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A call on art investments
(2010)
The art market has seen boom and bust during the last years and, despite the downturn, has received more attention from investors given the low interest environment following the financial crisis. However, participation has been reserved for a few investors and the hedging of exposures remains dificult. This paper proposes to overcome these problems by introducing a call option on an art index, derived from one of the most comprehensive data sets of art market transactions. The option allows investors to optimize their exposure to art. For pricing purposes, non-tradability of the art index is acknowledged and option prices are derived in an equilibrium setting as well as by replication arguments. In the former, option prices depend on the attractiveness of gaining exposure to a previously non-traded risk. This setting further overcomes the problem of art market exposures being dificult to hedge. Results in the replication case are primarily driven by the ability to reduce residual hedging risk. Even if this is not entirely possible, the replication approach serves as pricing benchmark for investors who are significantly exposed to art and try to hedge their art exposure by selling a derivative. JEL Classification: G11, G13, Z11
The well-known proof of termination of reduction in simply typed calculi is adapted to a monomorphically typed lambda-calculus with case and constructors and recursive data types. The proof differs at several places from the standard proof. Perhaps it is useful and can be extended also to more complex calculi.
This paper proposes the Shannon entropy as an appropriate one-dimensional measure of behavioural trading patterns in financial markets. The concept is applied to the illustrative example of algorithmic vs. non-algorithmic trading and empirical data from Deutsche Börse's electronic cash equity trading system, Xetra. The results reveal pronounced differences between algorithmic and non-algorithmic traders. In particular, trading patterns of algorithmic traders exhibit a medium degree of regularity while non-algorithmic trading tends towards either very regular or very irregular trading patterns. JEL Classification: C40, D0, G14, G15, G20
The objective of this study is to determine whether specific industries across countries or within countries are more likely to reach a stage of profitability and make a successful exit. In particular, we assess whether firms in certain industries are more prone to exit via IPO, be acquired, or exit through a leveraged buy-out. We are also interested in analyzing whether substantial differences across industries and countries arise when looking separately at the success’ rate of firms which have received venture funding at the early seed and start-up stages, vis-à-vis firms that received funding at later stages. Our results suggest that, inasmuch as some of the differences in performance can be explained by country-specific factors, there are also important idiosyncratic differences across industries: In particular, firms in the biotech and the medical / health / life science sectors tend to be significantly more likely to have a successful exit via IPO, while firms in the computer industry and communications and media are more prone to exit via merger or acquisition. Key differences across industries also emerge when considering infant versus mature firms, and their preferred exit. JEL Classification: G24, G3 Keywords:
This paper analyzes the impact of blockownership dispersion on firm value. Blockholdings by multiple blockholders is a widespread phenomenon in the U.S. market. It is not clear, however, whether dispersion among blockholder is preferable to having a more concentrated ownership structure. To test for the direction of the effect, we use a large dataset of U.S. firms that combines blockholder information, shareholder rights information, debt ratings, accounting information, and financial markets information. We find that a large fraction of aggregated block ownership negatively affects Tobin’s Q. The negative impact is larger if blockowners are more dispersed, suggesting that a concentrated ownership structure is to be preferred on average. Results are robust to controlling for blockholder type as well as proxies for shareholder rights. Our empirical findings are also confirmed if we study the impact of ownership dispersion on firm debt ratings rather than Tobin’s Q. JEL Classification: G3, G32
Over the past few decades, changes in market conditions such as globalisation and deregulation of financial markets as well as product innovation and technical advancements have induced financial institutions to expand their business activities beyond their traditional boundaries and to engage in cross-sectoral operations. As combining different sectoral businesses offers opportunities for operational synergies and diversification benefits, financial groups comprising banks, insurance undertakings and/or investment firms, usually referred to as financial conglomerates, have rapidly emerged, providing a wide range of services and products in distinct financial sectors and oftentimes in different geographic locations. In the European Union (EU), financial conglomerates have become part of the biggest and most active financial market participants in recent years. Financial conglomerates generally pose new problems for financial authorities as they can raise new risks and exacerbate existing ones. In particular, their cross-sectoral business activities can involve prudentially substantial risks such as the risk of regulatory arbitrage and contagion risk arising from intra-group transactions. Moreover, the generally large size of financial conglomerates as well as the high complexity and interconnectedness of their corporate structures and risk exposures can entail substantial systemic risk and can therefore threaten the stability of the financial system as a whole. Until a few years ago, there was no supervisory framework in place which addressed a financial conglomerate in its entirety as a group. Instead, each group entity within a financial conglomerate was subject to the supervisory rules of its pertinent sector only. Such silo supervisory approach had the drawback of not taking account of risks which arise or aggravate at the group level. It also failed to consider how the risks from different business lines within the group interrelate with each other and affect the group as a whole. In order to address this lack of group-wide prudential supervision of financial conglomerates, the European legislator adopted the Financial Conglomerates Directive 2002/87/EC8 (‘FCD’) on 16 December 2002. The FCD was transposed into national law in the member states of the EU (‘Member States’) by 11 August 2004 for application to financial years beginning on 1 January 2005 and after. The FCD primarily aims at supplementing the existing sectoral directives to address the additional risks of concentration, contagion and complexity presented by financial conglomerates. It therefore provides for a supervisory framework which is applicable in addition to the sectoral supervision. Most importantly, the FCD has introduced additional capital requirements at the conglomerate level so as to prevent the multiple use of the same capital by different group entities. This paper seeks to examine to what extent the FCD provides for an adequate capital regulation of financial conglomerates in the EU while taking into account the underlying sectoral capital requirements and the inherent risks associated with financial conglomerates. In Part 1, the definition and the basic corporate models of financial conglomerates will be presented (I), followed by an illustration of the core motives behind the phenomenon of financial conglomeration (II) and an overview of the development of the supervision over financial conglomerates in the EU (III). Part 2 begins with a brief elaboration on the role of regulatory capital (I) and gives a general overview of the EU capital requirements applicable to banks and insurance undertakings respectively. A delineation of the commonalities and differences of the banking and the insurance capital requirements will be provided (II). It continues to further examine the need for a group-wide capital regulation of financial conglomerates and analyses the adequacy of the FCD capital requirements. In this context, the technical advice rendered by the Joint Committee on Financial Conglomerates (JCFC) as well as the currently ongoing legislative reforms at the EU level will be discussed (III). The paper finally closes with a conclusion and an outlook on remaining open issues (IV).
Capturing the zero: a new class of zero-augmented distributions and multiplicative error processes
(2010)
We propose a novel approach to model serially dependent positive-valued variables which realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial time series observed on high frequencies, such as cumulated trading volumes or the time between potentially simultaneously occurring market events. We introduce a flexible pointmass mixture distribution and develop a semiparametric specification test explicitly tailored for such distributions. Moreover, we propose a new type of multiplicative error model (MEM) based on a zero-augmented distribution, which incorporates an autoregressive binary choice component and thus captures the (potentially different) dynamics of both zero occurrences and of strictly positive realizations. Applying the proposed model to high-frequency cumulated trading volumes of liquid NYSE stocks, we show that the model captures both the dynamic and distribution properties of the data very well and is able to correctly predict future distributions. Keywords: High-frequency Data , Point-mass Mixture , Multiplicative Error Model , Excess Zeros , Semiparametric Specification Test , Market Microstructure JEL Classification: C22, C25, C14, C16, C51
We test whether asymmetric preferences for losses versus gains as in Ang, Chen, and Xing (2006) also affect the pricing of cash flow versus discount rate news as in Campbell and Vuolteenaho (2004). We construct a new four-fold beta decomposition, distinguishing cash flow and discount rate betas in up and down markets. Using CRSP data over 1963–2008, we find that the downside cash flow beta and downside discount rate beta carry the largest premia. We subject our result to an extensive number of robustness checks. Overall, downside cash flow risk is priced most consistently across different samples, periods, and return decomposition methods, and is the only component of beta that has significant out-of-sample predictive ability. The downside cash flow risk premium is mainly attributable to small stocks. The risk premium for large stocks appears much more driven by a compensation for symmetric, cash flow related risk. Finally, we multiply our premia estimates by average betas to compute the contribution of the different risk components to realized average returns. We find that up and down discount rate components dominate the contribution to average returns of downside cash flow risk. Keywords: Asset Pricing, Beta, Downside Risk, Upside Risk, Cash Flow Risk, Discount Rate Risk JEL Classification: G11, G12, G14
The recent financial crisis has highlighted the limits of the “originate to distribute” model of banking, but its nexus with the macroeconomy and monetary policy remains unexplored. I build a DSGE model with banks (along the lines of Holmström and Tirole [28] and Parlour and Plantin [39] and examine its properties with and without active secondary markets for credit risk transfer. The possibility of transferring credit reduces the impact of liquidity shocks on bank balance sheets, but also reduces the bank incentive to monitor. As a result, secondary markets allow to release bank capital and exacerbate the effect of productivity and other macroeconomic shocks on output and inflation. By offering a possibility of capital recycling and by reducing bank monitoring, secondary credit markets in general equilibrium allow banks to take on more risk. Keywords: Credit Risk Transfer , Dual Moral Hazard , Monetary Policy , Liquidity , Welfare JEL Classification: E3, E5, G3 First Draft: December 2009, This Draft: September 2010