MERO Gulten

Chapter 1: How Should Investment Fund Behavior and Performance React to Fund Size? The Role of Liquidity Frictions.A number of empirical studies have investigated how mutual funds do react to incoming financial resources. As long as liquidity constraints are narrow, fund managers tend to upscale already existing positions without looking for new investment opportunities. Concomitantly, performance of funds decreases which is explained by the expansion of size-driven liquidity costs. We put forward a model of asset allocation that accounts for market liquidity frictions and recovers the inverse relation between fund size and fund performance. The model prescribes how fund portfolio managers should react as financial resources enter the fund. We estimate the model on S&P 600 stock data and investigate optimal allocation behavior under fund size increase. We obtain that to confine the negative effect of liquidity frictions on performance, portfolio diversification should be increased under fund size increase. In particular, once a certain fund size is attained, fund managers should incorporate new investments which enhances portfolio diversity and reduces liquidity-driven performance erosion.Chapter 2: The Role of Financial Market Liquidity on Investment Performance in the US Energy Equity.A large portion of financial investment in the energy sector goes through the stock market channel. Energy equity funds often ground their decisions on ex ante marked-to-market performance ranking of alternative opportunities. We put forward a liquidity-adjusted Sharpe ratio and show that liquidity frictions do affect investment performance ranking. Empirical analysis of the NYSE energy equity segment shows that imperfect liquidity may lead to rank reversal of investment opportunities compared to the standard marked-to-market assessment. This phenomenon is shown to be increasingly relevant with the share listed companies have in the renewable energy business. Market liquidity frictions may thus blur regulators’ policies aimed at attracting capital for investment in new energy areas. In particular, renewable energy policy makers should consider the ability of financial market regulators to improve stock market liquidity as a means to trigger the effectiveness of their policy.Chapter 3 Monetary Measurement of Risk: A Critical Overview.Risk assessment must cope with ever more stringent regulatory requirements in a context of rapidly evolving financial practices and new emerging risks. We put forward a critical overview of the theory of monetary risk measures. Relying on economical arguments, we suggest that properties defining coherent risk measures might not be desirable and rehabilitate Value-at-Risk. We present convex measures of risk, introduced by the literature to overcome some drawbacks of coherent risk measures, and especially to account for market liquidity risk. Finally, an economic capital allocation experiment is conducted which compares outputs obtained using alternatively Expected Shortfall (coherent) or the entropic risk measure (convex).

This thesis focuses on the new opportunities offered by the growth of Exchange-Traded Funds (ETFs). This research explores three of their impacts on collective bond management.The first step of this study observes the effects on underlyings of inclusion or exclusion in an ETF. Because of their hybrid structure, ETFs can affect the characteristics of underlyings and increase the proportion of uninformed investors. I exploit bond downgrades to establish a relationship between ETF ownership and the liquidity or price of the underlyings. The results show that the effects of ETF inclusion are long-lasting and partially persist after ETF exit. The next chapter proposes a methodology using the information contained in ETFs as a stress measure. Many investors take advantage of the speed of trading of ETFs to adjust their exposures very quickly. The proposed indicator provides a single methodology for all asset classes, which facilitates the analysis of its propagation. Finally, the last chapter deals with the integration of portfolio insurance strategies in an open-ended fund. The flexibility of ETFs across multiple asset classes creates new opportunities for portfolio insurance. This chapter proposes a model that tailors exposure to a risky asset based on downside and upside protections. The methodology allows for protection of all investors regardless of the subscription/redemption period. regardless of the subscription/redemption period.

The mixture of distribution hypothesis (MDH) model offers an appealing explanation for the positive relation between trading volume and volatility of returns. In this specification, the information flows constitute the only mixing variable responsible for all changes. However, this single static latent mixing variable cannot account for the observed short-run dynamics of volume and volatility. In this paper, we propose a dynamic extension of the MDH that specifies the impact of information arrival on market characteristics in the context of liquidity frictions. We distinguish between short-term and long-term liquidity frictions. Our results highlight the economic value and statistical accuracy of our specification. First, based on some goodness of fit tests, we show that our dynamic two-latent factor model outperforms all competing specifications. Second, the information flows latent variable can be used to propose a new momentum strategy. We show that this signal improves once we allow for a second signal – the liquidity frictions latent variable – as the momentum strategies based on our model present better performance than the strategies based on competing models.

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In a new environment where liquidity providers as well as liquidity consumers act strategically, understanding how liquidity flows and dries-up is key. We propose a model that specifies the impact of information arrival on market characteristics, in the context of liquidity frictions. We distinguish short-lasting liquidity frictions, which impact intraday prices, from long-lasting liquidity frictions, when information is not fully incorporated into prices within the day. We link the first frictions to the strategic behavior of intraday liquidity providers and the second to the strategic behavior of liquidity consumers, i.e. long-term investors who split up their orders not to be detected. Our results show that amongst 61% of the stocks facing liquidity problems, 57% of them point up liquidity providers as the sole strategic market investor. Another 27% feature long-term investors as the single strategic player, while both liquidity providers and liquidity consumers act strategically in the remaining 16%. This means that 43% of these stocks are actually facing a slow-down in the information propagation in prices, which thus results in a significant decrease of (daily) price efficiency due to long-term investors’ strategic behavior.

Based on the concept that the presence of liquidity frictions can increase the daily traded volume, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Tauchen and Pitts (1983) to measure the liquidity portion of volume. Our approach relies on a structural definition of liquidity frictions arising from the theoretical framework of Grossman and Miller (1988), which explains how liquidity shocks affect the way in which information is incorporated into daily trading characteristics. In addition, we propose an econometric setup exploiting the volatility–volume relationship to filter the liquidity portion of volume and infer the presence of liquidity frictions using daily data. Finally, based on FTSE 100 stocks, we show that the extended MDH model proposed here outperforms that of Andersen (1996) and that the liquidity frictions are priced in the cross-section of stock returns.

In this paper, we use a cross-sectional approach to get a deeper comprehension of the common risk profile of stock returns. Instead of employing static and ad hoc factor selection procedures as in Fama and French (1993), we use asymptotic developments of Bai and Ng (2002, 2006) to select the relevant factors. We thus reconcile two methodologies: a statistical one and the other, founded on observed factors. We apply our approach to French and US stock markets over the period 1999 to 2008 and test the performance of several traditionally observed factors, such as credit spread and firm-characteristic variables of Fama and French (1993) and Carhart (1997). Our results uncover strong time and country dependencies of stock risk profile.

Given the empirical failure of observable risk factors in explaining financial returns, the aim of this thesis is to use latent factor models and recent econometric developments to improve the understanding of asset risk. First, we describe latent factor models applied to finance and the main estimation methods. We also present how the use of financial and econometric theories allows us to link statistical factors with economic and financial variables in order to facilitate their interpretation. Second, we use latent factor models in cross-sections to estimate and interpret the risk profile of hedge funds and stocks. The methodology is consistent with the statistical properties of large samples and the dynamic nature of systematic risk. In a third step, we model a market where prices and volumes are sensitive to intraday liquidity shocks. We propose a structural mixture model of latent two-factor distributions to capture the impact of information shocks and liquidity frictions. This model allows us to construct a static liquidity measure specific to each stock. Second, we extend our structural model to account for the dynamic properties of liquidity risk. In particular, we distinguish two liquidity problems: liquidity frictions occurring at an intraday frequency and illiquidity events that persistently deteriorate market quality. Finally, we use statistical time series modeling to construct dynamic liquidity measures.