. We then illustrate the model and estimation methods with a few of different finance data sets.
    Keywords: Financial time series, GARCH model, SEMIFAR model, parameter estimation, kernel estimation, asymptotic property.
    Date: 2007–12–01
  7. By: Shamiri, Ahmed; Shaari, Abu Hassan; Isa, Zaidi
    Abstract: Being able to choose most suitable volatility model and distribution specification is a more demanding task. This paper introduce an analyzing procedure using the Kullback-Leibler information criteria (KLIC) as a statistical tool to evaluate and compare the predictive abilities of possibly misspecified density forecast models. The main advantage of this statistical tool is that we use the censored likelihood functions to compute the tail minimum of the KLIC, to compare the performance of a density forecast models in the tails. We include an illustrative simulation and an empirical application to compare a set of distributions, including symmetric/asymmetric distribution, and a family of GARCH volatility models. We highlight the use of our approach to a daily index, the Kuala Lumpur Composite index (KLCI). Our results shows that the choice of the conditional distribution appear to be a more dominant factor in determining the adequacy of density forecasts than the choice of volatility model. Furthermore, the results support the Skewed for KLCI return distribution.
    Keywords: Density forecast; Conditional distribution; Forecast accuracy; KLIC; GARCH models
    JEL: D53 C32 C16 C52
    Date: 2007–08–20
  8. By: R.S. Eckaus
    Abstract: The oil price really is a speculative bubble. Yet only recently has the U.S. Congress, for example, showed recognition that this might even be a possibility. In general there seems to be a preference for the claim that the price increases are the result of basic economic forces: rapid growth in consumption, pushed particularly by the oil appetites of China and India, the depreciation of the U.S. dollar, real supply limitations, current and prospective and the risks of supply disruption, especially in the Middle East. These “explanations” will be taken up one by one, but first a view of what has happened to oil prices over recent years.
    Date: 2008–06

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