New Economics Papers
on Financial Markets
Issue of 2007‒12‒01
fourteen papers chosen by

  1. Efficient Estimation of a Semiparametric Characteristic- Based Factor Model of Security Returns By Gregory Connor; Matthias Hagmann; Oliver Linton
  2. Hybrid Cat-bonds By Pauline Barrieu; Henri Loubergé
  3. Robust Value at Risk Prediction By Loriano Mancini; Fabio Trojani
  4. Arbitrage in Stationary Markets By Igor Evstigneev; Dhruv Kapoor
  5. Dynamic Option-Based Strategies under Downside Loss Averse Preferences By Amine Jalal
  6. Advance Information and Asset Prices By Albuquerque, Rui; Miao, Jianjun
  7. Measuring potential market risk By Bask, Mikael
  8. Pricing bivariate option under GARCH-GH model with dynamic copula : application for Chinese market. By Dominique Guégan; Jing Zhang
  9. Informed Trading, Liquidity Provision, and Stock Selection by Mutual Funds By Zhi Da; Pengjie Gao; Ravi Jagannathan
  10. Basel II and financial stability: An investigation of sensitivity and cyclicality of capital requirements based on QIS 5 By Balázs Zsámboki
  11. Copula based simulation procedures for pricing basket Credit Derivatives By Fathi , Abid; Nader, Naifar
  12. Forecasting Bonds Yields in the Brazilian Fixed Income Market By Jose Vicente; Benjamin M. Tabak
  13. NoVaS Transformations: Flexible Inference for Volatility Forecasting By Dimitris Politis; Dimitrios Thomakos
  14. 'Optimal' Probabilistic Predictions for Financial Returns By Dimitrios Thomakos; Tao Wang

  1. By: Gregory Connor (The London School of Economics); Matthias Hagmann (Concordia Advisors and Swiss Finance Institute); Oliver Linton (The London School of Economics)
    Abstract: This paper develops a new estimation procedure for characteristic-based factor models of security returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as time-varying weights, and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a time-series and cross-sectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristic-beta functions. By avoiding the curse of dimensionality our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the three-factor Fama-French model, Carhart’s four-factor extension of it adding a momentum factor, and a five-factor extension adding an own-volatility factor. We find that momentum and own-volatility factors are at least as important if not more important than size and value in explaining equity return comovements. We test the multifactor beta pricing theory against the Capital Asset Pricing model using a standard test, and against a general alternative using a new nonparametric test.
    Keywords: Additive Models; Arbitrage pricing theory; Factor model; Fama-French; Kernel estimation; Nonparametric regression; Panel data.
    JEL: G12 C14
    Date: 2007–09
  2. By: Pauline Barrieu (London School of Economics); Henri Loubergé (University of Geneva and Swiss Finance Institute)
    Abstract: Natural catastrophes attract regularly the attention of media and have become a source of public concern. From a financial viewpoint, natural catastrophes represent idiosyncratic risks, diversifiable at the world level. But for reasons analyzed in this paper reinsurance markets are unable to cope with this risk completely. Insurance-linked securities, such as cat bonds, have been issued to complete the international risk transfer process, but their development is disappointing so far. This paper argues that downside risk aversion and ambiguity aversion explain the limited success of cat bonds. Hybrid cat bonds, combining the transfer of cat risk with protection against a stock market crash, are proposed to complete the market. Using the concept of market modified risk measure, the paper shows that replacing simple cat bonds with hybrid cat bonds would lead to an increase in market volume.
    Keywords: Risk management, Risk transfer, Catastrophes, Risk measures, Reinsurance, Optimal design
    JEL: D81 G22
    Date: 2006–02
  3. By: Loriano Mancini (University of Zurich); Fabio Trojani (University of St-Gallen)
    Abstract: We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which controls the bootstrap instability due to influential observations in the tails of standardized GARCH residuals. Monte Carlo simulation shows that our method consistently provides lower VaR forecast errors, often to a large extent, and in contrast to classical methods never fails validation tests at usual significance levels. We test extensively our approach in the context of real data applications to VaR prediction for market risk, and find that only our robust procedure passes all validation tests at usual confidence levels. Moreover, the smaller tail estimation risk of robust VaR forecasts implies VaR prediction intervals that can be nearly 20% narrower and 50% less volatile over time. This is a further desirable property of our method, which allows to adapt risky positions to VaR limits more smoothly and thus more efficiently.
    Keywords: Backtesting, M-estimator, Extreme Value Theory, Breakdown Point.
    JEL: C14 C15 C23 C59
    Date: 2005–10
  4. By: Igor Evstigneev (University of Manchester); Dhruv Kapoor (University of Manchester)
    Abstract: We analyse questions of arbitrage in financial markets in which asset prices change in time as stationary stochastic processes. The main focus of the paper is on a model where the price vectors are independent and identically distributed. In the framework of this model, we find conditions that are necessary and su¢ cient for the absence of arbitrage opportunities. We discuss the relations between the results obtained and the phenomenon of "volatility-induced growth"in stationary markets.
    Keywords: Stationary markets, Arbitrage, Volatility-induced growth.
    JEL: G10 G11 G14
  5. By: Amine Jalal (Goldman Sachs International)
    Abstract: In this paper, dynamic option-based investment strategies are derived and illustrated for investors exhibiting downside loss aversion. The problem is solved in closed form when the stock market exhibits stochastic volatility and jumps. The specification of downside loss averse utility functions allows corresponding terminal wealth profiles to be expressed as options on the stochastic discount factor contingent on the loss aversion level. Therefore dynamic strategies reduce to the replicating portfolio using exchange traded and well selected options, and the risky stock
    Keywords: Asset allocation, Downside risk, Stochastic volatility, jumps.
    JEL: G11 G12
    Date: 2007–09
  6. By: Albuquerque, Rui; Miao, Jianjun
    Abstract: This paper provides an explanation for momentum and reversal in stock returns within a rational expectations framework in which investors are heterogeneous in their information and investment opportunities. We assume that informed agents privately receive advance information about company earnings that materializes into the future. While this information is immediately incorporated into prices, stock prices underreact to it causing short-run momentum. Stock prices may appear to move in ways unrelated to current fundamentals. When the information materializes, the stock price reverts back to its long run mean mimicking an overreaction pattern.
    Keywords: advance information; momentum and reversal effects; overreaction; rational expectations equilibrium; underreaction
    JEL: G11 G12 G14
    Date: 2007–11
  7. By: Bask, Mikael (Bank of Finland Research)
    Abstract: The difference between market risk and potential market risk is emphasized and a measure of the latter risk is proposed. Specifically, it is argued that the spectrum of smooth Lyapunov exponents can be utilized in what we call (l, s2)-analysis, which is a method to monitor the aforementioned risk measures. The reason is that these exponents focus on the stability properties (l) of the stochastic dynamic system generating asset returns, while more traditional risk measures such as value-at-risk are concerned with the distribution of returns (s2).
    Keywords: market risk; potential market risk; smooth Lyapunov exponents; stochastic dynamic system; value-at-risk
    JEL: G11
    Date: 2007–11–13
  8. By: Dominique Guégan (Centre d'Economie de la Sorbonne); Jing Zhang (East China Normal University et Centre d'Economie de la Sorbonne)
    Abstract: This paper develops the method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. In order to provide a general framework being able to accommodate skewness, leptokurtosis, fat tails as well as the time varying volatility that are often found in financial data, generalized hyperbolic (GH) distribution is used for innovations. As the association between the underlying assets may vary over time, the dynamic copula approach is considered. Therefore, the proposed method proves to play an important role in pricing bivariate option. The approach is illustrated for Chinese market with one type of better-of-two-markets claims : call option on the better performer of Shanghai Stock Composite Index and Shenzhen Stock Composite Index. Results show that the option prices obtained by the GARCH-GH model with time-varying copula differ substantially from the prices implied by the GARCH-Gaussian dynamic copula model. Moreover, the empirical work displays the advantage of the suggested method.
    Keywords: Call-on-max option, GARCH process, generalized hyperbolic (GH) distribution, normal inverse Gaussian (NIG) distribution, copula, dynamic copula.
    JEL: C51 G12
    Date: 2007–11
  9. By: Zhi Da; Pengjie Gao; Ravi Jagannathan
    Abstract: We show that the stock selection ability of a fund manager can be decomposed into two components: "informed trading" and "liquidity provision." As information loses value over time, informed trading tends to be liquidity-absorbing. We conjecture that value enhancing informed trading is more likely in stocks during times when they are associated with more information events. In contrast, liquidity provision is more likely to add value for stocks associated with few information events and little adverse selection risk. We identify times when there are more information events associated with a stock by its Probability of Informed Trading (PIN, Easley et. al., 1996) measure and information asymmetry component of the spread (Madhavan et. al., 1997). We provide empirical support for our conjecture using quarterly mutual fund holdings data for the period from 1983 to 2004. We find that the informed trading component is relatively more important for mutual funds with a growth oriented investment style whereas liquidity provision is more important for funds with more of an income orientation. Further, the informed trading component of the selection ability of a mutual fund exhibits greater persistence over time.
    JEL: G1 G11 G12 G14 G23
    Date: 2007–11
  10. By: Balázs Zsámboki (Magyar Nemzeti Bank)
    Abstract: This study aims to analyse the sensitivity of capital requirements to changes in risk parameters (PD, LGD and M) by creating a ‘model bank’ with a portfolio mirroring the average asset composition of internationally active large banks, as well as locally oriented smaller institutions participating in the QIS 5 exercise. Using historical data on corporate default rates, the dynamics of risk weights and capital requirements over a whole business cycle are also examined, with special emphasis on financial stability implications. The purpose of this paper is to contribute to a better understanding of the mechanism of Basel II and to explore the possible impacts of prudential regulation on cyclical swings in capital requirements.
    Keywords: Basel II, credit risk, capital requirement, regulation, cyclicality, financial stability.
    JEL: G21 G28 G32
    Date: 2007
  11. By: Fathi , Abid; Nader, Naifar
    Abstract: This paper deals with the impact of structure of dependency and the choice of procedures for rare-event simulation on the pricing of multi-name credit derivatives such as nth to default swap and Collateralized Debt Obligations (CDO). The correlation between names defaulting has an effect on the value of the basket credit derivatives. We present a copula based simulation procedure for pricing basket default swaps and CDO under different structure of dependency and assessing the influence of different price drivers (correlation, hazard rates and recovery rates) on modelling portfolio losses. Gaussian copulas and Monte Carlo simulation is widely used to measure the default risk in basket credit derivatives. Default risk is often considered as a rare-event and then, many studies have shown that many distributions have fatter tails than those captured by the normal distribution. Subsequently, the choice of copula and the choice of procedures for rare-event simulation govern the pricing of basket credit derivatives. An alternative to the Gaussian copula is Clayton copula and t-student copula under importance sampling procedures for simulation which captures the dependence structure between the underlying variables at extreme values and certain values of the input random variables in a simulation have more impact on the parameter being estimated than others .
    Keywords: Collateralized Debt Obligations; Basket Default Swaps; Monte Carlo method; One factor Gaussian copula; Clayton copula; t-student copula; importance sampling.
    JEL: G19
    Date: 2007–03
  12. By: Jose Vicente; Benjamin M. Tabak
    Abstract: This paper studies the predictive ability of a variety of models in forecasting the yield curve for the Brazilian fixed income market. We compare affine term structure models with a variation of the Nelson-Siegel exponential framework developed by Diebold and Li (2006). Empirical results suggest that forecasts made with the latter methodology are superior and appear accurate at long horizons when compared to different benchmark forecasts. These results are important for policy makers, portfolio and risk managers. Further research could study the predictive ability of such models in other emerging markets.
    Date: 2007–08
  13. By: Dimitris Politis; Dimitrios Thomakos
    Abstract: In this paper we contribute several new results on the NoVaS transformation approach for volatility forecasting introduced by Politis (2003a,b, 2007). In particular: (a) we introduce an alternative target distribution (uniform); (b) we present a new method for volatility forecasting using NoVaS ; (c) we show that the NoVaS methodology is applicable in situations where (global) stationarity fails such as the cases of local stationarity and/or structural breaks; (d) we show how to apply the NoVaS ideas in the case of returns with asymmetric distribution; and finally (e) we discuss the application of NoVaS to the problem of estimating value at risk (VaR). The NoVaS methodology allows for a flexible approach to inference and has immediate applications in the context of short time series and series that exhibit local behavior (e.g. breaks, regime switching etc.) We conduct an extensive simulation study on the predictive ability of the NoVaS approach and and that NoVaS forecasts lead to a much `tighter' distribution of the forecasting performance measure for all data generating processes. This is especially relevant in the context of volatility predictions for risk management. We further illustrate the use of NoVaS for a number of real datasets and compare the forecasting performance of NoVaS -based volatility forecasts with realized and range-based volatility measures.
    Keywords: ARCH, GARCH, local stationarity, structural breaks, VaR, volatility.
    Date: 2007
  14. By: Dimitrios Thomakos; Tao Wang
    Abstract: We examine the `relative optimality' of sign predictions for financial returns, extending the work of Christoffersen and Diebold (2006) on volatility dynamics and sign predictability. We show that there is a more general decomposition of financial returns than that implied by the sign decomposition and which depends on the choice of the threshold that defines direction. We then show that the choice of the threshold matters and that a threshold of zero (leading to sign predictions) is not necessarily `optimal'. We provide explicit conditions that allow for the choice of a threshold that has maximum responsiveness to changes in volatility dynamics and thus leads to `optimal' probabilistic predictions. Finally, we connect the evolution of volatility to probabilistic predictions and show that the volatility ratio is the crucial variable in this context. Our work strengthens the arguments in favor of accurate volatility measurement and prediction, as volatility dynamics are integrated into the `optimal' threshold. We provide an empirical illustration of our findings using monthly returns and realized volatility for the S&P500 index.
    Date: 2007

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