One of the keys for restaurant managers in conducting a proper assessment of their business opportunities is through understanding the level of risk these opportunities bear. This can be achieved by analyzing the causal relationships between external environmental forces and internal capabilities of the firm, and then make a strategic choice in what opportunities to invest. The purpose of this study was to investigate the concept of risk and its underlying dimensions that influence the restaurant industry's cash flows and stock returns. This study proposed a contemporary framework that enables restaurant industry executives to develop a better understanding of the risk factors (macroeconomic and industry) that influence their firms' cash flows and stock returns. The primary unit of analysis was at industry (portfolio) level. In addition, as a second step, three restaurant firms were selected to demonstrate the practical application of the model. Exploratory factor analysis indicated that the restaurant industry risk is represented by three dimensions: "Output," "PPI Meats," and "IP Restaurants." The macroeconomic risk construct was represented by the five variables of Arbitrage Pricing Theory of Chen et al. (1986). Time series-analysis regression of the portfolio of 75 restaurant firms, for the 1993-2004 period, revealed that macroeconomic variables explained a significant portion of restaurant stock returns. On the other hand, both macroeconomic and industry models explained a significant level of variation in operating cash flows. The addition of September 11 "dummy" variable improved the explained variation in stock returns for both equations (macroeconomic and industry). At a firm level, the industry model accounted for a significant variation in internal value drivers (operating cash flows, food cost, and labor cost) for all three restaurant companies. The industry risk model survived after controlling for the effect of macroeconomic variables on operating cash flows. The results indicate that the industry model provides a parsimonious solution in estimating variation in operating cash flows by capturing macroeconomic effects. / Ph. D.

<p>The three essays in this dissertation explore the role of fluctuations in aggregate volatility and global temperature as sources of systemic risk. </p><p>The first essay proposes a production-based asset pricing model and provides empirical evidence suggesting that compensation for volatility risk is closely related to an unexplored characteristic of a firm, namely, its reliance on skilled labor. I propose a model in which aggregate growth has time-varying volatility, and linear adjustment costs in labor increase with the skill of a worker. The model predicts that expected returns increase with a firm's reliance on skilled labor, as well as compensation for fluctuations in aggregate uncertainty. Consequently, a rise in aggregate uncertainty predicts an increase in expected returns as well as in cautiousness in hiring and firing. This impact is larger for firms with a high share of skilled workers because their labor is more costly to adjust. I empirically test the implications of the model using occupational estimates to construct a measure of a firm's reliance on skilled labor, and find a positive and statistically significant cross-sectional relation between the reliance on skilled labor and expected returns. Empirical estimates also show that an increase in aggregate uncertainty leads to a rise in expected returns, and this impact is larger for firms which rely heavily on skilled labor; thereby, a firm's exposure to aggregate volatility is positively related to its reliance on skilled labor.</p><p>In the second and third essay, co-authored with Ravi Bansal, we explore the impact of global temperature on financial markets and the macroeconomy. In tho second essay we explore if temperature is an aggregate risk factor that adversely affects economic growth. First, using data on global capital markets we find that the risk-exposure of these returns to temperature shocks, i.e., their temperature beta, is a highly significant variable in accounting for cross-sectional differences in expected returns. Second, using a panel of countries we show that GDP growth is negatively related to global temperature, suggesting that temperature can be a source of aggregate risk. To interpret the empirical evidence, we present a quantitative consumption-based long-run risks model that quantitatively accounts for the observed cross-sectional differences in temperature betas, the compensation for temperature risk, and the connection between aggregate growth and temperature risks. </p><p>The last essay proposes a general equilibrium model that simultaneously models the world economy and global climate to understand the impact of climate change on the economy. We use this model to evaluate the role of temperature in determining asset prices, and to compute utility-based welfare costs as well as dollar costs of insuring against temperature fluctuations. We find that the temperature related utility-costs are about 0.78% of consumption, and the total dollar costs of completely insuring against temperature variation are 2.46% of world GDP. If we allow for temperature-triggered natural disasters to impact growth, insuring against temperature variation raise to 5.47% of world GDP.</p> / Dissertation

Asset pricing implications of a DSGE model with recursive preferences and nominal rigidities. I study jointly macroeconomic dynamics and asset prices implied by a production economy featuring nominal price rigidities and Epstein-Zin (1989) preferences. Using a reasonable calibration, the macroeconomic DSGE model is consistent with a number of stylized facts observed in financial markets like the equity premium, a negative real term spread, a positive nominal term spread and the predictability of stock returns, without compromising the model's ability to fit key macroeconomic variables. The interest rate smoothing in the monetary policy rule helps generate a low risk-free rate volatility which has been difficult to achieve for standard real business cycle models where monetary policy is neutral. In an application, I show that the model provides a framework for analyzing monetary policy interventions and the associated effects on asset prices and the real economy. Macroeconomic news and the stock market: Evidence from the eurozone. This paper is an empirical study of excess return behavior in the stock market in the euro area around days when important macroeconomic news about inflation, unemployment or interest rates are scheduled for announcement. I identify state dependence such that equity risk premia on announcement days are significantly higher when the interests rates are in the vicinity of the zero lower bound. Moreover, I provide evidence that for the whole sample period, the average excess returns in the eurozone are only higher on days when FOMC announcements are scheduled for release. However, this result vanishes in a low interest rate regime. Finally, I document that the European stock market does not command a premium for scheduled announcements by the European Central Bank (ECB). The impact of ECB monetary policy surprises on the German stock market. We examine the impact of ECB monetary policy surprises on German excess stock returns and the possible reasons for such a response. First, we conduct an event study to asses the impact of conventional and unconventional monetary policy on stock returns. Second, within the VAR framework of Campbell and Ammer (1993), we decompose excess stock returns into news regarding expected excess returns, future dividends and future real interest rates. We measure conventional monetary policy shocks using futures markets data. Our main findings are that the overall variation in German excess stock returns mainly reflects revisions in expectations about dividends and that the stock market response to monetary policy shocks is dependent on the prevailing interest rate regime. In periods of negative real interest rates, a surprise monetary tightening leads to a decrease in excess stock returns. The channels behind this response are news about higher expected excess returns and lower future dividends.

Submitted by Livio Cuzzi Maya (liviomaya@gmail.com) on 2015-04-14T14:31:39Z No. of bitstreams: 1 dis_ref.pdf: 631490 bytes, checksum: d730ea4e26e9e8795547f24ea6da9284 (MD5) / Approved for entry into archive by BRUNA BARROS (bruna.barros@fgv.br) on 2015-04-17T14:05:44Z (GMT) No. of bitstreams: 1 dis_ref.pdf: 631490 bytes, checksum: d730ea4e26e9e8795547f24ea6da9284 (MD5) / Approved for entry into archive by Marcia Bacha (marcia.bacha@fgv.br) on 2015-05-04T12:20:07Z (GMT) No. of bitstreams: 1 dis_ref.pdf: 631490 bytes, checksum: d730ea4e26e9e8795547f24ea6da9284 (MD5) / Made available in DSpace on 2015-05-04T12:20:38Z (GMT). No. of bitstreams: 1 dis_ref.pdf: 631490 bytes, checksum: d730ea4e26e9e8795547f24ea6da9284 (MD5) Previous issue date: 2015-04-07 / We develop an affine jump diffusion (AJD) model with the jump-risk premium being determined by both idiosyncratic and systematic sources of risk. While we maintain the classical affine setting of the model, we add a finite set of new state variables that affect the paths of the primitive, under both the actual and the risk-neutral measure, by being related to the primitive's jump process. Those new variables are assumed to be commom to all the primitives. We present simulations to ensure that the model generates the volatility smile and compute the 'discounted conditional characteristic function'' transform that permits the pricing of a wide range of derivatives. / Desenvolvemos um model afim com saltos com o prêmio pelo risco dos saltos determinado tanto por variáveis idiossincráticas quanto por variáveis sistêmicas. Mantemos a clássica estrutura linear do modelo, mas adicionamos um conjunto finito de novas variáveis de estado que afetam o caminho percorrido pelo primitivo, tanto no distribuição real quanto na distribuição neutra ao risco, por afetar o processo de saltos do primitivo. Assumimos que essas novas variáveis de estado são comuns a todos os primitivos. Apresentamos simulações que garantem que o modelo gere o sorriso da volatilidade e computamos a transformação da 'função característica descontada condicional' que permite a precificação de uma ampla gama de derivativos.

Das moderne Finanzsystem ist komplex, dynamisch, hochdimensional und oftmals nicht stationär. All diese Faktoren stellen große Herausforderungen beim Messen des zugrundeliegenden Finanzrisikos dar, das speziell für Marktteilnehmer von oberster Priorität ist. Hochdimensionalität, die aus der ansteigenden Vielfalt an Finanzprodukten entsteht, ist ein wichtiges Thema für Ökonometriker. Ein Standardansatz, um mit hoher Dimensionalität umzugehen, ist es, Schlüsselvariablen auszuwählen und kleine Koeffizientenen auf null zu setzen, wie etwa Lasso. In der Finanzmarktanalyse kann eine solche geringe Annahme helfen, die führenden Risikofaktoren aus dem extrem großen Portfolio, das letztendlich das robuste Maß für finanzielles Risiko darstellt, hervorzuheben. In dieser Arbeit nutzen wir penalisierte Verfahren, um die ökonometrischen Maße für das finanzielle Risiko in hoher Dimension zu schätzen, sowohl mit nieder-, als auch hochfrequenten Daten. Mit Fokus auf dem Finanzmarkt, können wir das Risikonetzwerk des ganzen Systems konstruieren, das die Identifizierung individualspezifischen Risikos erlaubt. / Modern financial system is complex, dynamic, high-dimensional and often possibly non-stationary. All these factors pose great challenges in measuring the underlying financial risk, which is of top priority especially for market participants. High-dimensionality, which arises from the increasing variety of the financial products, is an important issue among econometricians. A standard approach dealing with high dimensionality is to select key variables and set small coefficient to zero, such as lasso. In financial market analysis, such sparsity assumption can help highlight the leading risk factors from the extremely large portfolio, which constitutes the robust measure for financial risk in the end. In this paper we use penalized techniques to estimate the econometric measures of financial risk in high dimensional, with both low-frequency and high-frequency data. With focus on financial market, we could construct the risk network of the whole system which allows for identification of individual-specific risk.