Recursive risk measures under regime switching applied to portfolio selection

In this paper, we define the conditional risk measure under regime switching and derive a class of time consistent multi-period risk measures. To do so, we describe the information process with regime switching in a product space associated with the product of two filtrations. Moreover, we show how to establish the corresponding multi-stage portfolio selection models using the time consistent multi-period risk measure for medium-term or long-term investments. Take the conditional value-at-risk measure as an example, we demonstrate the resulting multi-stage portfolio selection problem can be transformed into a second-order cone programming problem. Finally, we carry out a series of empirical tests to illustrate the superior performance of the proposed random framework and the corresponding multi-stage portfolio selection model.     

Fast swaption pricing under the market model with a square-root volatility process

In this paper we study a correlation-based LIBOR market model with a square-root volatility process. This model captures downward volatility skews through taking negative correlations between forward rates and the multiplier. An approximate pricing formula is developed for swaptions, and the formula is implemented via fast Fourier transform. Numerical results on pricing accuracy are presented, which strongly support the approximations made in deriving the formula.     

Asymptotic ruin probabilities for a discrete-time risk model with dependent insurance and financial risks

This contribution focuses on a discrete-time risk model in which both insurance risk and financial risk are taken into account and they are equipped with a wide type of dependence structure. We derive precise asymptotic formulas for the ruin probabilities when the insurance risk has a dominatedly varying tail. In the special case of regular variation, the corresponding formula is proved to be uniform for the time horizon.     

A Markov-switching generalized additive model for compound Poisson processes,with applications to operational loss models

This paper is concerned with modelling the behaviour of random sums over time. Such models are particularly useful to describe the dynamics of operational losses, and to correctly estimate tail-related risk indicators. However, time-varying dependence structures make it a difficult task. To tackle these issues, we formulate a new Markov-switching generalized additive compound process combining Poisson and generalized Pareto distributions. This flexible model takes into account two important features: on the one hand, we allow all parameters of the compound loss distribution to depend on economic covariates in a flexible way. On the other hand, we allow this dependence to vary over time, via a hidden state process. A simulation study indicates that, even in the case of a short time series, this model is easily and well estimated with a standard maximum likelihood procedure. Relying on this approach, we analyse a novel data-set of 819 losses resulting from frauds at the Italian bank UniCredit. We show that our model improves the estimation of the total loss distribution over time, compared to standard alternatives. In particular, this model provides estimations of the 99.9% quantile that are never exceeded by the historical total losses, a feature particularly desirable for banking regulators.     

On a continuous time stock price model with regime switching,delay, and threshold

Motivated by the need to describe bear-bull market regime switching in stock prices, we introduce and study a stochastic process in continuous time with two regimes, threshold and delay, given by a stochastic differential equation. When the difference between the regimes is simply given by a different set of real valued parameters for the drift and diffusion coefficients, with changes between regimes depending only on these parameters, we show that if the delay is known there are consistent estimators for the threshold as long we know how to classify a given observation of the process as belonging to one of the two regimes. When the drift and diffusion coefficients are of geometric Brownian motion type we obtain a model with parameters that can be estimated in a satisfactory way, a model that allows differentiating regimes in some of the NYSE 21 stocks analyzed and also, that gives very satisfactory results when compared to the usual Black–Scholes model for pricing call options.     

The new political importance of the old hurricane risk: a contextual approach to understanding contemporary struggles with hurricane risk and insurance

In the United States, policy-makers struggle to resolve conflict between public demands for affordable insurance costs covering hurricanes and market demands for risk-based insurance pricing. Given the socially constructed nature of risk, a risk-based pricing approach prioritizes insurer values and business practices over all societal value goals expressly limiting democratic inclusion in decision-making about risk. As a step towards the more democratically inclusive approach of risk governance, this article uses the state of Florida as a case study to provide a narrative of the social and political context for the evolution of the idea of U.S. hurricane risk. I argue that today’s hurricane risk is a product of long-standing shared efforts to build prosperity. However, it is no longer a simple risk for society to overcome on its way to economic well-being. Contemporary hurricane risk is systemic and serves as a nexus for political battles over American values.     

A joint model for temperature and natural gas with an application to the US market

Natural gas spot prices and temperatures have been studied in detail in the literature as separate processes. We propose a simple joint model that, in spite of its parsimony, describes accurately many stylized facts of the two time series: in particular we show the role played by a time-delay parameter in order to take into account the impact of temperature forecast in cross-dependency. We discuss in detail a stepwise procedure in order to calibrate model parameters, describing the elementary estimation techniques involved and the statistical accuracy achieved. In the analysis, we focus on the benchmark market in the USA (Henry Hub) and the temperatures in the Northeast and Midwest regions; we observe a negative, statistically significant, gas-temperature correlation in the cold season.     

Development and validation of an improved DeLone-McLean IS success model - application to the evaluation of a tax administration ERP

Enterprise resource planning (ERP) is critical to an organization’s success. However, the factors that contribute to the success and usage of these ERP systems have received little attention. This study developed and validation of an improved DeLone-McLean IS success model. Additionally, we examined the factors which influence ERP system usage, employee satisfaction, information quality, service quality, and system quality, as well as the factors that influence the system’s overall success. The proposed model is based on a mixed-methods case study (MM-CS). The results show that the proposed model significantly measures the success of an ERP system. The organizational climate, the information quality, the system quality, and the service quality all have an impact on the usage of an ERP system. The proposed model also shows that the use of an ERP system, training and learning, and the three information (IS) quality constructs are all significant predictors of user satisfaction. The results also indicate that gender and years of ICT use on the path of ERP users have a moderating effect on the relationship between teamwork & support and use.