Time Variation in Liquidity: The Role of Market-Maker Inventories and Revenues

We show that market-maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity-supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market-level and specialist firm-level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.     

Determinants of bid and ask quotes and implications for the cost of trading

Financial transaction costs are time varying. This paper proposes a model that relates transaction cost to characteristics of order flow. We obtain qualitatively consistent model results for different stocks and across different time periods. We find that an unusual excess of buyers (sellers) relative to sellers (buyers) tends to increase the ask (bid) price. Hence, the ask and bid components of spread change asymmetrically about the efficient price. For a fixed order imbalance surprise these effects are muted when unanticipated total volume is high. Unexpected high volatility in the transaction price process tends to widen the spread symmetrically about the efficient price. Our findings are consistent with predications from market microstructure theory that the cost of market making should depend on both the risk of trading with better-informed traders and inventory risk. We also find that order flow surprises have a significant impact on the efficient price and can also explain a substantial amount of persistence in the volatility of the efficient price. This dependence does not violate the efficient market hypothesis since the surprises, by definition, are not predictable.     

Liquidity,Volume, and Order Imbalance Volatility

We examine the dynamics of liquidity using a comprehensive sample of U.S. stocks in the post-decimalization period. Motivated by a continuous-time inventory model, we compute a high-frequency measure of order imbalance volatility to proxy for the inventory risk faced by liquidity providers. We show that high-frequency order imbalance volatility is an important driver of liquidity and explains the often positive time-series relation between spread and volume for large stocks, which seems to run counter to most theoretical models. Furthermore, order imbalance volatility is priced in the cross-section of stock returns.     

Determinants of the crude oil futures curve: Inventory,consumption and volatility

Since 2008, the WTI oil futures curve has been positively sloped for extended periods. We test whether changes in inventory alone can explain this atypically long contango. To do this, we estimate monthly VARs of the CME WTI oil futures spread and OECD and U.S. inventory in line with standard theory, and add petroleum consumption and implied volatility to the vector of endogenous variables. When we model the futures spread as one continuous series, results confirm two-way causation between inventory and the futures curve, as predicted by the theory of storage. However when we separate negative and positive futures spreads we find that: two-way causation between the futures spread and U.S. inventory breaks down; shocks to OECD petroleum consumption cause more negative spreads and shocks to U.S. consumption cause more positive spreads in addition to inventory-driven changes; and increases in volatility directly raise positive spreads. These new causal channels have become significant since 2008 and can be related to higher inventory, inelastic supply of oil and uncertainty about global economic conditions.     

Estimation accuracy of high–low spread estimator

In this paper we analyze the estimation accuracy of high–low spread estimator. It is found that the performance of high–low spread estimator depends on the size of the true spread, the level of transaction frequency, and the degree of volatility. Analyzing the probability of measurement error, it is shown that the high–low spread estimators have better performance when the size of the spread is even wider, when the level of transaction frequency is even higher, or when the degree of volatility is relatively lower.     

Optimal high-frequency trading with limit and market orders

We propose a framework for studying optimal market-making policies in a limit order book (LOB). The bid–ask spread of the LOB is modeled by a tick-valued continuous-time Markov chain. We consider a small agent who continuously submits limit buy/sell orders at best bid/ask quotes, and may also set limit orders at best bid (resp. ask) plus (resp. minus) a tick for obtaining execution order priority, which is a crucial issue in high-frequency trading. The agent faces an execution risk since her limit orders are executed only when they meet counterpart market orders. She is also subject to inventory risk due to price volatility when holding the risky asset. The agent can then also choose to trade with market orders, and therefore obtain immediate execution, but at a less favorable price. The objective of the market maker is to maximize her expected utility from revenue over a short-term horizon by a trade-off between limit and market orders, while controlling her inventory position. This is formulated as a mixed regime switching regular/impulse control problem that we characterize in terms of a quasi-variational system by dynamic programming methods. Calibration procedures are derived for estimating the transition matrix and intensity parameters for the spread and for Cox processes modelling the execution of limit orders. We provide an explicit backward splitting scheme for solving the problem and show how it can be reduced to a system of simple equations involving only the inventory and spread variables. Several computational tests are performed both on simulated and real data, and illustrate the impact and profit when considering execution priority in limit orders and market orders.     

ADVERSE SELECTION,INVENTORY‐HOLDING COSTS,AND DEPTH

We examine whether specialist depth quotes are related to the adverse‐selection and inventory‐holding‐cost components of the spread. Consistent with theory that predicts an inverse relation between depths and informed trading risk, we find that depth quotes are strongly inversely related to the adverse‐selection component of the spread. We also find that depth quotes are inversely related to the inventory‐holding‐cost component, though this relation is weaker than the relation between depths and adverse selection. Our evidence suggests cross‐sectional variation in depths is driven primarily by variation in informed trading risk, as proxied for by the adverse‐selection component, rather than by inventory concerns. JEL classification: G10, G14     

Bid-ask spread dynamics in foreign exchange markets

The aim of this paper is to examine the short term dynamics of foreign exchange rate spreads. Using a vector autoregressive model (VAR) we show that most of the variation in the spread comes from the long run dependencies between past and future spreads rather than being caused by changes in inventory, adverse selection, cost of carry or order processing costs. We apply the Integrated Cumulative Sum of Squares (ICSS) algorithm of Inclan and Tiao (1994) to discover how often spread volatility changes. We find that spread volatility shifts are relatively uncommon and shifts in one currency spread tend not to spillover to other currency spreads.     

Consumption Volatility Risk

We show that time variation in macroeconomic uncertainty affects asset prices. Consumption volatility is a negatively priced source of risk for a wide variety of test portfolios. At the firm level, exposure to consumption volatility risk predicts future returns, generating a spread across quintile portfolios in excess of 7% annually. This premium is explained by cross‐sectional differences in the sensitivity of dividend volatility to consumption volatility. Stocks with volatile cash flows in uncertain aggregate times require higher expected returns.     

Changes in volatility of credit spread and market efficiency during rapid growth of credit-related securities

This paper investigates the changes in credit spread volatility during 1993–2001. We find that the credit spreads between junk-grade corporate bonds and Treasury bonds were significantly more volatile in the second half of this period when credit-related securities became popular. In contrast, investment-grade bonds exhibited no significant change in volatility. The junk bonds variance ratios changed from being less than one to greater than one. Using the GJR-Garch model, the conditional volatilities of junk bonds increased in the second half of the period and the mean reversion speeds slowed, suggesting a longer time for mean reversion to occur. Our analysis rules out treasury volatility, credit spread level, equity market return, T-bill rate, curvature of the Treasury curve, financial crisis, quantity of defaults and standard deviation of defaults as explanations for the increase in junk bond volatility. In contrast, volatility of equity returns provides a partial explanation of junk bond spread volatility in the later period.     

TRADE SIZES AND THEORIES OF THE BID-ASK SPREAD

Relations between trade-size characteristics and the bid-ask spread are developed to distinguish among major theories of the spread. These trade-size characteristics are determinants of the spread for NASDAQ/NMS stocks. They explain much of the cross-sectional variation in the spread commonly associated with volume, volatility, and share price. Evidence shows that order-processing costs are dominant relative to inventory effects for low-price, small-capitalization, and low-volume stocks, but that the opposite is true for high-price, large-capitalization, and high-volume stocks. Inventory effects are more important relative to asymmetric information costs when stock price or capitalization is lower.     

INTRADAY VARIATIONS IN TRADING ACTIVITY,PRICE VARIABILITY,AND THE BID-ASK SPREAD

In this paper intraday variations in trading activity and the bid-ask spread are examined. Intraday variations in volume and price variability demonstrate a U-shaped pattern as in previous studies. However, I find a U-shaped pattern for a measure of the spread component that is related to the degree of information asymmetry between the specialist and informed traders and an inverted U-shaped pattern for the other spread component related to inventory and order costs. Two alternative explanations are given.     

Relationship Between Analyst Forecast Properties and Equity Bid‐Ask Spreads and Depths Around Quarterly Earnings Announcements

Abstract:   We study the relationships between three variables which proxy for the ex‐ante level of information asymmetry – forecast dispersion, forecast revision volatility, and the level of analyst coverage, and equity bid‐ask spread and depth changes around quarterly earnings releases. Kim and Verrecchia (1994) suggest that earnings releases increase the level of information asymmetry and lower the level of liquidity in the security market. Using both an OLS regression framework and a simultaneous equations model, we examine whether equity bid‐ask spreads increase and depths decrease as the level of information asymmetry increases. Our results indicate that spreads are higher (relative to a non‐event period) around earnings announcements when information asymmetry is more pronounced; however, depths are lower only on the day following the announcement when there is greater information asymmetry. Relative spreads have a significant positive relation with both forecast dispersion and revision volatility and a significant negative relation with analyst coverage. Relative depths have a significant negative relation with forecast dispersion and a significant positive relation with analyst coverage. Our findings indicate that the equity specialist adjusts both spreads and depths when confronting informed traders around earnings releases and that these adjustments are more pronounced when the level of information asymmetry is greater.     

Money,interest rates,and risk

This paper analyzes the role of the risk in the form of the volatility of open market interest rates as a factor in the demand for money. We demonstrate, using an inventory theoretic model of money demand, that increases in interest rate volatility will increase the demand for money. We then present empirical evidence that the demand for money has been influenced by alterations in the volatility of open market rates using standard specifications of the demand for money.     

Determinants of periodic volatility of intraday exchange rates in the Taipei FX Market

Using the periodic GARCH (P-GARCH) model, this paper investigates the cause of the volatility seasonality of intraday Taiwan dollar/U.S. dollar (NTD/USD) exchange rate. We study the intraday volatility of NTD/USD exchange rate by considering impacts from public news arrivals, inventory risk and central bank interventions. The estimation results indicate that news arrivals at the market open may induce traders to adjust their inventory position and result in higher NTD/USD volatility on days with reported central bank interventions.     

A study on foreign exchange dealers' bid–ask spread quote behavior

Based on a questionnaire surveying dealers in the Taipei inter-bank foreign exchange market that was conducted in March 2001, I attempted to answer the question of who initiated the wider currency spread. It was found that the risk-averse dealers of small banks quoted wider spreads in order to conceal their inferior positions regarding information and inventory or to avoid market volatility risk. Some of the dealers of large multinational banks in major financial centers who normally quote conventional spreads were found to quote wider spreads in response to the request for quotations by small Taiwanese bank dealers who widened their spread quotes.     

Option market liquidity: Commonality and other characteristics

This study examines option market liquidity using Ivy DB's OptionMetrics data. We establish convincing evidence of commonality for various liquidity measures based on the bid–ask spread, volumes, and price impact. The commonality remains strong even after controlling for the underlying stock market's liquidity and other liquidity determinants such as volatility. Smaller firms and firms with a higher volatility exhibit stronger commonalities in option liquidity. Aside from commonality, we also uncover several other important properties of the option market's liquidity. First, information asymmetry plays a much more dominant role than inventory risk as a fundamental driving force of liquidity. Second, the market-wide option liquidity is closely linked to the underlying stock market's movements. Specifically, the options liquidity responds asymmetrically to upward and downward market movements, with calls reacting more in up markets and puts reacting more in down markets.     

Determinants of yield spread dynamics: Euro versus US dollar corporate bonds

This paper presents a systematic comparison between the determinants of euro and US dollar yield spread dynamics. The results show that US dollar yield spreads are significantly more affected by changes in the level and the slope of the default-free term structure and the stock market return and volatility. Surprisingly, euro yield spreads are strongly affected by the US (and not the euro) level and slope. This confirms the dominance of US interest rates in the corporate bond markets. Interestingly, I find that liquidity risk is higher for US dollar corporate bonds than euro corporate bonds. For both regions, the effect of changes in the bid-ask spread is mainly significant during periods of high liquidity risk. Finally, the results indicate that the credit cycle as measured by the region-specific default probability significantly increases US yield spreads. This is not the case for euro yield spreads.     

The Effects of Purchases of Mortgages and Securitization by Government Sponsored Enterprises on Mortgage Yield Spreads and Volatility

In this paper, we investigate the effects of GSE (government sponsored enterprise) activities on mortgage yield spreads and volatility. Using various regression procedures (i.e., vector error correction (VEC) and GARCH models) and controlling for default and prepayment risk, we find that securitizations and purchases of mortgages by GSEs reduce mortgage yield spreads and volatility. In particular, we find that the yield spread between conforming and 10-year constant maturity treasury (CMT) rates decreases by 8.0 bp per $1billion increase in the level of GSE securitizations. Similarly, if GSEs increase mortgage purchases, the yield spread decreases 10.5 bp per $1billion increase of purchases. In addition, we hypothesize and find that GSE activities have a spillover effect to the non-conforming mortgage market; via investor substitutions, GSE purchases and securitizations of conforming loans reduce non-conforming loan rates. Thus, the measured influence of GSE activities is biased downward when measured using the spread of non-conforming loans over conforming loan rates. We also find that purchases of mortgages by GSEs significantly reduce mortgage yield volatility. In sum, our findings show that GSE activities reduce and stabilize mortgage market rates.     

The Volatility of Bid‐Ask Spreads

This study tests whether the volatility of bid‐ask spreads is positively related to expected returns. After controlling for market‐risk factors, we find that the average risk‐adjusted excess return for stocks in the highest spread volatility quintile is around 50 basis points per month. In a variety of multivariate tests, we find robust evidence of a return premium associated with spread volatility that is both statistically significant and economically meaningful. Our results are robust to controls for a variety of stock characteristics, different tick‐size regimes, and other measures of liquidity volatility.