SERIES EXPANSION OF THE SABR JOINT DENSITY

Under the SABR stochastic volatility model, pricing and hedging contracts that are sensitive to forward smile risk (e.g., forward starting options, barrier options) require the joint transition density. In this paper, we address this problem by providing closed‐form representations, asymptotically, of the joint transition density. Specifically, we construct an expansion of the joint density through a hierarchy of parabolic equations after applying total volatility‐of‐volatility scaling and a near‐Gaussian coordinate transformation. We then establish an existence result to characterize the truncation error and provide explicit joint density formulas for the first three orders. Our approach inherits the same spirit of a small total volatility‐of‐volatility assumption as in the original SABR analysis. Our results for the joint transition density serve as a basis for managing forward smile risk. Through numerical experiments, we illustrate the accuracy of our expansion in terms of joint density, marginal density, probability mass, and implied volatilities for European call options.     

Safety at play: let's all help to prevent drowning

The association between place of residence, population density, relief and type of event (collision or non-collision of the vehicle) has not been evaluated in developing countries. The main objective of this study is to determine the differential factors associated with the occurrence of deaths of collision and non-collision automobile users in Patagonia, Argentina. A multiple logistic regression analysis was performed using as the dependent variable death by car accident (collision or non-collision of the vehicle) and sex, age, place of residence of the victim, relief and population density as the independent variables. Collision fatalities were related to areas of high population density, while non-collision fatalities were related to areas of low population density, mountainous landscape and place of residence of the victims outside the Patagonian region. The results obtained in this study indicate the need to develop differential primary prevention policies by place of residence of car occupants, focusing on Patagonia non-resident drivers and by emphasising non-collision accidents.     

Bayesian modeling of joint and conditional distributions

In this paper, we study a Bayesian approach to flexible modeling of conditional distributions. The approach uses a flexible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of interest from the estimated joint distribution. We use a finite mixture of multivariate normals (FMMN) to estimate the joint distribution. The conditional distributions can then be assessed analytically or through simulations. The discrete variables are handled through the use of latent variables. The estimation procedure employs an MCMC algorithm. We provide a characterization of the Kullback–Leibler closure of FMMN and show that the joint and conditional predictive densities implied by the FMMN model are consistent estimators for a large class of data generating processes with continuous and discrete observables. The method can be used as a robust regression model with discrete and continuous dependent and independent variables and as a Bayesian alternative to semi- and non-parametric models such as quantile and kernel regression. In experiments, the method compares favorably with classical nonparametric and alternative Bayesian methods.     

Time Changes for Lévy Processes

The goal of this paper is to consider pure jump Lévy processes of finite variation with an infinite arrival rate of jumps as models for the logarithm of asset prices. These processes may be written as time-changed Brownian motion. We exhibit the explicit time change for each of a wide class of Lévy processes and show that the time change is a weighted price move measure of time. Additionally, we present a number of Lévy processes that are analytically tractable, in their characteristic functions and Lévy densities, and hence are relevant for option pricing.     

贸易结构、技术密度与经济增长——一个分析框架及基于中国数据的检验

以往的国际贸易理论都强调，贸易结构在促进经济增长方面起着重要作用。本文通过VAR模型估计了中国的贸易结构与经济增长的关系，结果显示，中国的贸易结构并不显著影响经济增长。     

Analysis of marginally specified semi-nonparametric models for clustered binary data

Generalized linear mixed models are widely used for analyzing clustered data. If the primary interest is in regression parameters, one can proceed alternatively, through the marginal mean model approach. In the present study, a joint model consisting of a marginal mean model and a cluster-specific conditional mean model is considered. This model is useful when both time-independent and time-dependent covariates are available. Furthermore our model is semi-parametric, as we assume a flexible, smooth semi-nonparametric density of the cluster-specific effects. This semi-nonparametric density-based approach outperforms the approach based on normality assumption with respect to some important features of 'between-cluster variation'. We employ a full likelihood-based approach and apply the Monte Carlo EM algorithm to analyze the model. A simulation study is carried out to demonstrate the consistency of the approach. Finally, we apply this to a study of long-term illness data.     

Asset allocation and multivariate position based trading

I add a second risky asset and a risk free bond to the univariate artificial market investigated by Lux and Marchesi (Int J Theor Appl Finance 3(4):675–702, 2000), keeping track of traders aggregate positions and wealth. Asset allocation and security selection are modeled as separate decision processes, as is common practice in financial institutions. Introducing position based trading avoids inconsistencies in traders inventories resulting from the order based setup of the original model, while preserving its ability to reproduce the stylized facts of financial return series.      

Bayesian inference in cointegrated systems

In this paper I deal with Bayesian methods for conducting inference on important features of (potentially) cointegrated VAR models involving I(1) variables. Firstly, (informal) inference is made on the cointegrating rank of the system. Secondly, posterior analysis is used to verify the validity of over-identifying restrictions on the cointegration parameters. Thirdly, posterior distributions are obtained for impulse response functions and predictive densities at different horizons. The relevant posterior distributions are obtained by means of Monte Carlo integration. The analysis is based on the use of simple weakly informative priors. Two applications on simulated data and on the Danish money demand data are presented.     

Range-based multivariate volatility model with double smooth transition in conditional correlation

This paper proposes a multivariate model named Double Smooth Transition Conditional Correlation Conditional Autoregressive Range (DSTCC-CARR for short). Determined by two transition variables, the correlations smoothly transit from one state to another. Together with the DSTCC-GARCH model, the model is employed to investigate the interdependence between Hong Kong's and international stock markets. It is proved by the empirical analysis that the DSTCC-CARR model is more credible and efficient than the DSTCC-GARCH model. Linkages among Hong Kong's and other world's markets captured by these two models are testified to be consistent with history, and have meaningful interpretations.