Transact taxes in a price maker/taker market

We develop a price maker/taker model to study how a financial transaction tax affects markets. We find taxes widen quoted and effective spreads by more than twice the tax. Taxes increase volatility slightly (without intermediation) to significantly (with intermediation). High taxes may halve volumes and gains from trade while doubling search costs. Measures of market quality are more affected by taxes in markets with intermediaries. Investors and intermediaries competing for liquidity can triple search costs and increase quoted spreads while decreasing effective spreads. We also find revenue-optimal rates of 60-75 bp. Our results are particularly relevant to markets with high-frequency trading or thin depth

Market structure, counterparty risk, and systemic risk

Networks modeling bilaterally-cleared and centrally-cleared derivatives markets are shown to yield economically different price impact, volatility and contagion after an initial bankruptcy. A large bankruptcy in bilateral markets may leave a counterparty unable to expectationally prevent bankruptcy (checkmate) or make counterparties push markets and profit from contagion (hunting). In distress, bilateral markets amplify systemic risk and volatility versus centralized markets and are more subject to crises with real effects: contagion, unemployment, reduced tax revenue, higher transactions costs, lower risk sharing, and reduced allocative efficiency. Pricing distress volatility may suggest when to transition to central clearing. The model suggests three metrics for the well-connected part of a market -- number of counterparties, average risk aversion, and standard deviation of total exposure -- may characterize its fragility

Approximating correlated defaults

Modeling defaults is critical to risk management as well as pricing debt portfolios and portfolio derivatives. In the recent financial crisis, multi-billion-dollar losses resulted from correlated defaults that were improperly modeled. This paper proposes statistical approximations which are more general than those used previously, follow from an intensity-based risk-factor model, and allow consistent parameter esti- mation. The parameters imply an approximating portfolio of independent, identical-credit loans and characterize both average credit quality and default-relative diversification (aka the “diversity score”). Unlike previous approaches, these metrics are derived jointly from theory. The approach addresses weaknesses in the typical diversity score-based methods by allowing for fatter tails as well as loans differing in size and credit quality. The approximations may also be used to model complete portfolio default and help set capital adequacy requirements. An example shows how to estimate the approximating portfolio

Approximating correlated defaults

Modeling defaults is critical to risk management as well as pricing debt portfolios and portfolio derivatives. In the recent financial crisis, multi-billion-dollar losses resulted from correlated defaults that were improperly modeled. This paper proposes statistical approximations which are more general than those used previously, follow from an intensity-based risk-factor model, and allow consistent parameter esti- mation. The parameters imply an approximating portfolio of independent, identical-credit loans and characterize both average credit quality and default-relative diversification (aka the “diversity score”). Unlike previous approaches, these metrics are derived jointly from theory. The approach addresses weaknesses in the typical diversity score-based methods by allowing for fatter tails as well as loans differing in size and credit quality. The approximations may also be used to model complete portfolio default and help set capital adequacy requirements. An example shows how to estimate the approximating portfolio

Approximating correlated defaults

Modeling defaults is critical to risk management as well as pricing debt portfolios and portfolio derivatives. In the recent financial crisis, multi-billion-dollar losses resulted from correlated defaults that were improperly modeled. This paper proposes statistical approximations which are more general than those used previously, follow from an intensity-based risk-factor model, and allow consistent parameter esti- mation. The parameters imply an approximating portfolio of independent, identical-credit loans and characterize both average credit quality and default-relative diversification (aka the “diversity score”). Unlike previous approaches, these metrics are derived jointly from theory. The approach addresses weaknesses in the typical diversity score-based methods by allowing for fatter tails as well as loans differing in size and credit quality. The approximations may also be used to model complete portfolio default and help set capital adequacy requirements. An example shows how to estimate the approximating portfolio

Performance metrics for algorithmic traders

Portfolio traders may split large orders into smaller orders scheduled over time to reduce price impact. Since handling many orders is cumbersome, these smaller orders are often traded in an automated (“algorithmic”) manner. We propose metrics using these orders to help measure various trading-related skills with low noise. Managers may use these metrics to assess how separate parts of the trading process contribute execution, market timing, and order scheduling skills versus luck. These metrics could save 4 basis points in cost per trade yielding a 15% reduction in expenses and saving $7.3 billion annually for US-domiciled equity mutual funds alone. The metrics also allow recovery of parameters for a price impact model with lasting and ephemeral effects. Some metrics may help evaluate external intermediaries, test for possible front-running, and indicate sloppy or overly passive trading

Market structure, counterparty risk, and systemic risk

Networks modeling bilaterally-cleared and centrally-cleared derivatives markets are shown to yield economically different price impact, volatility and contagion after an initial bankruptcy. A large bankruptcy in bilateral markets may leave a counterparty unable to expectationally prevent bankruptcy (checkmate) or make counterparties push markets and profit from contagion (hunting). In distress, bilateral markets amplify systemic risk and volatility versus centralized markets and are more subject to crises with real effects: contagion, unemployment, reduced tax revenue, higher transactions costs, lower risk sharing, and reduced allocative efficiency. Pricing distress volatility may suggest when to transition to central clearing. The model suggests three metrics for the well-connected part of a market -- number of counterparties, average risk aversion, and standard deviation of total exposure -- may characterize its fragility

Modeling Trade Direction

The problem of classifying trades as buys or sells is examined. I propose estimated quotes for midpoint and bid/ask tests and a modeling approach to classification. Prevailing quotes are estimated using flexible approximations to the distribution for delays of quotes relative to trade timestamps. Classification is done by a generalized linear model which includes improved versions of midpoint, tick, and bid/ask tests. The model also considers the relative strengths of these tests, can account for market microstructure peculiarities, and allows for autocorrelations and cross-correlations in trade direction. The correlation modeling corrects for pseudoreplication, yielding more accurate standard errors and fixed effect estimates. Further, the model estimates probabilities of correct classification. The model is compared to various trade classification methods using a sample of 2,836 domestic US stocks from an unexplored, recent, and readily-available dataset. Out of sample, modeled classifications are 1-2% more accurate overall than current methods; this improvement is consistent across dates, sectors, and locations relative to the inside quote. For Nasdaq and NYSE stocks, 1% and 1.3% of the improvement comes from using relative strengths of the various tests; 0.9% and 0.7% of the improvement, respectively, comes from using some form of estimated quotes. For AMEX stocks, a 0.4% improvement is attributed to using a lagged version of the bid/ask test. I also find indications of short- and ultra-short-term alpha

Performance metrics for algorithmic traders

Portfolio traders may split large orders into smaller orders scheduled over time to reduce price impact. Since handling many orders is cumbersome, these smaller orders are often traded in an automated (“algorithmic”) manner. We propose metrics using these orders to help measure various trading-related skills with low noise. Managers may use these metrics to assess how separate parts of the trading process contribute execution, market timing, and order scheduling skills versus luck. These metrics could save 4 basis points in cost per trade yielding a 15% reduction in expenses and saving $7.3 billion annually for US-domiciled equity mutual funds alone. The metrics also allow recovery of parameters for a price impact model with lasting and ephemeral effects. Some metrics may help evaluate external intermediaries, test for possible front-running, and indicate sloppy or overly passive trading