417 research outputs found
Are there bubbles in the art market? The detection of bubbles when fair value is unobservable
No full textThe purpose of this paper is to look for bubbles in the Art Market using a structure based on steady state results for TAR models and appropriate definitions of bubbles recently put forward by Knight, Satchell and Srivastava (2011). The usual method for investigating bubbles is to measure prices as deviations from fair value. We assess whether it is meaningful to define a fair value of art and conclude that it is very challenging empirically to implement any definition. We then treat fair value as zero in one instance and unobservable in the other case and in both cases provide evidence of bubbles in the art market
Finite-sample properties of a two-stage single equation estimator in the SUR model
No full textExact expressions are derived for the density function, variance, and kurtosis of a linear combination of the elements of a two-stage estimator for the coefficients in a single equation of a SUR system. The estimator is the first iterate in the iterative generalized least squares procedure described by Telser [14]. Our results generalize all previously known results for this estimator and, in certain special cases, also generalize some earlier exact results for Zellner's unrestricted covariance matrix estimator, to which it reduces in these special cases
The disappearance of style in the US equity market
No full textThis paper investigates the modelling of style returns in the US and the returns to
style "tilts" based on forecasts of enhanced future style returns. We use hidden
Markov model to build our forecasts. Our finding that style returns are less
forecastible in more recent years is consistent with the hypothesis that style returns
are the result of anomalies rather than risk premia. The erosion of anomalous
returns as public awareness of their presence is translated into strategies that
arbitrage away the excess returns seems to be a hypothesis consistent with our
modelling results
Tracking error: ex-ante versus ex-post measures
No full textIn this paper we show that ex-ante and ex-post tracking errors must necessarily differ, since portfolio weights are ex-post stochastic in nature. In particular, ex-post tracking error is always larger than ex-ante tracking error. Our results imply that fund managers always have a higher ex-post tracking error than their planned tracking error, and thus unless our results are considered, any performance fee based on ex-post tracking error is unfavourable to fund managers
Arisemus Satchell 1955
No full textArisemus Satchell, 1955 rubeni Bravo & Araújo, 2013: 85, Fig. 1 A–I Holotype: ♂, BRAZIL, Ceará: Chapada do Araripe, (“Parque Estadual Sitio Fundão, mata ciliar, 07º13’46”S / 39º26’31”W), 492 m, 03–10.ii.2011, Silva-Neto, A., Menezes, E., Araújo, M.X. leg.Published as part of Ferreira, André Da Silva, Araújo, Maíra Xavier, Vilarinho, Naiara Thaís, Silva-Neto, Alberto Moreira Da & Bravo, Freddy, 2020, Catalogue of type specimens of Insecta (Arthropoda: Hexapoda) deposited in the entomological collection of the Museum of Zoology of Universidade Estadual de Feira de Santana, Brazil, pp. 501-546 in Zootaxa 4728 (4) on page 517, DOI: 10.11646/zootaxa.4728.4.10, http://zenodo.org/record/362654
Optimal investment and asymmetric risk for a large portfolio: a large deviations approach
No full textIn this study, we propose a new method based on the large deviations theory to select an optimal investment for a large portfolio such that the risk, which is defined as the probability that the portfolio return underperforms an investable benchmark, is minimal. As a particular case, we examine the effect of two types of asymmetric dependence; 1) asymmetry in a portfolio return distribution, and 2) asymmetric dependence between asset returns, on the optimal portfolio invested in two risky assets. Furthermore, since our analysis is based on a parametric framework, this allows us to formulate a close-form relationship between the measures of correlation and the optimal portfolio. Finally, we calibrate our method with equity data, namely S&P 500 and Bangkok SET. The empirical evidences confirm that there is a significant impact of asymmetric dependence on optimal portfolio and risk
Steady-state distributions for models of bubbles: their existence and econometric implications
No full textThe purpose of this paper is to examine the properties of bubbles in the light of steady state results for threshold auto-regressive (TAR) models recently derived by Knight and Satchell (2011). We assert that this will have implications for econometrics. We study the conditions under which we can obtain a steady state distribution of asset prices using our simple model of bubbles based on our particular definition of a bubble. We derive general results and further extend the analysis by considering the steady state distribution in three cases of a (I) a normally distributed error process, (II) a non normally (exponentially) distributed steady-state process and (III) a switching random walk with a fairly general i.i.d error process We then examine the issues related to unit root testing for the presence of bubbles using standard econometric procedures. We illustrate as an example, the market for art, which shows distinctly bubble-like characteristics. Our results shed light on the ubiquitous finding of no bubbles in the econometric literature
Forecasting Nonlinear Functions of Returns Using LINEX Loss Functions
No full textThis paper applies LINEX loss functions to forecasting nonlinear functions of variance. We derive the optimal one-step-ahead LINEX forecast for various volatility models using data transformations such as ln(y2t) where yt is the return of the asset. Our results suggest that the LINEX loss function is particularly well-suited to many of these forecasting problems and can give better forecasts than conventional loss functions such as mean square error (MSE).LINEX Loss Function, Forecasting, Volatility
Return distributions in finance /
No full textQuantitative methods have revolutionised the area of trading, regulation, risk management, portfolio construction, asset pricing and treasury activities, and governmental activity such as central banking. One of the original contributions in this area is the classic by Cootner entitled 'The Random Nature of Stock Market Prices'. This work investigated the statistical properties of asset prices and was one of the first works to investigate this area in a rigorous manner. Much has happened in this field in the last 35 years and 'Return Distributions in Finance' contains much new information that reflects this huge growth. The authors combined experience reflects not only the new theory but also the new practice in this fascinating area. The rise of financial engineering now allows us to change the nature of asset returns to whatever pattern we desire, albeit at a cost. Benefits and costs can only be understood if we understand the underlying processes. 'Return Distributions in Finance' allows us to gain that understanding. Assists in understanding asset return distributions Provides a full overview of financial risk management techniques in asset allocation Demonstrates how to use asset return forecast applications.Quantitative methods have revolutionised the area of trading, regulation, risk management, portfolio construction, asset pricing and treasury activities, and governmental activity such as central banking. One of the original contributions in this area is the classic by Cootner entitled 'The Random Nature of Stock Market Prices'. This work investigated the statistical properties of asset prices and was one of the first works to investigate this area in a rigorous manner. Much has happened in this field in the last 35 years and 'Return Distributions in Finance' contains much new information that reflects this huge growth. The authors combined experience reflects not only the new theory but also the new practice in this fascinating area. The rise of financial engineering now allows us to change the nature of asset returns to whatever pattern we desire, albeit at a cost. Benefits and costs can only be understood if we understand the underlying processes. 'Return Distributions in Finance' allows us to gain that understanding. Assists in understanding asset return distributions Provides a full overview of financial risk management techniques in asset allocation Demonstrates how to use asset return forecast applications.Includes bibliographical references and index.Modelling asset returns with hyperbolic distributions / N.H. Bingham and Rüdiger Kiesel -- A review of asymmetric conditional density functions in autoregressive conditional heteroscedasticity models / Shaun A. Bond -- The distribution of commercial real estate returns / Colin Lizieri and Charles Ward -- Modelling emerging market risk premia using higher moments / Soosung Hwang and Stephen E. Satchell -- Are stock prices driven by the volume of trade? Empirical analysis of the FT30, FT100 and certain British shares over 1988-1990 / L.C.G. Rogers, Stephen E. Satchell and Youngjun Yoon -- Testing for a finite variance in stock return distributions / Jun Yu -- Implementing option pricing models when asset returns are predictable and discontinuous / George J. Jiang -- The probability functions of option prices, risk-neutral pricing and Value-at-Risk / John L. Knight, Stephen E. Satchell and Guoqiang Wang -- Pricing derivatives written on assets with arbitrary skewness and kurtosis / John L. Knight and Stephen E. Satchell -- The distribution of realized returns from moving average trading rules with application to Canadian stock market data / Alexander Fritsche.Print version record.Elsevie
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