Credit assets held by shadow banking entities increased 5.7% in 2018, up from 4.7% the year prior, to $44.9 trillion, with hedge funds and broker-dealers hoovering them up the fastest.

Among other financial intermediaries (OFIs), as defined by the Financial Stability Board (FSB), loans, bonds and deposits held by hedge funds grew fastest, by 20.1% in 2018.

Broker-dealers followed, expanding credit assets, excluding deposits, by 10.3% to $4.8 trillion. Money market funds (MMFs) increased their

COMMENTARY: Innovation farms

This week, Risk.net looked at examples of technological innovation across the banking sector – the utility settlement coin aimed at speeding up cash settlement legs, artificial intelligence market analysis, quantum computing, asset tokenisation and many more.

It’s striking how much of this innovation comes from inside large established banks, rather than challenger start-ups – certainly a contrast to the situation in, for example, goods retail (how many venerable department stores and high street chains have now collapsed in the face of Amazon?). What is it about banking that makes it so resistant to disruption? Why has, for example, Facebook’s attempt at an e-currency, Libra, failed to take off so far, while banks’ own attempts at digital currencies are showing promising signs of growth?

Technological innovation seems to come mostly from within, and there are three interlocking reasons for this: funding, data and...

The Risk Training Ibor series continues with a new course focusing on the transition from Euribor to risk-free rates. 

Attendees will gain a detailed insight into a wide range of areas that will be affected by the Euribor transition including necessary operational changes, accounting implications and fallback provisions.

This course provides an opportunity to discuss working methods and challenges with other experienced professionals.

Bank of America has hired Adolfo Montoro as a director in its global risk analytics function. Montoro previously held various senior roles at Deutsche Bank, where he served as risk analytics lead for large-scale regulatory implementation projects.

Montoro joined BofA in December 2019, a spokesperson confirms, and will continue to be based in London.

At Deutsche, Montoro worked on re-engineering the bank’s market risk analytics and market data frameworks, and on revamping underlying

Swaps trades referencing the Sterling Overnight Index Average (Sonia) soared to at least four year highs last week, according to data from the Depository Trust & Clearing Corporation’s (DTCC) trade repository.

The £174 billion ($226 billion) and £154 billion in notional traded on Thursday (January 9) and Friday (January 10), respectively, made the days both the largest single and largest consecutive trading days for Sonia-referencing swaps since at least the start of 2016, and possibly all-time

The Risk Training Ibor series continues with a new course focusing on the transition from Euribor to risk-free rates. 

Attendees will gain a detailed insight into a wide range of areas that will be affected by the Euribor transition including necessary operational changes, accounting implications and fallback provisions.

This course provides an opportunity to discuss working methods and challenges with other experienced professionals.

Optimising collateral coverage in fast-moving market conditions is challenging at the best of times. But in China – where corporate creditworthiness can sometimes be unclear – the trigger indicators can be even harder to read. One brokerage firm is using machine learning to up its game.

Mega-broker Haitong International (HTI) is now adopting natural language processing (NLP) algorithms – a family of machine learning techniques that learn patterns from free-form data such as text and adapt their

Eurex Clearing will raise the mandatory contributions to its default fund to 9% of an average margin metric as changes in its business mix have made its cash hoard look thin in stress tests.

The increase from the current 7% contribution goes into effect on Monday, February 3.

Eurex says the need for a huskier default fund arose from a tilt in its business. By the third quarter of last year, over-the-counter business had come to account for 26% of initial margin (IM) – up from a mere 7% in the

Possibly the most basic tool in any risk manager’s kit is the simplest: pick your battles. However, the UK’s Pension Protection Fund (PPF) – which steps in when a defined benefit pension can no longer pay its retirees – has no such option.

By the time the agency is on the scene, a problem has already arisen, ranging from minor to ground-shifting: a UK company offering a defined benefit scheme is in trouble, and its pension scheme requires assessment and, in the worst case, a bailout. The PPF

For this Quantcast, Risk.net spoke with Andrew Dickinson, who leads the CCP analytics group at Bank of America.

With Leif Andersen, global head of the quantitative strategies group, Dickinson developed a model to assess exposure to a central counterparty. Crucially, the model has the ability to capture wrong-way risk stemming from the presence of clearing members with outsized positions. Such positions can trigger the member’s default in the case of large, adverse market moves.

If the defaulting member has an unhedged position and is unable to meet the margin calls, the CCP’s default fund could suffer significant losses. The case of power trader Einar Aas’ default at Nasdaq Clearing in September 2018, and the default fund’s subsequent loss of $119.7 million, set alarm bells ringing for banks, CCPs and regulators.

Required initial margin (IM) held by SwapClear, LCH’s interest rate swaps-clearing service, at end-September was £145.1 billion, up 16% from end-June and 46% from a year-ago, following changes to its value-at-risk model.

The split of required IM was 40% for house accounts, 54% for client gross accounts and 6% for client net accounts. Quarter-on-quarter, house net margin increased 4%, client gross 24% and client net 39%. 

On July 22, SwapClear updated the parameters of its IM model, increasing

Jump to Spotlight: UBS defamation case | In Focus: bank fines

In December’s largest loss, the Zurich Court of Appeal ordered Swiss private bank Julius Baer to pay Sfr153 million ($154.6 million) to the German government over alleged unauthorised withdrawals made from an account at its subsidiary, Cantrade Bank, dating back to the fall of the Berlin Wall.

In 2014, Bundesanstalt für vereinigungsbedingte Sonderaufgaben – a government body that seeks to track down assets previously held by the

Possibly the most basic tool in any risk manager’s kit is the simplest: pick your battles. However, the UK’s Pension Protection Fund (PPF) – which steps in when a defined benefit pension can no longer pay its retirees – has no such option.

By the time the agency is on the scene, a problem has already arisen, ranging from minor to ground-shifting: a UK company offering a defined benefit scheme is in trouble, and its pension scheme requires assessment and, in the worst case, a bailout. The PPF

For this Quantcast, Risk.net spoke with Andrew Dickinson, who leads the CCP analytics group at Bank of America.

With Leif Andersen, global head of the quantitative strategies group, Dickinson developed a model to assess exposure to a central counterparty. Crucially, the model has the ability to capture wrong-way risk stemming from the presence of clearing members with outsized positions. Such positions can trigger the member’s default in the case of large, adverse market moves.

If the defaulting member has an unhedged position and is unable to meet the margin calls, the CCP’s default fund could suffer significant losses. The case of power trader Einar Aas’ default at Nasdaq Clearing in September 2018, and the default fund’s subsequent loss of $119.7 million, set alarm bells ringing for banks, CCPs and regulators.

US retailers appeared to have turned a corner in 2018. The Trump administration’s tax cuts lifted consumer spending and retail sales grew by more than 6% that summer. After a period of steady decline, the credit ratings of major US retailers began rising again. 

The optimism was short-lived. The industry’s fortunes deteriorated sharply in 2019, with 23 major bankruptcies – including clothing retailers Diesel and Forever 21 and department store Barneys – and nearly 10,000 store closures. According to Credit Benchmark data, sourced from financial institutions, credit quality in the US retail sector has dropped 5% since April 2019. 

The UK retail sector has fared even worse. With Brexit uncertainty sapping consumer sentiment, and no fiscal stimulus to boost spending, the credit risk of UK retailers has increased by 13% since 2017. 

A sustained turnaround is unlikely. Bricks and mortar retailers appear to have no answer for Amazon and other...

COMMENTARY: No margin for error

As quants at Bank of America pointed out this week in a technical paper, one clearing member’s disproportionately large position increases the credit risk for all members of a central counterparty (CCP). And, as the loss at Nasdaq in 2018 showed, the financial industry must remain vigilant and regularly reappraise the risks and costs arising from margin and loss-sharing mechanisms.

In September 2018, Nasdaq Clearing members nursed losses of €107 million ($122 million) when an independent commodity trader clearing his own account saw his bet that the spread between Nordic and German electricity futures would narrow go catastrophically wrong – drawing criticism that the CCP should have demanded more margin.

A number of clearing houses are already experiencing upticks in margin posted to balance counterparty risk. There has been an overall increase in initial margin posted to Eurex in recent quarters, and required initial margin...

The CECL implementation deadline is approaching. Banks and other financial institutions should be evaluating the likely effects of the standard on them and deciding what their next steps are.

This course will consider a range of key areas, including: challenges & opportunities; CECL quantification; model risk management for CECL; and internal controls framework. 

This course is CPE accredited and delegates will earn 12 points if in attendance for both days of the course. The course will be held under Chatham House Rule with the opportunity for discussion within a practical learning environment. 

This training course will address in-depth the opportunities and limitations of machine learning in quantitative finance with practical guidance from a variety of expert tutors.

Sessions will cover key theories, models and more advanced tools in machine learning using a quantitative approach. The course will examine what impact machine learning has on trading, portfolio construction and optimisation as well as focus on deep neural networks, applications of natural language processing , trading strategies and more.

The Japan Securities Clearing Corporation (JSCC) plans to cap the amount its members must pay to replenish its guarantee fund in the event of a default by a clearing participant on its futures markets – addressing members’ long-standing complaints that unlimited liability presents an unquantifiable risk.

In tandem, the CCP is making a raft of changes to its margin model for futures and options clearing that will increase initial margin requirements and better balance the split between margins

By operational risk standards, 2019 was a modest year. Its $17.4 billion in losses look almost cursory next to the behemoth amounts of the recent past: $42.1 billion in 2018, $28.2 billion in 2017 and the astounding $56.9 billion of 2016.

Should anything be read into this? Has the industry turned a leaf? Will a virtuous cycle see op risk losses drop to negligible amounts over the next decade?

Judging by 2019, risk managers have little reason to fear redundancy any time soon: theft, tax evasion

This training course will address in-depth the opportunities and limitations of machine learning in quantitative finance with practical guidance from a variety of expert tutors.

Sessions will cover key theories, models and more advanced tools in machine learning using a quantitative approach. The course will examine what impact machine learning has on trading, portfolio construction and optimisation as well as focus on deep neural networks, applications of natural language processing , trading strategies and more.