GS2 Flashcards
Bitcoin: Economics, Technology, and Governance. Important characteristics of Bitcoin?
Bitcoin’s design allows for
- irreversible transactions,
- a prescribed path of money creation over time,
- a public transaction history
Bitcoin: Economics, Technology, and Governance.
What are the design principles?
• Scarcity of a money supply;
• Lacks a centralised authority to distribute coins or to track who holds which coins;
• Issues new currency to private parties at a controlled pace in order to provide an incentive to
maintain bookkeeping system, including verifying the validity of transactions.
Bitcoin: Economics, Technology, and Governance.
The “block chain”?
Mining?
1) Data structure that verifies all past Bitcoin activity.
2) the Bitcoin system periodically awards newly minted bitcoins to the user who solves a
mathematical puzzle that is based on the pre-existing contents of the block (the solution must be verified by other users, which can cause a delay).
Bitcoin: Economics, Technology, and Governance.
Two fundamental technologies?
- Public-private key cryptography (to store and spend money).
- Cryptographic validation of transactions (an instruction to transfer money is encrypted using the sender’s private key,
confirming for everyone that the instruction in fact came from the sender).
Bitcoin: Economics, Technology, and Governance.
What are the intermediaries - Centralisation?
- Currency Exchanges: designated institutions that convert traditional currencies into bitcoins.
Need online infrastructure capable of withstanding attacks including hacking and denial-of-service attacks. - Digital Wallet Services: applications for managing a user’s account in a more convenient way (less space, more mobility, better interface), but prone to hackers.
- Mixers: to preserve privacy against this tactic, mixers let users pool sets of transactions
in unpredictable combinations, thus preventing tracking across transactions (however, timing can still yield a clue). - Mining Pools: more difficult puzzles and lumpy rewards. In response, mining pools now combine resources from numerous
miners. Miners work independently, but share earnings with others in the pool. Threaten the decentralisation that underpins Bitcoin’s trustworthiness (manipulation risk).
Bitcoin: Economics, Technology, and Governance.
Users of Bitcoin?
Gambling services, retailers (and consumers), buy & hold for price appreciation
Bitcoin: Economics, Technology, and Governance.
Risks in Bitcoin
- Market risk: the value of bitcoins is highly volatile (fluctuations in the exchange rate between bitcoin and other currencies)
- Shallow market problem (liquidity risk): a person seeking to trade a large amount of bitcoins typically cannot do so quickly without affecting the market price
- Counterparty risk: currency exchanges may cease operations (45%) without reimbursing their consumers (46%), while digital wallet services are lucrative targets for cybercriminals
- Transaction risk: (irreversible transactions), possibility to cancel the payment (by the majority/collusion), blacklisting.
- Operational risk: Threats to security of the private key, Miner “51 percent attack”, Denial-of-service attack
- Privacy risk
- Legal and regulatory risk
Bitcoin: Economics, Technology, and Governance.
Fighting Crime - types of crime?
- 1 Bitcoin-specific crime (attacks on currency exchanges and infrastructure, e.g. bitcoin theft, attacks on mining pools, and denial-of-service attacks on exchanges to manipulate exchange rates)
- 2 Money laundering (through mixers)
- 3 Bitcoin-facilitated crime: payment for unlawful services delivered (or purportedly delivered) offline, like the illegal goods and services sold on Silk Road and payment of funds in extortion
Bitcoin: Economics, Technology, and Governance.
Future concerns?
- Bitcoin as a Financial Asset —> diversification benefits and arbitrage
- Incentive-compatibility in Bitcoin Protocols
- Privacy and Anonymity —> transaction patterns
- Monetary Policy —> “k-percent rule” – fixing the annual growth rate of the money supply
- Will it replace other forms of payments completely (low cost, privacy, decentralisation)?
- Numerous competing virtual currencies that are waiting to achieve confidence in their values and adoption
Prone to Fail: The Pre-Crisis Financial System.
The key sources of fragility?
- Weakly supervised balance sheets of the largest banks and investment banks - “By relying on these sources of funding, dealers were much more vulnerable to runs than was generally appreciated.”
- The run-prone designs.
- Weak regulation of the markets for securities financing and over-the-counter derivatives.
- The undue reliance of regulators on “market discipline” & too-big-to-fail.
Prone to Fail: The Pre-Crisis Financial System.
Why did regulators fail to safeguard Financial Stability?
Inappropriate goals - protect the customers of financial firms, no focus on financial stability.
Too difficult for regulators to detect the excessive buildup of risk and flight-prone shortrun debt and derivatives.
The historical US emphasis on a decentralised banking system
Irrationally low probabilities to disaster outcomes.
The SEC had not supervised the investment banks (or their subsidiaries) adequately from the viewpoint of solvency.
Prone to Fail: The Pre-Crisis Financial System.
Core Meltdown Risks?
Credit provision dependent on capital markets ->
Largest dealers financed enormous quantities of
inventoried securities with very short-term debt
—> Risks: (1) creditor runs and (2) fire-sale losses
…up to $2.8 trillion in intra-day financing was provided to the dealers every day by the two tri-party
agent banks —> systemic risk (on top of the two agents being large creditors)…
an alternative short-term funding - “commercial paper” (that is, unsecured debt typically issued for up to six or nine months), either directly or indirectly
through off-balance-sheet “structured investment vehicles” (SIVs) created by banks -> liquidity risk
Prone to Fail: The Pre-Crisis Financial System.
Regulatory response since the crisis (financial system)?
- The elimination of intra-day credit provision by tri-party agent banks.
- The securities inventories themselves are also much smaller —> the need for financing has been reduced
- Because of the declining presumption by bank
creditors of “too big to fail,” dealer financing costs have gone up substantially, so the incentive to hold giant inventories is much reduced. - The dependence of dealers on flight-prone financing from money market mutual funds has been lowered by a tightening of the regulation of those money funds.
- Bank capital requirements now apply to all large dealers at the holding company level.
- The two surviving investment banks became regulated as banks; substantial new bank liquidity
coverage regulations have been introduced, forcing runnable short-term financing to be covered by a stock of high-quality liquid and unencumbered assets
Prone to Fail: The Pre-Crisis Financial System.
Why did the over-the-counter derivatives market contribute to the crisis?
- no regulations of the minimum margin, central clearing, and trade reporting
- unobservable counter-party exposures and the degree of their protection by collateral
- as asset prices related to subprime mortgages fell sharply and concern about counterparty creditworthiness grew,
margin calls on derivatives acted as a stress amplifier (AIG’s sudden and heavy cash margin calls on
credit-default-swap protection)
Prone to Fail: The Pre-Crisis Financial System.
Regulatory response since the crisis (OTC)?
- increased use of central clearing
—> improves the transparency of derivatives positions
—> enforces uniform collateral practices that are more easily supervised by regulators
—> all swap transactions must be reported publicly - new regulatory capital requirements
—> all inter-dealer swaps have minimum margin requirements
—> the largest dealers are now subject to markedly higher capital requirements on their OTC derivatives exposures - Compression trading —> fintech approach that helps to eliminate redundant sequences of derivative positions
Moore’s Law versus Murphy’s Law.
Explain the two laws.
Moore’s Law in financial markets: from 1929 to 2009 the total market capitalisation of the US stock
market has been doubling every decade
Murphy’s Law: “Whatever can go wrong will go wrong” (faster and with worse consequences when
computers are involved)
Moore’s Law versus Murphy’s Law.
3 developments in the financial industry have greatly facilitated the rise of AT?
- Greater complexity is a consequence of general economic growth and globalisation
- Quantitative modelling
- Computer technology
Moore’s Law versus Murphy’s Law.
5 major developments that have fuelled growing popularity of AT?
- Quantitative models in finance
- The emergence and proliferation of index funds
- Arbitrage trading activities
- The push for lower costs of intermediation and execution
- The proliferation of high-frequency trading
Moore’s Law versus Murphy’s Law.
How has the meaning of passive investing changed?
An investment process is “passive” if it does not require any discretionary human intervention— it is based on a welldefined and transparent algorithm. Such a definition decouples active investing from active trading; today, a passive investor may be an active trader to minimise transaction costs, manage risks more adroitly, participate in new investment opportunities or respond more quickly to changing objectives and market conditions.
Moore’s Law versus Murphy’s Law.
Execution strategy?
Executing a large order in a single transaction is typically more costly than breaking up the order into a sequence of smaller orders;
The method for determining the timing and sizes of smaller orders is called an “execution strategy,” and optimal execution strategies can be derived by specifying an objective function and a statistical model for stock-price dynamics.
Moore’s Law versus Murphy’s Law.
Arbitrage Gone Wild - “Quant Meltdown” - what happened?
The Unwind Hypothesis:
• the initial losses during the second week of August 2007 were due to the forced liquidation of one or
more large equity market-neutral portfolios -> the deadly
feedback loop of coordinated forced liquidations leading to the deterioration of collateral value.
Bottom line: the scaling up and down of portfolios can affect many other portfolios and investors.
AT greatly magnifies the impact of these consequences.
Moore’s Law versus Murphy’s Law.
The Perfect Financial Storm - the “Flash Crash” - what happened?
Accenture traded at a penny a share, while Apple traded at $100,000 per share
Reasons:
• An automated execution algorithm on autopilot (a rapid automated sale of 75,000 E-mini S&P 500 June
2010 stock index futures contracts (worth about $4.1 billion))
• a game of “hot potato” among high-frequency traders
• cross-market arbitrage trading
Moore’s Law versus Murphy’s Law.
High-Frequency Manipulation types?
• “Spoofing”: intentionally manipulating prices by placing an order to buy or sell a security and then cancelling it shortly thereafter, at which point the spoofer consummates a trade in the opposite direction of
the cancelled order
• “Layering”: placing a sequence of limit orders at successively increasing or decreasing prices to give the
the appearance of a change in demand and artificially increase or decrease the price that unsuspecting
investors are willing to pay; after a trade is consummated at the manipulated price, the layered limit orders are cancelled
Moore’s Law versus Murphy’s Law.
Financial Regulation 2.0
- Systems-Engineered: should approach financial markets as complex systems composed of multiple software applications, hardware devices and human
personnel - Safeguards-Heavy: both human and machine safeguards are necessary to ensure the safe functioning of the system
- Transparency-rich: should aim to make the design of financial products more transparent and accessible to regular automated audits
- Platform-neutral: should be designed to encourage innovation in technology and finance and should be neutral with respect to the specifics of how core
computing technologies work
