Influence of starvation on selected temperatures in the young of bream, roach and perch [Translation from: Informatsionnyi Byulleten Biologiya Vnutrennikh Vod No.50, 45-47, 1981]

Works devoted to the influence of starvation on temperature selection by fishes are few and their conclusions are contradictory. This study determined the influence of brief, up to 14 days, starvation on temperature selection by young fishes. The experiments were carried out in August-September 1976 on fingerling bream (Abramis brama L.), roach (Rutilus rutilus L.) and perch (Perca fluviatilis L.) with body lengths of 3-5 cm and weight 0.5-1.2 g. The young fish were caught in the littoral by seine-nets or small drag-nets. Immediately after catching the fish they were put in acclimatization boxes. The period of acclimatization did not exceed 2 days for bream and roach at a temperature of 20 °C and 6 days for perch at 17 °C. Before the start of the experiment and for the first 10 days of the experiment the fish were fed with oligochaetes, earthworms and daphnia, after that feeding discontinued. At the end of a 10-14 day period the giving of food was resumed. The study concludes that the experiments have shown that in the summer season the factor of starvation significantly changes the reaction to the gradient of temperature in young cyprihids - roach and bream

Invariant Differential Operators for Non-Compact Lie Groups: the Sp(n,R) Case

In the present paper we continue the project of systematic construction of invariant differential operators on the example of the non-compact algebras sp(n,R), in detail for n=6. Our choice of these algebras is motivated by the fact that they belong to a narrow class of algebras, which we call 'conformal Lie algebras', which have very similar properties to the conformal algebras of Minkowski space-time. We give the main multiplets and the main reduced multiplets of indecomposable elementary representations for n=6, including the necessary data for all relevant invariant differential operators. In fact, this gives by reduction also the cases for n<6, since the main multiplet for fixed n coincides with one reduced case for n+1.Comment: Latex2e, 27 pages, 8 figures. arXiv admin note: substantial text overlap with arXiv:0812.2690, arXiv:0812.265

An Economic Evaluation of Investment on Aonla (Emblica officinalis G.) in Gujarat

The economic viability of aonla plantation in Gujarat has been studied through a sample of 120 aonla growers spread over 12 selected villages of the Kheda and Anand districts for the agricultural year 2003-04. It has been found that establishment of aonla orchard involves high investment, but the annual net returns are also quite high, after the third year of plantation. The values of economic parameters, viz. NPV, BCR, IRR and PBP have been found to be Rs 652652, 5.25, 65.03 per cent and 55 months, respectively at 10 per cent discount rate. Under varying cost and return situations, values of all these feasibility parameters have satisfied the acceptance rules for the investment proposition. It has confirmed the economic viability, stability and certainty of investment on aonla orchard. The study has suggested that financial institutions should give credit to aonla producers in the area.Crop Production/Industries,

Multivariate Option Pricing with Time Varying Volatility and Correlations

In recent years multivariate models for asset returns have received much attention, in particular this is the case for models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes to option pricing. Specifically, we derive the risk neutral dynamics for a general class of multivariate heteroskedastic models, and we provide a feasible way to price options in this framework. Our framework can be used irrespective of the assumed underlying distribution and dynamics, and it nests several important special cases. We provide an application to options on the minimum of two indices. Our results show that not only is correlation important for these options but so is allowing this correlation to be dynamic. Moreover, we show that for the general model exposure to correlation risk carries an important premium, and when this is neglected option prices are estimated with errors. Finally, we show that when neglecting the non-Gaussian features of the data, option prices are also estimated with large errors.Multivariate risk premia, option pricing, GARCH models

Bayesian inference for the mixed conditional heteroskedasticity model

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik, and Paolella 2004a). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion. We apply the model to the SP500 daily returns.Finite mixture, ML estimation, bayesian inference, value at risk.

Special Reduced Multiplets and Minimal Representations for SO(p,q)

Using our previous results on the systematic construction of invariant differential operators for non-compact semisimple Lie groups we classify the special reduced multiplets and minimal representations in the case of SO(p,q).Comment: 26 pages, 11 figures, to appear in the Proceedings of the X International Workshop "Lie Theory and Its Applications in Physics}, (Varna, Bulgaria, June 2013), "Springer Proceedings in Mathematics and Statistics", Vol. 11

Density and Hazard Rate Estimation for Censored and ?-mixing Data Using Gamma Kernels

In this paper we consider the nonparametric estimation for a density and hazard rate function for right censored ?-mixing survival time data using kernel smoothing techniques. Since survival times are positive with potentially a high concentration at zero, one has to take into account the bias problems when the functions are estimated in the boundary region. In this paper, gamma kernel estimators of the density and the hazard rate function are proposed. The estimators use adaptive weights depending on the point in which we estimate the function, and they are robust to the boundary bias problem. For both estimators, the mean squared error properties, including the rate of convergence, the almost sure consistency and the asymptotic normality are investigated. The results of a simulation demonstrate the excellent performance of the proposed estimators.Gamma kernel, Kaplan Meier, density and hazard function, mean integrated squared error, consistency, asymptotic normality.

MIXED EXPONENTIAL POWER ASYMMETRIC CONDITIONAL HETEROSKEDASTICITY

To match the stylized facts of high frequency financial time series precisely and parsimoniously, this paper presents a finite mixture of conditional exponential power distributions where each component exhibits asymmetric conditional heteroskedasticity. We provide stationarity conditions and unconditional moments to the fourth order. We apply this new class to Dow Jones index returns. We find that a two-component mixed exponential power distribution dominates mixed normal distributions with more components, and more parameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, all the conditional variance processes become stationary. This happens because the mixed exponential power distribution allows for component-specific shape parameters so that it can better capture the tail behaviour. Therefore, the more general new class has attractive features over mixed normal distributions in our application: Less components are necessary and the conditional variances in the components are stationary processes. Results on NASDAQ index returns are similar.Finite mixtures, exponential power distributions, conditional heteroskedasticity, asymmetry, heavy tails, value at risk.

Nonparametric Density Estimation for Multivariate Bounded Data

We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g., nonnegative) or completely bounded (e.g., in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well-known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided.Asymmetric kernels, multivariate boudnary bias, nonparametric multivariate density estimation, asymptotic properties, bandwidth selection, least squares cross-validation

Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order moments of the risk neutral distribution. Parameter inference using Gibbs sampling is explained and we detail how to compute risk neutral predictive densities taking into account parameter uncertainty. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared to a benchmark model in terms of dollar losses and the ability to explain the smirk in implied volatilities.Bayesian inference, option pricing, finite mixture models, out-of-sample prediction, GARCH models