Narrow Results

The paper provides a continuous time model for order-driven stock market. The model allows to derive a nonlinear PDE as a modification of Black–Scholes equation for option pricing with a local volatility as a function of the stock price. The solution can be expanded in series in the parameter, which relates to the size of option market. The first-order correction for the option price increases the price of a European call. The second-order correction for volatility allows to describe the "volatility smile".

We propose a model of a financial market with multiple assets that takes into account the impact of a large institutional investor rebalancing its positions so as to maintain a fixed allocation in each asset. We show that feedback effects can lead to significant excess realized correlation between asset returns and modify the principal component structure of the (realized) correlation matrix of returns. Our study naturally links, in a quantitative manner, the properties of the realized correlation matrix — correlation between assets, eigenvectors and eigenvalues — to the sizes and trading volumes of large institutional investors. In particular, we show that even starting with uncorrelated “fundamentals”, fund rebalancing endogenously generates a correlation matrix of returns with a first eigenvector with positive components, which can be associated to the market, as observed empirically. Finally, we show that feedback effects flatten the differences between the expected returns of assets and tend to align them with the returns of the institutional investor’s portfolio, making this benchmark fund more difficult to beat, not because of its strategy but precisely because of its size and market impact.

We propose a model for a manager of a hedge fund with a liquidity constraint, where he is seeking to optimize his utility of wealth, with one and multiple period horizons. By using stochastic control techniques, we state the corresponding multi-dimensional Hamilton–Jacobi–Bellman partial differential equation and we use a robust numerical approximation to obtain its unique viscosity solution. We examine the effects of the liquidity constraint on managerial trading decisions and optimal allocation, finding that the manager behaves in a less risky manner. We also calculate the cost of being at sub-optimal positions as the difference in the certainty equivalent payoff for the manager. Moreover, we compare the values of a benchmark hedge fund with another one having a risky asset with a higher rate of return but less liquidity, finding that higher rate of return with a liquidity constraint does not always lead to greater return.

This paper investigates the relationship between volatility and liquidity on the German electricity futures market based on high-frequency intraday prices. We estimate volatility by the time-weighted realized variance acknowledging that empirical intraday prices are not equally spaced in time. Empirical evidence suggests that volatility of electricity futures decreases as time approaches maturity, while coincidently liquidity increases. Established continuous-time stochastic models for electricity futures prices involve a growing volatility function in time and are thus not able to capture our empirical findings a priori. In Monte Carlo simulations, we demonstrate that incorporating increasing liquidity into the established models is key to model the decreasing volatility evolution.

In this paper, we propose a framework for credit and debit valuation adjustments (CVA and DVA, respectively) for options and option portfolios which is based on conic finance, that is, where the positions are valued at their bid or ask prices depending on whether they are assets or liabilities. This can be achieved by transforming the pricing measure via appropriate distortion functions, depending on (at least) one parameter. We apply our methodology, which is based on the Wang transform, to portfolios of European commodity futures options, and we show that both CVA and DVA are significantly impacted by bid-ask spreads, when compared to their traditional risk-neutral counterparts. In particular, we show that DVA decreases when computed under conic finance settings, which is in line with the regulatory efforts to rein in DVA gains for financial institutions resulting from their own credit quality deterioration. Finally, we investigate the robustness of our approach with respect to the calibrated parameters, and we show that the calibrated distortion parameter is an excellent explanatory variable for the observed bid-ask spreads.

Risk-neutral default probabilities can be implied from credit default swap (CDS) market quotes. In practice, mid-CDS quotes are used as inputs, as their risk-neutral counterparts are not observable. We show how to imply risk-neutral default probabilities from bid and ask quotes directly by means of formulating the CDS calibration problem to bid and ask market quotes within the conic finance framework. Assuming the risk-neutral distribution of the default time to be driven by a Poisson process we prove, under mild liquidity-related assumptions, that the calibration problem admits a unique solution that also allows to jointly calculate the implied liquidity of the market.

We propose an agent-based computational model for a financial system consisting of a network of banks with interconnected balance sheets comprising fixed assets (e.g. loans to agents outside the network), liquid assets (e.g. cash or central bank reserves), general collateral (e.g. government debt), unsecured interbank loans and reverse-repos to other banks as assets, as well as deposits, unsecured interbank loans and repos from other banks as liabilities. Importantly, we allow banks to use reverse-repo assets as collateral for obtaining repo loans from other banks, that is to say, rehypothecation. Banks need to satisfy liquidity, collateral, and solvency constraints. If the first two constraints are violated because of internal or external shocks, solvent banks attempt to restore them by rebalancing their assets, which might lead to the propagation of the shock because of fire-sale effects (if fixed assets are sold) or liquidity hoarding (if secured or unsecured loans are recalled). Insolvent banks, as well as banks that failed to restore the liquidity and collateral constraints after rebalancing, are removed from the network using a resolution algorithm that includes a netting step (i.e. removal of closed cycles of liabilities) and a novation step (i.e. redistribution of repo assets and liabilities to remaining banks). We show analytically that this proposed resolution algorithm has several desirable properties, most importantly the order-independence of the novation step, and we investigate the stability properties of the network through a series of numerical experiments.

Bank loan sales activities in the U.S. increased dramatically in the 1980s. We develop a dynamic control model that integrates existing rationales for loan sales. Our model explains several recently documented empirical facts concerning both bank loan sales and purchases in a consistent fashion and yields additional testable hypotheses.

The purpose of this chapter is to identify the attributes affecting the cost, revenue, and profit efficiency of life insurance companies in India from 2013–2014 to 2018–2019. A two-phase analysis is applied in the study. In the first phase, the cost, revenue, and profit efficiency scores of all the life insurance companies are calculated using the technique of data envelopment analysis. In the second phase, a panel tobit regression model is run to estimate the antecedents of efficiency. The results of the study emphasize that capital adequacy, asset quality, reinsurance and actuarial issues, management soundness, and liquidity have a positive relationship with cost, revenue, and profit efficiency. However, “earning and profitability” has a negative impact on all the efficiency scores, depicting that Indian life insurance companies are not getting much return from their investments, which is their major source of revenue. Low revenues do not seem to be sufficient to cover the cost of insurance and, consequently, generate low profits. In order to improve efficiency, insurers should focus on balancing the input–output mix, taking into consideration their prices. Also, modern virtual platforms should be adopted, which can lead to cost savings and higher profitability.

This chapter provides readers with the skills and insights to enable a fuller understanding of a public company’s underlying financial statements. Financial statement analysis is a form of fundamental analysis that identifies and analyzes the key financial information relevant to a company, for the purpose of determining the company’s intrinsic value. This chapter concentrates on the calculation and application of the principal financial ratios for profitability, efficiency, liquidity, solvency, and investment potential. There is an effort to make the content accessible to readers on a practical level, by applying the various ratios to the real financial statements of Johnson & Johnson. While the chapter outlines in detail the quantitative calculations required to undertake financial statement analysis, there is a firm emphasis on the importance of context in properly applying these techniques. The establishment of trends, the comparison of data to benchmarks, and the consideration of strategic factors all form part of this holistic contextual analysis of financial statement information.

We study three widely used liquidity measures and find that they all carry significant premiums beyond the size, book-to-market, and momentum effects. Although liquidity as a risk factor bears a significant return premium, it is better characterized by a characteristic-based model. Further analysis shows that (1) although the premium persists for up to five years following formation, it diminishes over time and becomes insignificant in the post-1960 period; (2) the premium is larger for stocks with higher idiosyncratic risk. Thus, the empirical results provide some evidence that supports the mispricing argument.

This study examines the implications of a bank’s activity mix and funding strategy for its liquidity management as defined in the Basel III accords or when they create more liquidity as measured by Berger and Bouwman (2009). Using an international sample of 624 banks in 65 countries, we find that, at low levels of noninterest income and nondeposit funding, there could be some risk diversification benefits in increasing these shares; however, at higher levels of noninterest income and nondeposit funding shares, additional increases result in higher illiquidity. Finally, better accounting disclosure will improve the effect on bank liquidity by both bank’s activity mix and funding strategy, but at the same time, they worsen the bank liquidity by stricter capital stringency and more market power.

The credit default swap (CDS) market has experienced tremendous changes over the last two decades. This chapter reviews recent studies on the CDS contract and the CDS market, highlighting recent developments in CDS contracts and market structures. Characteristics of CDS contracts make them outstanding from other derivatives products, and the rapid development of CDS markets shows the popular use and importance of CDS contracts in general. Policies and regulations on CDSs have brought huge changes to the market and the availability of publicly reported data. The relationship between firms and CDS has long been a focus among academics. Moreover, CDSs are also commonly used among mutual funds, hedge funds, and banks, affecting the trading activities of various financial intermediaries. While the sovereign CDS and index CDS are shown to be more and more significant in financial markets, other CDS products, including CDS options and tranches, also experience changes. We summarize the evolution in CDS markets and the structure of CDS studies, aiming to gain a better understanding of CDS markets and provide insights for future studies on the CDS market.

We construct liquidity and earnings-based factors and combine with the Market to describe stock returns. Liquidity and Liquidity Growth are significant factors across markets. Intercept tests show that the IELM (International Earnings, Liquidity, and Market) model fits the cross section in various country groupings. As previous research showed, a Liquidity Growth factor subsumes momentum in the U.S., and we test this across international markets. From 2001 through 2019, the momentum factor has a high mean and is significant in Europe and in the Asia-Pacific, except Japan. For this time period, however, momentum is not significant in North American and Japan. While the IELM model reduces the momentum intercept in North America, both IELM and Fama and French (2017) have trouble explaining momentum in Europe and Asia where momentum is pervasive.