3,757 research outputs found
A New Look at the Forward Premium Puzzle
This paper analyzes the sampling properties of the widely documented large negative slope estimates in regressions of future exchange returns on current forward premium. We argue that the abnormal behavior of the slope estimators in these regressions arises from the simultaneous presence of high persistence, low signal-to-noise ratio, strong endogeneity and an omitted variable problem. The paper develops the limiting theory for the slope parameter estimators in the levels and differenced forward premium regressions under some assumptions that match the empirical properties of the data. The asymptotic results derived in the paper help to reconcile the findings from the levels and difference specifications and provide important insights about the time series properties of the implied risk premium.Forward premium anomaly, high persistence, low signal-to-noise ratio, local-to-unity asymptotics
Parametric and Nonparametric Volatility Measurement
Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.
Parametric and Nonparametric Volatility Measurement
Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.
Efficient simulation of density and probability of large deviations of sum of random vectors using saddle point representations
We consider the problem of efficient simulation estimation of the density
function at the tails, and the probability of large deviations for a sum of
independent, identically distributed, light-tailed and non-lattice random
vectors. The latter problem besides being of independent interest, also forms a
building block for more complex rare event problems that arise, for instance,
in queueing and financial credit risk modelling. It has been extensively
studied in literature where state independent exponential twisting based
importance sampling has been shown to be asymptotically efficient and a more
nuanced state dependent exponential twisting has been shown to have a stronger
bounded relative error property. We exploit the saddle-point based
representations that exist for these rare quantities, which rely on inverting
the characteristic functions of the underlying random vectors. These
representations reduce the rare event estimation problem to evaluating certain
integrals, which may via importance sampling be represented as expectations.
Further, it is easy to identify and approximate the zero-variance importance
sampling distribution to estimate these integrals. We identify such importance
sampling measures and show that they possess the asymptotically vanishing
relative error property that is stronger than the bounded relative error
property. To illustrate the broader applicability of the proposed methodology,
we extend it to similarly efficiently estimate the practically important
expected overshoot of sums of iid random variables
Efficient simulation of large deviation events for sums of random vectors using saddle-point representations
We consider the problem of efficient simulation estimation of the
density function at the tails, and the probability of large
deviations for a sum of independent, identically distributed (i.i.d.),
light-tailed and nonlattice random vectors. The latter problem
besides being of independent interest, also forms a building block
for more complex rare event problems that arise, for instance, in
queuing and financial credit risk modeling. It has been extensively
studied in the literature where state-independent, exponential-twisting-based
importance sampling has been shown to be asymptotically
efficient and a more nuanced state-dependent exponential twisting
has been shown to have a stronger bounded relative error property.
We exploit the saddle-point-based representations that exist for
these rare quantities, which rely on inverting the characteristic
functions of the underlying random vectors. These representations
reduce the rare event estimation problem to evaluating certain
integrals, which may via importance sampling be represented as
expectations. Furthermore, it is easy to identify and approximate the
zero-variance importance sampling distribution to estimate these
integrals. We identify such importance sampling measures and show
that they possess the asymptotically vanishing relative error
property that is stronger than the bounded relative error
property. To illustrate the broader applicability of the proposed
methodology, we extend it to develop an asymptotically vanishing
relative error estimator for the practically important expected
overshoot of sums of i.i.d. random variables
Capital allocation for credit portfolios with kernel estimators
Determining contributions by sub-portfolios or single exposures to
portfolio-wide economic capital for credit risk is an important risk
measurement task. Often economic capital is measured as Value-at-Risk (VaR) of
the portfolio loss distribution. For many of the credit portfolio risk models
used in practice, the VaR contributions then have to be estimated from Monte
Carlo samples. In the context of a partly continuous loss distribution (i.e.
continuous except for a positive point mass on zero), we investigate how to
combine kernel estimation methods with importance sampling to achieve more
efficient (i.e. less volatile) estimation of VaR contributions.Comment: 22 pages, 12 tables, 1 figure, some amendment
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