The situation of a limited availability of historical data is frequently encountered in portfolio risk estimation, especially in credit risk estimation. This makes it, for example, difficult to find temporal structures with statistical significance in the data on the single asset level. By contrast, there is often a broader availability of cross-sectional data, i.e., a large number of assets in...

Estimation of risk of the large portfolios of credit risky securities is the problem that can be studied using Monte Carlo methods. The main difficulties include the large number of risk factors (interest rates, fx rates, ...) and statistical dependencies between probabilities of default and market risk factors. There are several variance reduction techniques (importance sampling, stratifies sa...

We introduce an equity-credit portfolio framework taking into account the structural interaction of market and credit risk, along with their systemic dependencies. We derive an explicit expression for the optimal investment strategy in stocks and credit default swaps (CDSs). We exploit its representation structure and analyze the mechanisms driving the optimal investment decisions. The transmis...

A typical shortcoming of most current credit portfolio models is the lack of a stochastic modeling of risk factors, such as interest rates or credit spreads, during the revaluation process at the risk horizon. Within the simple credit risk model underlying the Internal Ratings-based approach of Basel II with incorporated correlated interest rate risk the effect which results from neglecting the...

Multi-asset class (MAC) portfolios can be comprised of investments in equities, fixed-income, commodities, foreign-exchange, credit, derivatives, and alternatives such as real-estate and private equity. The return for such non-linear portfolios is asymmetric with significant tail risk. The traditional Markowitz Mean-Variance Optimization (MVO) framework, that linearizes all the assets in the po...

The goal of this paper is to determine the Incremental Risk Charge (IRC) and the Comprehensive Risk Measure (CRM) of a portfolio consisting of credit derivatives and tranches. More specifically, we implement different methods to calibrate default intensity models, backtest our IRC calculations over historical data, and focus our attention on a basket of Credit Default Swaps (CDS).

In this paper we present a new mean–variance customer portfolio optimization algorithm for a class of ergodic finite controllable Markov chains. In order to have a realistic result we propose an iterated twostep method for solving the given portfolio constraint problem: (a) the first step is designed to optimize the nonlinear problem using a quadratic programming method for finding the long run...

In this paper, we solve the intertemporal investment problem of an investor holding a portfolio of default-free and defaultable bonds. Default-risk is modeled in an intensity based framework with state variables following an a¢ne di¤usion. The structure of the optimal portfolio over time is investigated and compared to the static meanvariance portfolio. Furthermore, we describe the impact of ti...