In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of … Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the … [increasingly] Bayesian—continues to be a key methodological foundation of risk management and regulation-related risk modeling …

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time … the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using alternative methods …

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 and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …

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 … around mid-2013. Based upon review of related financial risk modeling practices and exponentially increasing Cyber era …

Tail risk refers to the possibility that a rare event would adversely affect the value of a portfolio in a significant … manner. It became much more relevant due to recent periods of strong market turbulence.We describe how to quantify such risk …, which tail risk protection strategies were considered in the literature, their effectiveness and associated costs. We also …

show that the absence of the risk-return relationship in the high volatility state is due to leverage and volatility … modification that yields a positive tail risk-return relationship in all states of market volatility …We investigate the dynamics of the relationship between returns and extreme downside risk in different states of the …

series patterns for currency risk management.Our approach is Bayesian where extensive use is made of Markov chainMonte Carlo … methods. The effects of several model characteristics(unit roots, GARCH, stochastic volatility, heavy tailed …

exchange rates for currency risk management. Ourapproach is Bayesianwhere extensive use is made of Markov chain Monte Carlo … methods. The effects ofseveral modelcharacteristics (unit roots, GARCH, stochastic volatility, heavy taileddisturbance …