Yield curve | EPFL Graph Search

In finance, the yield curve is a graph which depicts how the yields on debt instruments – such as bonds – vary as a function of their years remaining to maturity. Typically, the graph's horizontal or x-axis is a time line of months or years remaining to maturity, with the shortest maturity on the left and progressively longer time periods on the right. The vertical or y-axis depicts the annualized yield to maturity. Those who issue and trade in forms of debt, such as loans and bonds, use yield curves to determine their value. Shifts in the shape and slope of the yield curve are thought to be related to investor expectations for the economy and interest rates. Ronald Melicher and Merle Welshans have identified several characteristics of a properly constructed yield curve. It should be based on a set of securities which have differing lengths of time to maturity, and all yields should be calculated as of the same point in time. All securities measured in the yield curve should

A General Stochastic Volatility Model for the Pricing of Interest Rate Derivatives

Anders Trolle

We develop a tractable and flexible stochastic volatility multifactor model of the term structure of interest rates. It features unspanned stochastic volatility factors, correlation between innovations to forward rates and their volatilities, quasi-analytical prices of zero coupon bond options, and dynamics of the forward rate curve, under both the actual and risk-neutral measures, in terms of a finite-dimensional affine state vector. The model has a very good fit to an extensive panel data set of interest rates, swaptions, and caps. In particular, the model matches the implied cap skews and the dynamics of implied volatilities.

Oxford University Press2009

Essays in Monetary Policy and Asset Pricing

Benoit Vincent Sylvain Cornet

This thesis uses machine learning techniques and text data to investigate the relationships that arise between the Fed and financial markets, and their consequences for asset prices.The first chapter, entitled Market Expectations and the Impact of Unconventional Monetary Policy: An Application to Twitter Data, is an answer to (Greenlaw et al., 2018), who show, by looking at a large set of monetary policy announcements made between November 2008 and December 2017 - FOMC meetings, release of minutes and speeches of the Fed Chair - that long term yields tended to increase following these events. By doing so, the authors challenge common wisdom according to which the central bank intervention in the aftermath of the financial crisis lowered long term rates (Gagnon, 2016). Using machine learning and twitter data, this chapter develops a novel measure of market expectations of monetary policy, and shows that the increase in yields was simply due to a marginal adjustment of market expectations following announcements being less dovish than expected.The second chapter, entitled Informational Feedback Loop, Monetary Policy Decisions and Asset Prices Dynamics, investigates the consequences of a Fed that uses (1) its own private signal and (2) fed funds futures to take its monetary policy decision. Fed funds futures aggre- gate private information received by financial markets participants - traders - but they also depend on traders' expectations about the Fed's behavior, which makes futures endogenous in the central bank decision. The theoretical model shows that the surprise generated by monetary policy announcements and the subsequent adjustment in short term U.S. treasury yields depend on the precision of the signals received by each agent. When the signal received by traders is more precise than the central bank's, the latter relies more on fed funds futures to take its decision, and the surprise and adjustment of short term yields are smaller. By contrast, long term yields adjust only because the announcement provides traders with new information about the state of the economy, by revealing the central bank's private signal. Finally, when the Fed is averse to financial markets volatility, it tends to put some weight on fed funds futures even if they are not informative about the state of the economy. The empirical part of the paper provides some evidence supporting these channels, by using a topic and tone approach (Hansen and McMahon, 2016) to extract the precision of the signals received by the central bank and traders from FOMC minutes and tweets respectively.

EPFL2022

Essays in Information-Based Asset Pricing

Michael Hasler

I started my Ph.D. studies in the Fall 2008, a period ex-post perceived as being at the core of the Financial Crisis. At that time my ideas were vague and I struggled to find a good research topic. As surprising as it might appear, in one single week the discussions I had with my relatives provided me with enough ideas needed to write my thesis. Indeed, during that single week my relatives contacted me and complained about the fact that they had lost money invested in the financial market. They wanted me to advise them. I simply told them to invest their money in relatively safe bonds. My relatives did not realize how fruitful their behavior was for me. Indeed, after having advised them I wondered whether their willingness to gather information in bad times was optimal or pure irrationality. This issue is addressed in the first chapter of my thesis. The last two chapters investigate the impact of the willingness to gather more information in bad times than in good times on the dynamics of asset prices. My thesis answers two very important questions: When should investors acquire information given that information-processing is costly and what is the impact of the optimal information acquisition strategy on asset price dynamics? Consequently, the main topic of my thesis is the transmission of acquired information in financial markets. In the first chapter I consider a dynamic portfolio choice and costly information acquisition problem. Consistent with my relatives behavior, I show that it is optimal to gather more information in bad times than in good times. As the state of the economy worsens, investors optimally re-balance their portfolio towards safer investment and spend more and more money to gather more and more accurate information regarding future market moves. This result derived in a purely rational setup helps to understand the empirical observation that people tend to pay fluctuating attention to news, focusing more in downturns than in upturns. To the best of my knowledge, this paper is the first to consider costly information acquisition in a dynamic setting. The main contribution is to provide insights on how investors should optimally allocate their wealth to the financial market on the one hand and to information market on the other hand given current market conditions. In the second chapter (with Daniel Andrei) we model the optimal information acquisition strategy obtained in the first chapter in a general equilibrium framework and we show that this strategy (fluctuating attention paid to news) has very interesting implications on the dynamics of asset prices. Our main contribution is to highlight a J-shaped relationship between attention and volatility. By constructing an empirical measure of attention to news, we show that the theoretical prediction of our model holds in the data. Moreover, we are the first to provide a theoretical foundation for the fact that the slope of the term structure of equity premia is pro-cyclical. In the third chapter I investigate the impact of fluctuating attention to news in a multi-asset general equilibrium framework. My main contribution is to show that fluctuating attention to news generates return and volatility spillover effects among financial markets. As a bad shock hits one market, investors raise their attention to that particular market. This implies a simultaneous increase in each market volatility and in each cross-market correlation, a phenomenon called financial contagion. Moreover, I document that the interconnection between attention and uncertainty generates persistence in cross-market correlations. This very interesting result proves that persistence can be generated through Bayesian learning. Finally, I am the first to provide a theoretical foundation for the observed shape of the term structure of correlation. Consistent with empirical findings, I show that fluctuating attention implies an upward sloping term structure of correlation where the shorthand is wider than the longhand.

EPFL2013