Systemic Risk and the Refinancing Ratchet Effect

The confluence of three trends in the U.S. residential housing market-rising home prices, declining interest rates, and near-frictionless refinancing opportunities-led to vastly increased systemic risk in the financial system. Individually, each of these trends is benign, but when they occur simultaneously, as they did over the past decade, they impose an unintentional synchronization of homeowner leverage. This synchronization, coupled with the indivisibility of residential real estate that prevents homeowners from deleveraging when property values decline and homeowner equity deteriorates, conspire to create a "ratchet" effect in which homeowner leverage is maintained during good times without the ability to decrease leverage during bad times. If refinancing-facilitated homeowner-equity extraction is sufficiently widespread-as it was during the years leading up to the peak of the U.S. residential real-estate market-the inadvertent coordination of leverage during a market rise implies higher correlation of defaults during a market drop. To measure the systemic impact of this ratchet effect, we simulate the U.S. housing market with and without equity extractions, and estimate the losses absorbed by mortgage lenders by valuing the embedded put-option in non-recourse mortgages. Our simulations generate loss estimates of $1.5 trillion from June 2006 to December 2008 under historical market conditions, compared to simulated losses of$280 billion in the absence of equity extractions.Risk; Financial Crisis; Household Finance; Real Estate; Subprime

What Happened To The Quants In August 2007?: Evidence from Factors and Transactions Data

During the week of August 6, 2007, a number of quantitative long/short equity hedge funds experienced unprecedented losses. It has been hypothesized that a coordinated deleveraging of similarly constructed portfolios caused this temporary dislocation in the market. Using the simulated returns of long/short equity portfolios based on five specific valuation factors, we find evidence that the unwinding of these portfolios began in July 2007 and continued until the end of 2007. Using transactions data, we find that the simulated returns of a simple marketmaking strategy were significantly negative during the week of August 6, 2007, but positive before and after, suggesting that the Quant Meltdown of August 2007 was the combined effects of portfolio deleveraging throughout July and the first week of August, and a temporary withdrawal of marketmaking risk capital starting August 8th. Our simulations point to two unwinds---a mini-unwind on August 1st starting at 10:45am and ending at 11:30am, and a more sustained unwind starting at the open on August 6th and ending at 1:00pm---that began with stocks in the financial sector and long Book-to-Market and short Earnings Momentum. These conjectures have significant implications for the systemic risks posed by the hedge-fund industry.

Statistical analysis of illiquidity risk and premium in financial price signals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 185-188).Price is the most visible signal produced by competition and interaction among a complex ecology of entities in a system called financial markets. This thesis deals with statistical analysis and model identification based on such signals. We approach this problem at various levels of abstraction, with a particular emphasis on linking certain statistical anomalies identified to specific frictions that are only observable in a more microscopic view.We first give a brief review of the framework for the analysis of financial prices. We highlight the important role of information by introducing the concept of informational efficiency. The main body consists of two parts. Part A consists of Chapters 3, 4 and 5. We first link unpredictability of financial returns, a direct consequence of the informational efficiency, to the expected covariance structure of resulting return signals. We discuss a particular algorithm designed to detect the existence of weak mean-reverting component in the observed returns. Applying this detection scheme to US stock returns between 1995 and 2007, we detect a statistically significant but continually decreasing mean-reverting component in the returns. To explain this observation, we link the mean-reverting component to the arrival structure of buyers and sellers and their interactions. We discuss a particular model for this interaction and apply various tests to establish the validity of the proposed model. Part A concludes with an application of these tools in analyzing the sequence of events in August 2007 which resulted in a breakdown of normal behavior of the system.Part B, consisting of Chapters 6 and 7, also deals with the issue of predictability in financial returns, but at a different frequency and based on a different set of instruments. We first produce the evidence for an unusually high level of predictability among returns of certain classes of hedge funds. To explain this observation, we discuss a model built based on the notion of partially observed price signals. When prices are not observed, for example due to lack of trading, the most recent price is used to calculate the value of an investment, and this process results in perceived serial correlation in the calculated returns. We view this lack of trading as the second example of friction in this system, and set out to link this friction to the mean of the resulting returns signals. We find strong link between predictability and first moment in certain groups of returns used.by Amir E. Khandani.Ph.D

Cooperative routing in wireless networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 87-89).In this thesis, we study the problem of energy efficiency and reliability in wireless ad-hoc networks. First, we introduce the idea of wireless cooperation advantage. We formulate the problem of finding the minimum energy cooperative route for a wireless network under idealized channel and receiver models. Fundamental to the understanding of the routing problem is the understanding of the optimal power allocation for a single message transmission between two sets of nodes. We present the solution to this problem, and use that as the basis for solving the minimum energy cooperative routing problem. We analytically obtain the energy savings in regular line and regular grid networks. We propose heuristics for selecting the cooperative route in random networks and give simulation results confirming significant energy savings achieved through cooperation. In the second part, we study the problem of route reliability in a multi-hop network. We look at the reliability issue at the link level and extend those result to a wireless network setting. In the network setting, we first define and analyze the reliability for a fixed route and then propose algorithms for finding the optimal route between a source-destination pair of nodes. The relationship between the route reliability and consumed power is studied. The idea of route diversity is introduced as a way to improve the reliability by taking advantage of the broadcast property, the independence of fading state between different pairs of nodes, and space diversity created by multiple intermediate relay nodes along the route. We give analytical results on improvements due to route diversity in some simple network topologies.by Amir E. Khandani.S.M

Systemic risk and the refinancing ratchet effect

The combination of rising home prices, declining interest rates, and near-frictionless refinancing opportunities can create unintentional synchronization of homeowner leverage, leading to a “ratchet” effect on leverage because homes are indivisible and owner-occupants cannot raise equity to reduce leverage when home prices fall. Our simulation of the U.S. housing market yields potential losses of $1.7 trillion from June 2006 to December 2008 with cash-out refinancing vs. only$330 billion in the absence of cash-out refinancing. The refinancing ratchet effect is a new type of systemic risk in the financial system and does not rely on any dysfunctional behaviors

Systemic Risk and the Refinancing Ratchet Effect

The confluence of three trends in the U.S. residential housing market---rising home prices, declining interest rates, and near-frictionless refinancing opportunities---led to vastly increased systemic risk in the financial system. Individually, each of these trends is benign, but when they occur simultaneously, as they did over the past decade, they impose an unintentional synchronization of homeowner leverage. This synchronization, coupled with the indivisibility of residential real estate that prevents homeowners from deleveraging when property values decline and homeowner equity deteriorates, conspire to create a "ratchet" effect in which homeowner leverage is maintained or increased during good times without the ability to decrease leverage during bad times. If refinancing-facilitated homeowner-equity extraction is sufficiently widespread---as it was during the years leading up to the peak of the U.S. residential real-estate market---the inadvertent coordination of leverage during a market rise implies higher correlation of defaults during a market drop. To measure the systemic impact of this ratchet effect, we simulate the U.S. housing market with and without equity extractions, and estimate the losses absorbed by mortgage lenders by valuing the embedded put-option in non-recourse mortgages. Our simulations generate loss estimates of $1.5 trillion from June 2006 to December 2008 under historical market conditions, compared to simulated losses of$280 billion in the absence of equity extractions.

Privacy-Preserving Methods for Sharing Financial Risk Exposures

Unlike other industries in which intellectual property is patentable, the financial industry relies on trade secrecy to protect its business processes and methods, which can obscure critical financial risk exposures from regulators and the public. We develop methods for sharing and aggregating such risk exposures that protect the privacy of all parties involved and without the need for a trusted third party. Our approach employs secure multi-party computation techniques from cryptography in which multiple parties are able to compute joint functions without revealing their individual inputs. In our framework, individual financial institutions evaluate a protocol on their proprietary data which cannot be inverted, leading to secure computations of real-valued statistics such a concentration indexes, pairwise correlations, and other single- and multi-point statistics. The proposed protocols are computationally tractable on realistic sample sizes. Potential financial applications include: the construction of privacy-preserving real-time indexes of bank capital and leverage ratios; the monitoring of delegated portfolio investments; financial audits; and the publication of new indexes of proprietary trading strategies

Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study

Studies have shown that tumor angiogenesis is an essential process for tumor growth, proliferation and metastasis. Also, tumor angiogenesis is an important prognostic factor of clear cell renal cell carcinoma (ccRCC), as well as a factor in guiding treatment with antiangiogenic agents. Here, we attempted to find the associations between tumor angiogenesis and radiomic imaging features from PET/MRI. Specifically, sparse canonical correlation analysis was conducted on 3 feature datasets (i.e., radiomic imaging features, tumor microvascular density (MVD), and vascular endothelial growth factor (VEGF) expression) from 9 patients with primary ccRCC. In order to overcome the potential bias of intratumoral heterogeneity of angiogenesis, this study investigated the relationship between regional expressions of angiogenesis and VEGF, and localized radiomic features from different parts within the tumors. Our study highlighted the significant strong correlations between radiomic features and MVD, and also demonstrated that the spatiotemporal features extracted from DCE-MRI provided stronger radiomic correlation to MVD than the textural features extracted from Dixon sequences and FDG PET. Furthermore, PET/MRI, which takes advantage of the combined functional and structural information, had higher radiomics correlation to MVD than solely utilizing PET or MRI alone

Alternate Metabolic Programs Define Regional Variation of Relevant Biological Features in Renal Cell Carcinoma Progression

ccRCC has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We’ve recently employed the novel positron emission tomography/magnetic resonance (PET/MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans

Consumer Credit-Risk Models Via Machine-Learning Algorithms

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies and defaults, with linear regression R2’s of forecasted/realized delinquencies of 85%. Using conservative assumptions for the costs and benefits of cutting credit lines based on machine-learning forecasts, we estimate the cost savings to range from 6% to 25% of total losses. Moreover, the time-series patterns of estimated delinquency rates from this model over the course of the recent financial crisis suggest that aggregated consumer credit-risk analytics may have important applications in forecasting systemic risk.Massachusetts Institute of Technology. Laboratory for Financial EngineeringMassachusetts Institute of Technology. Center for Future Bankin