Talk Titles and Abstracts
Yacine Aït-Sahalia (with Julio Cacho-Diaz and Roger Laeven)
Modeling Financial Contagion Using Mutually Exciting Jump Processes
Abstract: Adverse shocks to stock markets propagate across the
world, with a jump in one region of the world seemingly causing
an increase in the likelihood of a different jump in another region
of the world. To capture this effect mathematically, we introduce
a model for asset return dynamics with a drift component, a volatility
component and mutually exciting jumps known as Hawkes processes.
In the model, a jump in one region of the world or one segment
of the market increases the intensity of jumps occurring both
in the same region (self-excitation) as well as in other regions
(cross-excitation). The model generates the type of jump clustering
that is observed empirically. Jump intensities then mean-revert
until the next jump. We develop and implement an estimation procedure
for this model. Our estimates provide evidence for self-excitation
both in the US market as well as in other world markets. Furthermore,
we find that US jumps tend to get reflected quickly in most other
markets, while statistical evidence for the reverse transmission
is much less pronounced. Implications of the model for measuring
market stress, risk management and optimal portfolio choise are
also investigated.
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Marco Bonomo (with René Garcia, Nour Meddahi and Roméo
Tédongap)
Generalized Disappointment Aversion, Volatility Long-Run Risk
and Asset Prices
Abstract: We propose an asset pricing model where preferences
display generalized disappointment aversion (as in Routledge and
Zin 2009) and the endowment process involves long-run volatility
risk. Those preferences, which are embedded in Epstein and
Zin recursive utility framework, overweight disappointing results
as compared to expected utility, and display relatively larger
risk aversion for small gambles. Our endowment process has only
one of the two sources of long-run risks proposed by Bansal and
Yaron (2004) (BY): the volatility risk. We approximate the
endowment process with a Markov switching model. This enables
us to derive closed formula solutions for all returns moments
and predictability regressions.The model produces asset returns
moments and predictability patterns in line with the data. Compared
to BY we generate: i) more predictability of excess returns
by price-dividend ratios; ii) less predictability of consumption
growth rates by price-dividend ratios. Differently from BY model,
our results do not depend on IES being greater than one: similar
results may be obtained with IES lower than one. Our results
are not due to overparametrization of preferences either: simple
disappointment averse with two paramters, where risk aversion
comes only from disappointment aversion generates similar implications.
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Robert Engle
Long Term Skewness and Systemic Risk
Abstract: Financial risk management has generally focused on
short term risks rather than long term risks and arguably this
is an important component of the current financial crisis. Econometric
approaches to measuring long term risk are investigated by testing
for measures of long term skewness associated with asymmetric
volatility models. This skewness in a market factor leads to default
correlations even far in the future. Investors concerned about
long term risks can hedge exposure as in the ICAPM. Such hedging
will affect asset prices and can be tested directly with volatility
models. Using estimates from VLAB, evidence is found for several
types of hedge portfolios including volatility, long bonds, term
spread, credit spread and gold.
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Jianqing Fan
Vast Volatility Matrix Estimation using High Frequency Data for
Portfolio Selection
Abstract: Portfolio allocation with gross-exposure constraint
is an effective method to increase the efficiency and stability
of selected portfolios among a vast pool of assets, as demonstrated
in Fan, Zhang and Zhang (2008). The required high-dimensional
volatility matrix can be estimated by using high frequency financial
data. This enables us to better adapt the local volatilities and
local correlations among vast assets and to increase significantly
the sample size for estimating the volatility matrix. This
paper studies the volatility matrix estimation using high-dimensional
high-frequency data from the perspective of portfolio selection.
Specifically, we propose the use of ``pairwise-refresh time"
and ``all-refresh-time" methods for estimation vast
covariance matrix and compare their merits in the portfolio selection.
We also establish the large deviation results of the estimates,
which guarantee good properties of the estimated volatility matrix
in vast asset allocation with gross exposure constraints.
Extensive numerical studies are made via carefully designed simulation
studies. Comparing with the methods based on low frequency
daily data, our methods can capture the most recent trend of the
time varying volatility and correlation, hence provide more accurate
guidance of the portfolio allocation of the next time period.
The advantage of use high-frequency data is significant in our
simulation and empirical studies, which consist of 30 Dow-Jones
industrial stocks.
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Christian Gourieroux (with Patrick Gagliardini)
Approximate Derivative Pricing for Large Class of Homogeneous
Assets with Systematic Risk
Abstract: We consider an homogeneous class of assets, whose returns
are driven by an unobservable factor representing systematic risk.
We derive approximated pricing formulas for the future factor
values and their proxies, when the size n of the class is large.
Up to order 1=n, these closed form approximations involve well-chosen
summary statistics of the basic asset returns, but not the current
and lagged factor values. The potential of the closed form approximation
formulas seems quite large, especially for credit risk analysis,
which considers large portfolios of individual loans or corporate
bonds, and for longevity risk analysis, which involves large portfolios
of life insurance contracts.
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Kaddour Hadri (with Ruijun Bu)
Modelling Multivariate Interest Rates using Time-Varying Copulas
and Reducible Non-Linear Stochastic Differential Equations
Abstract: We propose a new approach for modelling non-linear
multivariate interest rate processes based on time-varying copulas
and reducible stochastic differential equations (SDEs). In the
modelling of the marginal processes, we consider a class of non-linear
SDEs that are reducible to Ornstein-Uhlenbeck (OU) process or
Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility
is achieved via a non-linear transformation function. The main
advantage of this approach is that these SDEs can account for
non-linear features, observed in short-term interest rate series,
while at the same time leading to exact discretization and closed
form likelihood functions. Although a rich set of specifications
may be entertained, our exposition focuses on a couple of non-linear
constant elasticity volatility (CEV) processes, denoted OU-CEV
and CIR-CEV, respectively. These two processes encompass a number
of existing models that have closed form likelihood functions.
The transition density, the conditional distribution function,
the steady-state density function are derived in closed form as
well as the conditional and unconditional moments for both processes.
In order to obtain more flexible functional form over time, we
allow the transformation function to be time-varying. Results
from our study of US and UK short term interest rates suggest
that the new models outperform existing parametric models with
closed form likelihood functions. We also find the time-varying
effects in the transformation functions statistically significant.
To examine the joint behaviour of interest rate series, we propose
flexible non-linear multivariate models by joining univariate
non-linear processes via appropriate copulas. We study the conditional
dependence structure of the two rates using Patton (2006a) time-varying
Symmetrized Joe-Clayton copula. We find evidence of asymmetric
dependence between the two rates, and that the level of dependence
is positively related to the level of the two rates.
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Lars Hansen
Nonlinear Filtering and Learning Dynamics (with Nick Polson and
Thomas Sargent)
Abstract: We develop and apply two refinements of particle filtering
methods to be used in characterizing the learning behavior of
individual agents within an economic model. One refinement extends
the use of sufficient statistics conditioned on hidden states
and a subset of parameters as a device to induce randomization
in the parameters within the algorithm. This allows us to replenish
particles and extend the number of time periods to which the numerical
results remain reliable. The other refinement focuses the accuracy
of the particle filtering algorithm on the portions of the filtered
distribution that are more germane to decision problems of the
individual agents. We illustrate these methods in an equilibrium
model with investors that make robust decisions implemented through
the use of exponential tilting.
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Stan Hurn
Quasi-maximum Likelihood Estimation of the Parameters of Multivariate
Diffusions
Abstract: This paper develops a quasi-maximum likelihood procedure
for estimating the parameters of multi-dimensional stochastic
differential equations. The transitional density is taken to be
a time-varying multivariate Gaussian where the first two moments
of the distribution are approximately the true moments of the
unknown transitional density. For affine drift and diffusion functions,
the moments are shown to be exactly those of the true transitional
density and for nonlinear drift and diffusion functions the approximation
is extremely good. The estimation procedure is easily generalizable
to models with latent factors, such as the stochastic volatility
class of model, thereby avoiding the need to use proxies.
___________________________________________________________________
Jean Jacod
Testing for Functional Relationships between Log-price and Volatility
(with C. Kluppelberg and G. Muller)
Abstract: In many models for asset prices, the (stochastic) volatility
jumps at the same times as the price itself, and moreover the
two jumps are related by a functional relationship: this is in
particular the case for the so-called COGARCH (continuous-time
GARCH) models. In this paper we give a method allowing to
test whether a specific relationship between the price and volatility
jumps is satisfied.
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Robert Kimmel
On Estimation of Risk Premia in Linear Factor Models (with Kewei
Hou)
Abstract: We examine theoretical and econometric issues in the
estimation of risk premia in a linear factor model, when the model
is possibly misspecified. Common empirical methodologies can produce
very misleading results. With unspanned factors and possible model
misspecification, there are problems not just in estimating the
risk premia, but even in defining them unambiguously. We show
that, for a given set of test assets, the risk premium of an unspanned
factor is very sensitive to the choice of other factors in the
model. However, the risk premium of the projection of the unspanned
factor onto the asset space is robust to the choice of other factors.
The problem is greatly exacerbated in the presence of model misspecification,
and can occur even when the unspanned components of the factors
are very small (relative to the spanned components). These results
highlight the importance of using factor-mimicking portfolios,
rather than unspanned factors, in estimation of linear factor
models.
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Suzanne Lee
Jumps and Information Flow in Financial Markets
Abstract: I propose a new two-stage semi-parametric test to investigate
the predictability of stochastic jump arrivals in asset prices.
The test allows us to pin down relevant information for jump prediction
up to the intra-day level. Based on the test, I find that systematic
jumps in U.S. individual equity markets are likely to occur shortly
after macroeconomic information release such as Fed's announcements,
market jumps, employment reports, or initial jobless claims. I
also present firm-specific jump predictors along with the jump
clustering effect. Evidence suggests that systematic jump intensity
has increased in recent years.
__________________________________________________________________
Haitao Li
Exploring Statistical Arbitrage Opportunities in the Term Structure
of CDS Spreads
Abstract: The rapid growth of the CDS market makes it possible
to speculate on the relative pricing of the credit risk of a company
across a wide range of maturities. Based on a reduced-form model
of credit risk, we explore “statistical” arbitrage opportunities
in the term structure of CDS spreads of a large number of companies
in North America. Specifically, we estimate an affine model for
the term structure of CDS spreads of a given company and identify
mis-valued CDS contracts along the credit curve. We trade market-neutral
portfolios of mis-valued CDS contracts relative to our model,
betting that the mis-valuation will disappear over time. Empirical
analysis shows that our “arbitrage” strategy can be
very profitable. For most firms, the Sharpe ratios are higher
than one, and for some firms, the Sharpe ratios are even above
two.
__________________________________________________________________
Jia Li
A Local-to-continuity Theory for the Pre-averaging Method
Abstract: This paper develops a local asymptotic theory for certain
functionals of moving averages of Ito semi-martingales plus noise
when the semimartingales are nearly continuous. The model provides
a more complete interface between the continuity and jump asymptotics
developed in (Jacod, Podolskij, and
Vetter 2009). Simulation suggests that our theory provides an
improvement over the existing theory in a
practically relevant setting. The theory has applications to the
estimation of functionals of jumps and to
the analysis of the local asymptotic power of tests for jumps
with noisy high frequency data.
_________________________________________________________________
Yingying Li (with Yacine Aït-Sahalia and Jianqing Fan)
Studying the Leverage Effect Based on High-frequency Data
Abstract: We show how high-frequency data can be used to detect
the leverage effect, and explain why extra caution has to be used
when one studies the leverage effect based on the asymptotic results
of the high-frequency volatility estimators.
_________________________________________________________________
Andrew Lo (with Mark T. Mueller)
WARNING: Physics Envy May Be Hazardous To Your Wealth
The quantitative aspirations of economists and financial analysts
have for many years been based on the belief that it should be
possible to build models of economic systems---and financial markets
in particular---that are as predictive as those in physics. While
this perspective has led to a number of important breakthroughs
in economics, "physics envy" has also created a false
sense of mathematical precision in some cases. We speculate on
the origins of physics envy, and then describe an alternate perspective
of economic behavior based on a new taxonomy of uncertainty. We
illustrate the relevance of this taxonomy with two concrete examples:
the classical harmonic oscillator with some new twists that make
physics look more like economics, and a quantitative equity market-neutral
strategy. We conclude by offering a new interpretation of tail
events, proposing an "uncertainty checklist" with which
our taxonomy can be implemented, and considering the role that
quants played in the current financial crisis.
________________________________________________________________
Cecilia Mancini
Test for the Presence of Noise in Observed Data
________________________________________________________________
Gael Martin (with Brendan McCabe and David Harris)
Optimal Probabilistic Forecasts for Counts
Abstract: Optimal probabilistic forecasts of integer-valued random
variables are derived. The optimality is achieved by estimating
the forecast distribution nonparametrically over a given broad
model class and proving asymptotic efficiency in that setting.
The ideas are demonstrated within the context of the integer autoregressive
class of models, which is a suitable class for any count data
that can be interpreted as a queue, stock, birth and death process
or branching process. The theoretical proofs of asymptotic optimality
are supplemented by simulation results which demonstrate the overall
superiority of the nonparametric method relative to a misspecified
parametric maximum likelihood estimator, in large but finite samples.
The method is applied to counts of wage claim benefits, stock
market iceberg orders and civilian deaths in Iraq, with bootstrap
methods used to quantify sampling variation in the estimated forecast
distributions. Illustration of the method using the stock market
order data will be emphasized in the presentation.
_______________________________________________________________
Nour Meddahi (with Peter Christoffersen, Bruno Feunou, and Kris
Jacobs)
The Economic Value of Realized Volatility
Abstract: Many existing studies have documented that daily realized
volatility estimates based on intraday data provide volatility
forecasts that are superior to forecasts constructed from daily
data only. Some studies also find that density forecasts based
on realized volatility are superior to those based on daily data.
We investigate whether these forecasting improvements translate
into economic value added. In order to address this question we
develop a new class of discrete-time option valuation models that
use daily returns as well as realized volatility, and that nest
the daily Heston and Nandi (2000) GARCH model as a special case.
We derive closed-form option valuation formulas and we assess
the option valuation properties using S&P500 return and option
data. We find that realized volatility reduces the pricing errors
of the benchmark model significantly across moneyness, maturity
and volatility levels.
______________________________________________________________
Joon Park
Asymptotic Theory of Maximum Likelihood Estimator for Diffusion
Model
Abstract: We derive the asymptotics of the maximum likelihood
estimators for diffusion models. The models considered in the
paper are very general, including both stationary and nonstationary
diffusions. For such a broad class of diffusion models, we establish
the consistency and find the limit distributions of the exact
maximum likelihood estimator, and also the quasi and approximate
maximum likelihood estimators based on various versions of approximated
transition densities. Our asymptotics are two dimensional, allowing
the sampling interval to decrease as well as the time span of
sample to increase. The two dimensional asymptotics provide a
unifying framework for the development of statistical theories
for the stationary and nonstationary diffusion model. More importantly,
they yield the asymptotic expansions that are very useful to analyze
the exact, quasi and approximate maximum likelihood estimators
of the diffusion models, if the samples are collected at high
frequency intervals over modest lengths of sampling horizons as
in the case of many practical applications.
______________________________________________________________
Eckhard Platen
Empirical Properties of a Well Diversified Global Stock Index
Abstract: Most of the papers that study the distributional and
fractal properties of financial instruments focus on stock prices
or exchange rates. This leads typically to mixed results concerning
the distributions of log-returns and some multi-fractal properties
of exchange rates, stock prices, and regional indices. It will
be suggested to use a very well diversified world stock index
in various denominations as the main object of empirical analysis.
Such index has been formed using daily and intraday data. It aggregates,
in principle, the non-diversifiable risk of the stock market.
Compared to other global stock market indices it has extremely
low volatility and, thus, a high signal to noise ratio when denominated
in a currency. Furthermore, by diversification such an index can
be shown to approximate the growth optimal portfolio or numeraire
portfolio, which is the central object of the, so called benchmark
approach. The paper will demonstrate that the above mentioned
diversified index is an ideal object for studying the statistical
properties of given securities. For instance, when denominating
the savings account of a currency in units of this diversified
global world index, one observes the movements of the currency
against the entire market. This provides a practically undisturbed
observation of the currency dynamics against the whole of the
market. In this manner, one can conveniently disentangle, e.g.,
the superposition of the characteristic properties of the two
currencies generating a given exchange rate. The exchange rate
is then obtained as the ratio of the two currency denominations
of the benchmark.
The proposed benchmark approach to the empirical analysis of financial
data allows one to establish remarkable stylized facts. For instance,
the log-returns of a well diversified global stock index, when
denominated in a currency, are with high significance Student
t distributed with about four degrees of freedom. The repeatedly
documented multi-fractal appearance of financial time series turns
out to be only very weak when analysed for a well diversified
global index. The Hurst exponent of the observed mono-fractal
behavior assumes typical values between 0.55 and 0.65. Accordingly,
the quadratic variation vanishes asymptotically when reducing
the observation time step size. These results can be contrasted
with the mixed findings on empirical properties of FX rates or
stock prices. A range of further empirical facts can be expected
to be identifiable when using a well diversified index in the
denomination of a given security as the object of study.
_____________________________________________________________
Eric Renault (with Thijs van der Heijdeny and Bas J.M. Werker)
A Structural Autoregressive Conditional Duration Model
Abstract: We propose a structural model for durations between
events and associated marks. Our model is structural in the sense
that both durations and marks are generated by an underlying Brownian
motion. In particular, we model the durations as the successive
passage times of this Brownian motion relative to in itself random
boundaries. Additional Brownian motions serve as processes generating
the marks, whose conditional distribution is a mixture of normals.
Multivariate Brownian motions allow us to incorporate a vector
of marks combined with a single duration generating process. Our
model embeds in particular the standard autoregressive conditional
duration model. Applied to high-frequency financial data, we derive
the conditional distributions of the durations and the vector
of price changes. A first empirical illustration, using transaction
level data on a NYSE.
_____________________________________________________________
Roberto Renò
Nonparametric Leverage Effects
Abstract: Vast empirical evidence points to the existence of
a negative correlation, named "leverage effect," between
shocks in volatility and shocks in returns. We provide a nonparametric
theory of leverage estimation in the context of a continuous-time
stochastic volatility model with jumps in returns, jumps in volatility,
or both. Leverage is defined as a flexible function of the state
of the firm, as summarized by the spot volatility level. We show
that its point-wise functional estimates have asymptotic properties
(in terms of rates of convergence, limiting biases, and limiting
variances) which crucially depend on the likelihood of the individual
jumps and co-jumps as well as on the features of the jump size
distributions. Empirically, we find economically important time-variation
in leverage with more negative values associated with higher volatility
levels.
_____________________________________________________________
Paul Schneider (with Damir Filipovic and Eberhard Mayerhofer)
Transition Density Approximations for Multivariate Affine Jump
Diffusion Processes
Abstract: We develop closed-form transition density approximations
for multivariate affine jump diffusion processes using polynomial
expansion techniques. The approximations converge in L2 for a
fixed time horizon, provided that the processes with support on
R+ satisfy non-attainment conditions. Empirical applications in
portfolio credit risk, likelihood inference, and option pricing
using the (integrated) square-root jump diffusion, and Heston's
model indicate that the approximations perform very accurately.
The expansions are extremely fast to evaluate and numerically
stable compared to Fourier inversion.
____________________________________________________________
Osnat Stramer
Bayesian Inference of Discretely Sampled Markov Processes with
Closed-Form Likelihood Expansions
Abstract: The closed-form (CF) likelihood approximation of Ait-Sahalia
(2002, 2008) is commonly used in financial modeling. Bayesian
inference requires the use of MCMC and the (unnormalized) CF likelihood
can become inaccurate when the parameters are far from the MLE;
samplers can become stuck when (typically) in the tails of the
posterior distribution. Auxiliary variables have been used in
conjunction with MCMC to address intractable normalizers (see
Moller et al. (2006)), but choosing such variables is not trivial.
We propose a MCMC algorithm that addresses the intractable normalizers
in the CF likelihood which 1) is easy to implement, 2) yields
a sampler with the correct limiting distribution, and 3) greatly
increases the stability of the sampler compared to using the unnormalized
CF likelihood in a standard Metropolis-Hastings algorithm. The
efficacy of our approach is demonstrated in a simulation study
of the Cox-Ingersoll-Ross (CIR) and Heston models, and is applied
to two well known real-world datasets.
____________________________________________________________
George Tauchen (with Viktor Todorov)
The Realized Laplace Transform of Volatility
Abstract: We introduce a new measure constructed from high-frequency
financial data which we call the Realized Laplace Transform of
volatility. The statistic provides a nonparametric estimate for
the empirical Laplace transform of the latent stochastic volatility
process over a given interval of time. When a long span of data
is used, i.e., under joint long-span and fill-in asymptotics,
it is an estimate of the volatility Laplace transform. The asymptotic
behavior of the statistic depends on the small scale behavior
of the driving martingale. We derive the asymptotics both in the
case when the latter is known and when it needs to be inferred
from the data. When the underlying process is a jump-diffusion
our statistic is robust to jumps and when the process is pure-jump
it is robust to presence of less active jumps. We apply our results
to simulated and real financial data.
__________________________________________________________
Allan Timmerman
What is the Shape of the Risk-Return Relation?
Abstract: Using a flexible modeling approach that avoids imposing
restrictive parametric assumptions, we find evidence of a clear
non-monotonic relation between conditional volatility and expected
stock returns: At low-to-medium levels of conditional volatility
there is a positive trade-off between risk and expected returns,
but this relationship gets inverted at high levels of conditional
volatility as observed during the recent financial crisis. We
next propose a novel measure of risk based on the conditional
covariance between daily observations on a broad economic activity
index and stock returns. Using this measure, we find clear evidence
of a monotonically increasing risk-return trade-off. Our finding
that the conditional volatility-expected return relation is non-monotonic,
while the conditional covariance-expected return is monotonically
rising helps explain why some empirical studies find a negative
risk-return relation, while others find a positive risk-return
trade-off and also suggests that a positive risk-return relationship
can be established once a better measure of risk is used.
_________________________________________________________
Viktor Todorov (with Tim Bollerslev)
Estimation of Jump Tails
Abstract: We consider the problem of estimating the jump tails
of an Ito semimartingale. The new estimation strategy developed
in the paper is based on in-fill asymptotic arguments and a method-of-moments
type procedure that explicitly utilizes the weak assumption of
regular variation in the jump tails. On implementing the new procedures
with actual high-frequency data for the aggregate market portfolio,
we find strong evidence for richer and much more complex dynamic
dependencies in the jump tails than hitherto considered in the
literature.
________________________________________________________
Giovanni Urga (with Ana-Maria Dumitru)
Identifying Jumps in Financial Assets with a Comparison between
Nonparametric Jump Tests
Abstract: We perform a comprehensive Monte Carlo comparison between
five procedures available in the literature to detect jumps in
financial assets-Andersen et al. (2007), Lee and Mykland (2008),
the Aït-Sahalia and Jacod (2008), the Barndorff-Nielsen and
Shephard (2006a), the Jiang and Oomen (2008), and the Podolskij
and Ziggel (2008). We evaluate size and power properties of the
procedures under alternative sampling frequencies, levels of volatility,
persistence in volatility, degree of contamination with microstructure
noise, jump size and intensity. Using high frequency data for
US Treasury bonds, we compare the performance of the alternative
tests. Though overall the best performance is showed by the Lee
and Mykland (2008) and Andersen et al. (2007) intraday procedures,
however we show the validity to use reunion and intersection across
procedures and across sampling frequencies for potential users
of the tests to minimise spurious jump detection.
________________________________________________________
Rossen Valkanov
Robust Measure of Time-Varying Skewness at Short and Long Horizons
________________________________________________________
Liuren Wu (with Laurent Calvet and Adlai Fisher)
A Multifrequency Theory of the Interest Rate Term Structure
Abstract: By applying power law scaling, we propose an extremely
parsimonious modeling framework to capture the interest rate term
structure movements across all frequencies. We estimate a model
with merely five parameters on the U.S. dollar LIBOR from one
to 12 months and swap rates from two to 30 years. Due to the extreme
parsimony, the five model parameters are estimated with strong
statistical significance. Meanwhile, by capturing movements of
all frequencies, the model prices all interest rate term structures
to near perfection, with the mean absolute pricing error averaging
around half a basis point. The model also generates much better
out-of-sample forecasting performance on the short-term interest
rates than either the random walk assumption or an autoregressive
specification. Further specification analysis shows that the power
law scaling assumption matches well with data.
________________________________________________________
Dacheng Xiu
Quasi-Maximum Likelihood Estimation of Volatility with High Frequency
Data
Abstract: This paper investigates the properties of the well-known
maximum likelihood estimator in the presence of stochastic volatility
and market microstructure noise, by extending the classic asymptotic
results of quasi-maximum likelihood estimation. When trying to
estimate the integrated volatility and the variance of noise,
this parametric approach remains consistent, efficient and robust
as a quasi-estimator under misspecified assumptions. Moreover,
it shares the model-free feature with nonparametric alternatives,
for instance realized kernels, while being advantageous over them
in terms of finite sample performance. Comparisons with a variety
of implementations of the Tukey-Hanning 2 kernel are provided
using Monte Carlo simulations, and an empirical study with the
Euro/US Dollar future illustrates its application in practice.
________________________________________________________
Jialin Yu
Option Value of Cash
Abstract: This paper presents a dynamic model of heterogeneous
beliefs (where investors agree to disagree) to study the positive
price-volume correlation during a housing downturn. It shows:
(i) beliefs may diverge, which prevents some pessimists from buying;
(ii) in the case that beliefs cross (i.e., buyers become more
optimistic than the sellers), home sales occur but are delayed
due to the buyers' option to sell cash higher (using house as
numeraire) if the downturn worsens. Such option to wait also has
implications for the velocity of money during deflation, troubled
assets since 2007, takeover bids, IPO waves, and fire sales.
_________________________________________________________
Zhibiao Zhao
Nonparametric Model Validations for Hidden Markov Models with
Applications in Financial Econometrics
Abstract: Nonparametric model validation under dependence has
been an important yet difficult problem. We address this problem
for hidden Markov models with partially observable variables and
unobservable or hidden states. We achieve this goal by constructing
nonparametric simultaneous confidence envelope for transition
density function of the observable variables and checking whether
the parametrically implied density estimate is entirely contained
within such an envelope. Our specification test procedure is motivated
by a functional connection between the transition density of the
observable variables and the Markov transition kernel of the unobservable
states. We show that our approach is applicable for a variety
of models widely used in financial econometrics, including continuous-time
diffusion models, hyperbolic Levy motions, stochastic volatility
models, nonlinear time series models, multivariate stochastic
regression models, and models with measurement errors among others.
The finite sample performance of the proposed method is studied
through simulations.
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