Testing and comparing conditional CAPM with a new approach in the cross-sectional framework

This study examines the conditional relationship between beta and return for stocks traded on S&P 500 for the period from July 2001 to June 2011. The portfolios formed based on the Book value per share and betas using monthly data. A novel approach for capturing time variation in betas whose pattern is treated as a function of market returns is developed and presented. The estimated coefficients of a nonlinear regression constitute the basis of creating a two factor model. Our results indicate that the proposed specification outperforms alternative models in explaining the cross-section of returns

Dynamic Time Warping as a Similarity Measure: Applications in Finance

This paper presents the basic DTW-algorithm and the manner it can be used as a similarity measure for two different series that might differ in length. Through a simulation process it is being showed the relation of DTW-based similarity measure, dubbed ?_DTW, with two other celebrated measures, that of the Pearson’s and Spearman’s correlation coefficients. In particular, it is shown that ?_DTW takes lower (greater) values when other two measures are great (low) in absolute terms. In addition a dataset composed by 8 financial indices was used, and two applications of the aforementioned measure are presented. First, through a rolling basis, the evolution of ?_DTW has been examined along with the Pearson’s correlation and the volatility. Results showed that in periods of high (low) volatility similarities within the examined series increase (decrease). Second, a comparison of the mean similarities across different classes of months is being carried. Results vary, however a statistical significant greater similarity within Aprils is being reported compared to other months, especially for the CAC 40, IBEX 35 and FTSE MIB indices

Modeling and Forecasting CAT and HDD Indices For Weather Derivative Pricing

In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein–Uhlenbeck temperature process, with seasonality in the level and volatility and time-varying speed of mean reversion. We forecast up to 2 months ahead out of sample daily temperatures, and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in the Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods, proposed in prior studies, in most cases. We find that wavelet networks can model the temperature process very well and consequently they constitute an accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Cooling and Heating Degree Day indices

Predicting Rainfall in the Context of Rainfall Derivatives Using Genetic Programming

Rainfall is one of the most challenging variables to predict, as it exhibits very unique characteristics that do not exist in other time series data. Moreover, rainfall is a major component and is essential for applications that surround water resource planning. In particular, this paper is interested in the prediction of rainfall for rainfall derivatives. Currently in the rainfall derivatives literature, the process of predicting rainfall is dominated by statistical models, namely using a Markov-chain extended with rainfall prediction (MCRP). In this paper we outline a new methodology to be carried out by predicting rainfall with Genetic Programming (GP). This is the first time in the literature that GP is used within the context of rainfall derivatives. We have created a new tailored GP to this problem domain and we compare the performance of the GP and MCRP on 21 different data sets of cities across Europe and report the results. The goal is to see whether GP can outperform MCRP, which acts as a benchmark. Results indicate that in general GP significantly outperforms MCRP, which is the dominant approach in the literature

Cross-sectional conditional risk return analysis in the sorted beta framework: A novel Two Factor Model

This study examines the conditional relationship between beta and return for stocks traded on S&P 500 for the period from July 2001 to June 2011. The portfolios formed based on the Book value per share and betas using monthly data. A novel approach for capturing time variation in betas whose pattern is treated as a function of market returns is developed and presented. The estimated coefficients of a nonlinear regression constitute the basis of creating a two factor model. Our results indicate that the proposed specification surpasses alternative models in explaining the cross-section of returns

Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives

Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms

Design, Analysis, and Measurements of an Antenna Structure for 2.4 GHz Wireless Applications

This paper reports measured results of a multielement antenna implementation, we constructed, that performs at 2.4 GHz ISM band. Particular emphasis was given to the scattering parameters and validation characterization of this antenna structure. The constructed multielement antenna that was studied in both azimuth and elevation planes consists of a number of printed dipoles with integrated baluns. Due to its multielement construction, the proposed antenna structure is suitable for applications that require multielements nature such as MIMO, channel sounder, and digital beamforming