Revisiting the Dualism of Point-in-Time and Through-the-Cycle Credit Risk Measures

In this paper, we investigate the role of liquidity in banks lending activity and how liquidity provision is related to bank’s credit risk and others market-based risk measures, such as bank’s implied volatility skew from options traded on the market and realized volatility from futures contract on LIBOR, during periods of global financial distress. Credit risk is given by the ratio between loan loss reserves and total assets and we find that losses from lending activity force banks to build up new liquidity provisions only during the period of financial distress. Liquidity ratio is given by the sum of cash and short-term assets over total assets and we discovered that credit risk reduces liquidity ratio only in bad times, as this demand for liquid asset is suddenly switched on and the more reserves from loan losses the bank has, the more it cleans its balance sheet from long-term commitments in order to replenish its cash and short-term securities. When we control for market-based risk measures, we evidence that both implied volatility skew and LIBOR’s realized volatility are negatively related with the liquidity ratio and are useful in predicting a distress in bank’s liquidity holdings.

Download Full-text

Energy Risk Management

10.1093/oso/9780190656010.003.0024 ◽

2018 ◽

Author(s):

Thomas Barkley

Keyword(s):

Risk Management ◽

Credit Risk ◽

Value At Risk ◽

Risk Measures ◽

Commodity Prices ◽

End User ◽

Energy Sustainability ◽

Business Operation ◽

Energy Risk Management ◽

Energy Companies

The backdrop of rapid growth of worldwide energy consumption and increasing concerns about global energy sustainability and environment protection, as well as an increasing uncertainty of commodity prices, require energy companies to use derivatives to hedge against risks related to energy trading. Over time, this situation has led to a more important role for energy risk management as part of a company’s core business operation. This chapter discusses the primary financial instruments used in the energy sector and risk management for energy companies. It reviews the application of several important quantitative methodologies, including Value at Risk and its variant risk metrics, to measure market risk. The chapter also examines credit risk measures and credit risk migration. Lastly, it discusses liquidity risk, operational risk, and legal risk. Overall, the chapter focuses more on the risk the commodity producer/deliverer faces and less on the end user.

Download Full-text

Comparing risk measures when aggregating market risk and credit risk using different copulas

The Journal of Risk ◽

10.21314/jor.2016.335 ◽

2016 ◽

Author(s):

Jakob Maciag ◽

Frederik Hesse ◽

Rolf Boeve ◽

Andreas Pfingsten

Keyword(s):

Credit Risk ◽

Risk Measures ◽

Market Risk

Download Full-text

A Discussion on Recent Risk Measures with Application to Credit Risk: Calculating Risk Contributions and Identifying Risk Concentrations

Risks ◽

10.3390/risks6040142 ◽

2018 ◽

Vol 6 (4) ◽

pp. 142 ◽

Cited By ~ 2

Author(s):

Matthias Fischer ◽

Thorsten Moser ◽

Marius Pfeuffer

Keyword(s):

Credit Risk ◽

Value At Risk ◽

Risk Measures ◽

Risk Measure ◽

Theory And Practice ◽

Credit Risk Management ◽

It Implementation ◽

Benchmark Portfolio ◽

Credit Portfolio ◽

First Time

In both financial theory and practice, Value-at-risk (VaR) has become the predominant risk measure in the last two decades. Nevertheless, there is a lively and controverse on-going discussion about possible alternatives. Against this background, our first objective is to provide a current overview of related competitors with the focus on credit risk management which includes definition, references, striking properties and classification. The second part is dedicated to the measurement of risk concentrations of credit portfolios. Typically, credit portfolio models are used to calculate the overall risk (measure) of a portfolio. Subsequently, Euler’s allocation scheme is applied to break the portfolio risk down to single counterparties (or different subportfolios) in order to identify risk concentrations. We first carry together the Euler formulae for the risk measures under consideration. In two cases (Median Shortfall and Range-VaR), explicit formulae are presented for the first time. Afterwards, we present a comprehensive study for a benchmark portfolio according to Duellmann and Masschelein (2007) and nine different risk measures in conjunction with the Euler allocation. It is empirically shown that—in principle—all risk measures are capable of identifying both sectoral and single-name concentration. However, both complexity of IT implementation and sensitivity of the risk figures w.r.t. changes of portfolio quality vary across the specific risk measures.

Download Full-text

Blockchains, Real-time Accounting, and the Future of Credit Risk Modeling

Ledger ◽

10.5195/ledger.2019.100 ◽

2019 ◽

Vol 4 ◽

Cited By ~ 8

Author(s):

Hans Byström

Keyword(s):

Credit Risk ◽

Real Time ◽

Risk Measures ◽

Substantial Effect ◽

Risk Modeling ◽

Accounting Data ◽

Default Prediction ◽

The Future ◽

Z Scores

In this paper I discuss how blockchains potentially could affect the way credit risk is modeled, and how the improved trust and timing associated with blockchain-enabled real-time accounting could improve default prediction. To demonstrate the (quite substantial) effect the change would have on well-known credit risk measures, a simple case-study compares Z-scores and Merton distances to default computed using typical accounting data of today to the same risk measures computed under a hypothetical future blockchain regime.

Download Full-text