65 research outputs found
Labys
No full textPerdrizet Paul. Labys. In: Revue des Études Grecques, tome 11, fascicule 43,1898. pp. 245-249
Encore Labys
No full textPerdrizet Paul. Encore Labys. In: Revue des Études Grecques, tome 12, fascicule 45,1899. pp. 40-42
Modeling and Forecasting Realized Volatility
No full textThis paper provides a general framework for integration of high-frequency intraday data into the measurement forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariancematrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark Dollar and Yen / Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatilities perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quantile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.
Modeling and Forecasting Realized Volatility
No full textThis paper provides a general framework for integration of high-frequency intraday data into the measurement, modeling and forecasting of daily and lower frequency volatility and return distributions. Most procedures for modeling and forecasting financial asset return volatilities, correlations, and distributions rely on restrictive and complicated parametric multivariate ARCH or stochastic volatility models, which often perform poorly at intraday frequencies. Use of realized volatility constructed from high-frequency intraday returns, in contrast, permits the use of traditional time series procedures for modeling and forecasting. Building on the theory of continuous-time arbitrage-free price processes and the theory of quadratic variation, we formally develop the links between the conditional covariance matrix and the concept of realized volatility. Next, using continuously recorded observations for the Deutschemark/Dollar and Yen /Dollar spot exchange rates covering more than a decade, we find that forecasts from a simple long-memory Gaussian vector autoregression for the logarithmic daily realized volatitilies perform admirably compared to popular daily ARCH and related models. Moreover, the vector autoregressive volatility forecast, coupled with a parametric lognormal-normal mixture distribution implied by the theoretically and empirically grounded assumption of normally distributed standardized returns, gives rise to well-calibrated density forecasts of future returns, and correspondingly accurate quintile estimates. Our results hold promise for practical modeling and forecasting of the large covariance matrices relevant in asset pricing, asset allocation and financial risk management applications.
The treatment of Gulf War syndrome with cognitive behavioral therapy: a case comparison study
No full textSince the late 1990s, researchers have been focused on finding effective treatments for military veterans with Gulf War Syndrome (GWS), a multisymptom (cognitive and physical) illness whose roots have still remained largely unexplained. With the possibility that such war-related syndromes may affect as many as 45-60% of returning soldiers, researchers have recommended that future research on GWS prioritize qualitative work, which has been scarce, to deepen the understanding of this illness in the veteran population -- including their attributions, fears, and concerns -- so that more refined, suitable treatments may be developed to meet their needs. The following paper examines a prior treatment study which evaluated the efficacy of manualized cognitive-behavioral therapy (CBT) to improve physical health and reduce psychological stress in military veterans with GWS. The current analysis is comprised of a cross-case comparison of two soldiers and considers the various factors that may have contributed to the success or failure of this particular CBT treatment for this population. In the original treatment trial, patients were given weekly individual outpatient therapy sessions over a three-month period and were monitored periodically for physical, cognitive, and emotional changes. Two cases were selected for analysis from the original study based on their opposing outcomes: Soldier 2 was successful in achieving a substantial increase in physical functioning, while Soldier 1 was not. Although the CBT treatment yielded positive changes in both patients’ level of self-awareness, and significant improvements in GWS-related psychological and physical stress in Soldier 2's case, the results indicate that additional factors, such as individual personality traits, states of cognitive functioning, and comorbidity need to be more closely examined and considered when designing treatments for veterans with Gulf War Syndrome.Psy.DIncludes bibliographical referencesby Charlotte Alexandra Laby
Exchange Rate Returns Standardized by Realized Volatility Are (Nearly) Gaussian
No full textThe prescriptions of modern financial risk management hinge critically on the associated characterization of the distribution of future returns (cf., Diebold, Gunther and Tay, 1998, and Diebold, Hahn and Tay, 1999). Because volatility persistence renders high-frequency returns temporally dependent (e.g., Bollerslev, Chou and Kroner, 1992), it is the conditional return distribution, and not the unconditional distribution, that is of relevance for risk management. This is especially true in high-frequency situations, such as monitoring and managing the risk associated with the day-to-day operations of a trading desk, where volatility clustering is omnipresent. Exchange rate returns are well-known to be unconditionally symmetric but highly leptokurtic. Standardized daily or weekly returns from ARCH and related stochastic volatility models also appear symmetric but leptokurtic; that is, the distributions are not only unconditionally, but also conditionally leptokurtic, although less so than unconditionally.1 A sizable literature explicitly attempts to model the fat-tailed conditional distributions, including, for example, Bollerslev (1987), Engle and Gonzalez-Rivera (1991), and Hansen (1994).
The Distribution of Exchange Rate Volatility
No full textUsing high-frequency data on Deutschemark and Yen returns against the dollar, we construct model-free estimates of daily exchange rate volatility and correlation, covering an entire decade. In addition to being model-free, our estimates are also approximately free of measurement error under general conditions, which we delineate. Hence, for all practical purposes, we can treat the exchange rate volatilities and correlations as observed rather than latent. We do so, and we characterize their joint distribution, both unconditionally and conditionally. Noteworthy results include a simple normality-inducing volatility transformation, high contemporaneous correlation across volatilities, high correlation between correlation and volatilities, pronounced and highly persistent temporal variation in both volatilities and correlation, clear evidence of long-memory dynamics in both volatilities and correlation, and remarkably precise scaling laws under temporal aggregation.
Essays on microstructure and the use of information in limit order markets
No full textCompetitive international financial exchanges can distinguish themselves by offering different types of trading features, such as the ability to partially hide an order\u27s quantity. Although order hiding is generally assumed to be an advantageous mechanism, it has undergone very little empirical analysis, due in part to a lack of appropriate data. The Paris Bourse does provide limit order trading records on a second-by-second basis, but its data set omits such critical elements as the time of order cancellation and the electronic order book\u27s composition. In this dissertation, I develop a method to reconstruct the internal states of the Bourse, then explore the role and impact of hidden orders from both empirical and theoretical perspectives. In Chapter One, I discuss the reconstruction itself: the Bourse\u27s electronic trading system is reverse-engineered and a working model is constructed. By running historical streams of orders through this model, it is possible to simulate the internal state of the system over time, replicating the markets hidden microstructural dynamics. In Chapter Two, I conduct an empirical analysis of a year\u27s worth of reconstructed second-by-second data for representative French stocks, testing a series of hypotheses relevant to order hiding. I find that order hiding decreases the immediate impact that a limit order has on the market while reducing the amount of undercutting; however, order hiding also increases the probability that the order will not execute in full. Because it slows down the interaction of orders with the market, order hiding can be a useful tool for limit order traders wishing to mitigate adverse selection risk. In Chapter Three, I construct a theoretic model of a limit order market in which traders use order hiding as a strategic mechanism in the struggle for liquidity. Their interaction can be represented as a dynamic game of incomplete information, or signaling game, in which traders try to deduce the true depths contained in the book, information which has a critical effect on their probabilities of execution. I show that the equilibrium of this game is consistent with observed facts
Is the discount on the secondary market a case for LDC debt relief?
No full textIn 1988, the prices on the secondary market of LDC debt averaged 50 cents per dollar of face value. From the observation of such discount, this paper goes one step further and argues thatthe debt should be written down in order to account for the discrepancy between the face and market value of the debt. The paper is structured as follows. Section 1 spells out the model, section 2 calculates the socially efficient and the post-default growth rates of the economy. Section 3 shows that the lenders, if they were to monitor the investment and the consumption strategy of the borrower, would choose a lower investment strategy than the socially efficient one. Section 4 shows how an optimum rescheduling can achieve the equilibrium described in section 3. Section 5 shows the dynamic inconsistency of the optimal strategy spelled out in section 4, and shows the link with the"debt overhang"literature. Section 6 investigates the empirical relevance of the"debt overhang".Economic Theory&Research,Banks&Banking Reform,Environmental Economics&Policies,Strategic Debt Management,Financial Intermediation
Biodiesel and vegetable oil market in European Union: some evidences from threshold cointegration analysis
No full textIn this paper we analyse the long-run relationships between vegetable oils prices and conventional diesel price in EU during the period 2005- 2007. We utilise recent developments on threshold cointegration approach to investigate if asymmetric dynamic adjusting processes exist among rapeseed oil, sunflower oil, soybean oil and diesel prices. The results suggest that the two-regime threshold cointegration model exist only in favour of rapeseed oil-diesel price pair. Therefore, this vegetable oil price adjusts rapidly to its long run equilibrium, determined by fossil diesel prices, in an asymmetric manner when the divergence between the two prices is above a critical threshold. Consequently, rapeseed oil seems to be particularly exposed to exogenous shocks deriving from global political scenarios, suggesting to redefine the high quota (80%) of EU biodiesel produced by this vegetable oil through a sustainable development of international trade.Vegetable oils market, Biodiesel price, Threshold cointegration., Marketing, Resource /Energy Economics and Policy,
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