Markov chain Monte Carlo (MCMC) methods have an important role in solving high dimensionality stochastic problems characterized by computational complexity. Given their critical importance, there is need for network and security risk management research to relate the MCMC quantitative...

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of Bayesian inference on subjective judgment, the key limitation...

This paper answers two research questions: what is the appropriate modeling tool for NPL study? and whether the NPL rates in Thailand show improving or deteriorating trend? NPL is of interests to management decision makers because it serves as an indicator for assessing risk in commercial loans....

Purpose - The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability. Design/methodology/approach - Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered...

We propose a new concept of modulated bipower variation for diffusion models with microstructure noise. We show that this method provides simple estimates for such important quantities as integrated volatility or integrated quarticity. Under mild conditions the consistency of modulated bipower...

Market risk can be described as potential losses in portfolio value caused by price changes in the investor's portfolio. Value-at-Risk (VaR) quantifies a loss bound that cannot be exceeded with a specified probability at a given time horizon, i.e., a quantile of the portfolio's loss...

We define a non-parametric estimator of the integrated leverage effect as the covariance between the logarithmic asset price and its volatility. In Curato and Sanfelici (2015), a consistent estimator of the leverage effect has been introduced through a pre-estimate of the Fourier coefficients of...

This presentation reconsiders Knight's Risk, Uncertainty, and Profit of 1921 in light of the emergence of the World Wide Web in early-1990s, Emanuel Derman's pioneering work in Model Risk Management at Goldman Sachs in mid-1990s, backlash against quantitative models in aftermath of the Global...

We provide a tutorial that illuminates the aspects which need to be considered when fitting neural network regression models to claims frequency data in insurance. We discuss feature pre-processing, choice of loss function, choice of neural network architecture, class imbalance problem, balance...

This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...