Does Algorithmic Trading Improve Liquidity? HENDERSHOTT, TERRENCE; JONES, CHARLES M.; MENKVELD, ALBERT J.
The Journal of finance (New York),
February 2011, Letnik:
66, Številka:
1
Journal Article
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Algorithmic trading (AT) has increased sharply over the past decade. Does it improve market quality, and should it be encouraged? We provide the first analysis of this question. The New York Stock ...Exchange automated quote dissemination in 2003, and we use this change in market structure that increases AT as an exogenous instrument to measure the causal effect of AT on liquidity. For large stocks in particular, AT narrows spreads, reduces adverse selection, and reduces trade-related price discovery. The findings indicate that AT improves liquidity and enhances the informativeness of quotes.
Discount-rate variation is the central organizing question of current asset-pricing research. I survey facts, theories, and applications. Previously, we thought returns were unpredictable, with ...variation in price-dividend ratios due to variation in expected cashflows. Now it seems all price-dividend variation corresponds to discount-rate variation. We also thought that the cross-section of expected returns came from the CAPM. Now we have a zoo of new factors. I categorize discount-rate theories based on central ingredients and data sources. Incorporating discount-rate variation affects finance applications, including portfolio theory, accounting, cost of capital, capital structure, compensation, and macroeconomics.
I describe asset price dynamics caused by the slow movement of investment capital to trading opportunities. The pattern of price responses to supply or demand shocks typically involves a sharp ...reaction to the shock and a subsequent and more extended reversal. The amplitude of the immediate price impact and the pattern of the subsequent recovery can reflect institutional impediments to immediate trade, such as search costs for trading counterparties or time to raise capital by intermediaries. I discuss special impediments to capital formation during the recent financial crisis that caused asset price distortions, which subsided afterward. After presenting examples of price reactions to supply shocks in normal market settings, I offer a simple illustrative model of price dynamics associated with slow-moving capital due to the presence of inattentive investors.
Tails, Fears, and Risk Premia BOLLERSLEV, TIM; TODOROV, VIKTOR
The Journal of finance (New York),
December 2011, Letnik:
66, Številka:
6
Journal Article
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We show that the compensation for rare events accounts for a large fraction of the average equity and variance risk premia. Exploiting the special structure of the jump tails and the pricing thereof, ...we identify and estimate a new Investor Fears index. The index reveals large time-varying compensation for fears of disasters. Our empirical investigations involve new extreme value theory approximations and high-frequency intraday data for estimating the expected jump tails under the statistical probability measure, and short maturity out-of-the-money options and new model-free implied variation measures for estimating the corresponding risk-neutral expectations.
In a model with heterogeneous-risk-aversion agents facing margin constraints, we show how securities' required returns increase in both their betas and their margin requirements. Negative shocks to ...fundamentals make margin constraints bind, lowering risk-free rates and raising Sharpe ratios of risky securities, especially for high-margin securities. Such a funding-liquidity crisis gives rise to "bases," that is, price gaps between securities with identical cash-flows but different margins. In the time series, bases depend on the shadow cost of capital, which can be captured through the interest-rate spread between collateralized and uncollateralized loans and, in the cross-section, they depend on relative margins. We test the model empirically using the credit default swap—bond bases and other deviations from the Law of One Price, and use it to evaluate central banks' lending facilities.
We derive an equilibrium asset pricing model incorporating liquidity risk, derivatives, and short-selling due to hedging of nontraded risk. We show that illiquid assets can have lower expected ...returns if the short-sellers have more wealth, lower risk aversion, or shorter horizon. The pricing of liquidity risk is different for derivatives than for positive-net-supply assets, and depends on investors' net nontraded risk exposure. We estimate this model for the credit default swap market. We find strong evidence for an expected liquidity premium earned by the credit protection seller. The effect of liquidity risk is significant but economically small.
Energy systems around the world have started going through rapid and profound transformations. Electric vehicles are breaking into the mainstream, and millions of wind and solar farms are replacing ...fossil fuel power plants, but both developments create fundamental challenges for the security of electricity supply. Energy storage could resolve these and drive deep decarbonization at lower cost. As a result, the storage industry is projected to grow to hundreds of times its current size in the coming decades. Businesses, policy-makers, and academics need to assess the economic case for energy storage and the future roles it will play. This is complicated by rapidly falling investment costs, the wide range of technologies, and the vast array of use cases for energy storage. The authors introduce a comprehensive toolkit required for assessing how the benefits of energy storage stack up against its costs. They give sharp insights on future prices, lifetime costs, technology competitiveness, profitability, and market size based on this toolkit. These are complemented by clear and transparent explanations of the underlying datasets and methods. Worked examples using an easy-to-access online tool further empower readers to conduct their own assessments with custom data. Various frequently asked questions provide real-world examples and bust some of the most prevalent myths on energy storage. This book is aimed at both decision-makers that require key insights into the energy storage business and practitioners that want to perform their own analyses on the current and future cost and value of energy storage.
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