Large derivatives counterparties rarely default, but when they do, cleaning up the mess can be a fraught process. Dealers must close out trades, realise collateral and deduct any residual losses from capital. Potentially costly, but unambiguous.
However, Credit Suisse’s handling of the Archegos default in 2021 has revived a controversy – one that first arose after the fall of Lehman Brothers, but was never resolved – over how trades should be booked while they’re being closed out.
One former
Banks cannot rely on assumptions that operational risks are correlated with economic factors, casting doubt on the loss projections used in bank stress tests, new research suggests.
Operational risk predictions are a key part of regulatory stress-testing, but “wide error margins” in the correlations mean the forecasts may be flawed, according to a paper by Peter Mitic, head of operational risk methodology for UK at Santander. The paper was published in the Journal of Operational Risk in June.
Geopolitical risk has surged to the top of investors’ agendas this year, following Russia’s invasion of Ukraine and the consequent impact on global supply chains.
That does not mean investors have a rigorous scientific basis for forecasting it. This, at least, is the view of Joseph Simonian, a quant with long experience at the intersection of finance and data science.
Simonian was senior investment strategist at Acadian Asset Management and a director of quant research at Natixis Investment
Many will wish they could rewrite recent history and edit Covid out of the picture. And, in a roundabout way, that’s what some banks are doing with the Covid narrative around their credit risk models.
At the start of the pandemic, bank models, unsurprisingly, began to predict major losses. Contrary to its general toll on humanity, however, the economic after-effects of Covid were not as disastrous as anticipated – largely because of the high degree of government intervention in the world’s
European banks want regulators to rescind new guidance requiring them to set aside capital against fleeting exposure spikes that occur when cashflows are exchanged on interest rate swaps.
When a bank makes an interest payment on a swap, it immediately calls for variation margin from the client to reset its exposure to the trade. The collateral is usually delivered the following day, resulting in a brief period where the bank has an elevated level of counterparty exposure to the client. The
The head of markets clearing and futures execution at Wells Fargo, George Simonetti, has left the bank, according to a spokesperson.
Simonetti left the bank in June after a 27-year career with Wells, which culminated in him taking the top job in clearing in 2012. Based in Charlotte, North Carolina, Simonetti was responsible for all over-the-counter and listed derivatives clearing for both the bank’s house account and for clients, according to his Tumblr blog.
The veteran developed the tiny
The scale of the losses sustained by Credit Suisse when a now-infamous client defaulted in March shocked the financial industry. Many things went wrong at the bank to result in the haemorrhage of $5.5 billion, but one of the most unexpected was the fact that the person managing the risk posed to Credit Suisse by Archegos was a former sales and marketing executive, rather than a risk management professional.
A recently published tell-all independent report lists, among other missteps by Credit
A team of researchers at Wells Fargo has begun deploying a novel explainability technique for deep learning models – something the bank hopes will allow it to begin using more complex approaches to power credit decisioning.
Banks have long sought to tap the potential of neural networking – a family of deep learning approaches that works by seeking to replicate human thought patterns – for complex problem-solving in credit risk. Yet the technique finds itself underused, since models that rest on
Two quants at one of the world’s largest clearing houses believe they have come up with a remedy for one of the thorniest challenges facing their industry: how to stop initial margin models, designed to protect against future risk exposure, from ramping up too dramatically when volatility suddenly spikes?
In a paper published in the Journal of Risk, Lauren Wong and Yang Zhang, two quants from the Options Clearing Corporation (OCC), propose a dynamic approach to risk-based margining which they
In July’s largest loss, Westpac was defrauded of US$255.1 million by Sydney-based equipment lease company Forum Finance. It was one of four financial institutions caught up in the scheme, which involved using false invoices and forged signatures to fraudulently obtain loans.
According to Westpac, the alleged fraud related to a portfolio of equipment leases with Westpac customers that were arranged by Forum Finance. The latter offered and arranged lease financing from banks to its clients for
Capitolis, a start-up which operates a peer-to-peer market that allows banks to optimise or offload capital-intensive exposures, has struck a deal to acquire foreign exchange-focused compression provider, LMRKTS. The deal, whose terms have not been disclosed, is set to close at the end of August.
The tie-up combines Capitolis’s bilateral optimisation operations with LMRKTS’s multilateral focus to create “the most complete trade compression solutions” in the market, according to a statement.
Gi
Eurex is set to introduce a new auction procedure to its default management process, which would see members compete to conduct hedges for a defaulting member’s portfolio before the positions are liquidated.
The new component would precede the main liquidation auction in the event of a clearing member collapse. It replaces an opaque process in which the Frankfurt-based clearing house currently appoints a hedging provider in consultation with its default management committee (DMC), in an attempt
A scenario is more than a number: it is a narrative. And constructing the scenario is akin to writing a novel. At its heart is a plotline that twists and turns, driving the reader forward. It has a catalyst that gets the action going. Its setting is the market environment: perhaps investor exuberance and leverage, tight money and credit constraints, or high concentration and momentum.
Financial risk managers can use a multi-pronged process to build scenarios. First, we identify a catalytic
From derivatives pricing to credit card fraud detection – and a few places in between – artificial intelligence is extending its reach across the financial sector. But difficulties with explaining to regulators and senior management how self-learning algorithms work continue to hold back the use of machine learning in most banks’ core business of lending.
“Credit underwriting is the highest risk use of this technology and we would expect a great deal of explainability to be provided,” says a
For high-frequency traders, the expression ‘time is money’ holds a literal truth.
Firms that use algorithmic trading strategies must time-stamp transactions to the nearest fraction of a millisecond. This requires internal systems capable of staying in near-perfect sync.
A UK government initiative is aiming to develop technology that delivers a more reliable and accurate indicator of time than existing satellites. The scheme could be an important step in enabling high-frequency trading (HFT)
Ten of the world’s largest clearing banks have agreed to fund the growth of an industry utility tasked with helping standardise post-trade workflows – seen as vital in preventing a repeat of the operational bottlenecks that crippled futures trade processing during last year’s Covid-19 market meltdown.
FIA Tech, a for-profit subsidiary of industry trade body the Futures Industry Association, has received $44 million in funding from a group of futures commission merchants (FCMs), including
Clearing banks are up in arms after a seemingly arcane change to the way US regulations categorise their clients for the purposes of margining led to large funds lobbying for their requirements to be dropped by as much as 9% versus previous levels.
On January 27, an amendment to US Commodity Futures Trading Commission (CFTC) Regulation 39.13(g)(8)(ii) – which governs the way clearing houses and futures commission merchants (FCMs) should treat clients when determining base margin requirements –
Cyber attacks pose the most serious threat to US financial institutions and the system as a whole, the chief executives of four of the nation’s largest banks told Congress yesterday.
Asked by Representative Bill Huizenga of Michigan to name the biggest risk facing the financial sector, the CEOs of Citigroup, Goldman Sachs, Morgan Stanley and Wells Fargo each singled out cyber threats.
“Cyber, and specifically the potential impact on consumer data and data privacy,” said James Gorman, CEO of
Credit Suisse’s recent losses on its Archegos financing remind me of an announcement – nearly 10 years ago – from the repo desk of my then-employer, at our weekly sales meeting. The desk had proudly booked $3 million of profits on a long-dated, multi-billion-euro structured trade with the National Bank of Greece. When I enquired about the collateral, I learned it was composed of Greek government bonds. My heart sank.
This was only the beginning of the eurozone crisis, and many institutions
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COMMENTARY: The new worst cases
There’s a striking chart in Risk.net’s summary of its second scenario crowdsourcing exercise: it shows the survey pool’s first attempts to forecast the future movement of the S&P 500 back in March, their second attempts in the more recent exercise, and the actual course of the index so far. And though the index itself recovered smoothly back to its pre-pandemic peak, the uncertainty around its future movements is actually higher now than it was in late March, when the forecasters were looking at explosive growth in the pandemic and a slumping index.
What’s going on?
There are two broad answers: either uncertainty about future economic scenarios actually is greater now than in March; or it was always this big and the survey pool is now starting to recognise that. Since it is not possible to ‘rerun’ 2020 a few thousand times to get an accurate idea of the distribution of possible outcomes (the idea of rerunning 2020 even...
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