IRIS Scuola Normale Superiorehttps://ricerca.sns.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Sat, 16 Oct 2021 03:23:50 GMT2021-10-16T03:23:50Z10291A Stochastic Volatility Model With Realized Measures for Option Pricinghttp://hdl.handle.net/11384/83006Titolo: A Stochastic Volatility Model With Realized Measures for Option Pricing
Abstract: Based on the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both observed returns and realized measures to the latent conditional variance. A semi-analytical option pricing framework is developed for this class of models. In addition, we provide analytical filtering and smoothing recursions for the basic specification of the model, and an effective MCMC algorithm for its richer variants. The empirical analysis shows the effectiveness of filtering and smoothing realized measures in inflating the latent volatility persistence—the crucial parameter in pricing Standard and Poor’s 500 Index options.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/11384/830062019-01-01T00:00:00ZA Statistical Physics Approach to Quantitative Financehttp://hdl.handle.net/11384/10747Titolo: A Statistical Physics Approach to Quantitative Finance
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/11384/107472007-01-01T00:00:00ZA backward Monte Carlo approach to exotic option pricinghttp://hdl.handle.net/11384/90738Titolo: A backward Monte Carlo approach to exotic option pricing
Abstract: We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing exotic options. The new algorithm relies on the construction of a discrete multinomial tree. The crucial feature of our approach is that – in a similar spirit to the Brownian Bridge – each random path runs backward from a terminal fixed point to the initial spot price. We characterize the tree in two alternative ways: (i) in terms of the optimal grids originating from the Recursive Marginal Quantization algorithm, (ii) following an approach inspired by the finite difference approximation of the diffusion's infinitesimal generator. We assess the reliability of the new methodology comparing the performance of both approaches and benchmarking them with competitor Monte Carlo methods.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/11384/907382017-01-01T00:00:00ZA stochastic volatility framework with analytical filteringhttp://hdl.handle.net/11384/90802Titolo: A stochastic volatility framework with analytical filtering
Abstract: Motivated by the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both the observed returns and realized measures to the latent conditional variance.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/11384/908022017-01-01T00:00:00ZComment on: Price Discovery in High Resolutionhttp://hdl.handle.net/11384/83609Titolo: Comment on: Price Discovery in High Resolution
Abstract: This note is commenting on Hasbrouck (2018). The paper investigates the problem of price discovery on markets with trades recorded at sub-millisecond frequencies. The application of the popular information share measure of Hasbrouck (1995) to such data faces several difficulties, as the underlying vector error correction models would need a huge number of lags to capture dynamics at different time-scales. The problem is handled by imposing a set of restrictions on parameters inspired by the HAR model for realized volatility. We illustrate some potential drawbacks of the information share measure adopted in the paper and propose a modeling strategy aimed at dealing with such limitations. In particular, we introduce a structural multi-market model with a lagged adjustment mechanism describing lagged absorption of information across markets. The advantages of themethod are shown in simulations.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/11384/836092019-01-01T00:00:00ZEstimating Value-at-Risk with Product Partition Modelshttp://hdl.handle.net/11384/10458Titolo: Estimating Value-at-Risk with Product Partition Models
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/11384/104582009-01-01T00:00:00ZPath integrals and exotic options: Methods and numerical resultshttp://hdl.handle.net/11384/10684Titolo: Path integrals and exotic options: Methods and numerical results
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/11384/106842005-01-01T00:00:00ZModelling systemic cojumps with Hawkes factor modelshttp://hdl.handle.net/11384/3967Titolo: Modelling systemic cojumps with Hawkes factor models
Abstract: Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/11384/39672013-01-01T00:00:00ZMulti-curve HJM modelling for risk managementhttp://hdl.handle.net/11384/58681Titolo: Multi-curve HJM modelling for risk management
Abstract: We present a HJM approach to the projection of multiple yield curves
developed to capture the volatility content of historical term structures for
risk management purposes. Since we observe the empirical data at daily
frequency and only for a finite number of time-to-maturity buckets, we propose
a modelling framework which is inherently discrete. In particular, we show how
to approximate the HJM continuous time description of the multi-curve dynamics
by a Vector Autoregressive process of order one. The resulting dynamics lends
itself to a feasible estimation of the model volatility-correlation structure
and market risk-premia. Then, resorting to the Principal Component Analysis we
further simplify the dynamics reducing the number of covariance components.
Applying the constant volatility version of our model on a sample of curves
from the Euro area, we demonstrate its forecasting ability through an
out-of-sample test.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/11384/586812014-01-01T00:00:00ZValue Matters: Predictability of Stock Index Returnshttp://hdl.handle.net/11384/3976Titolo: Value Matters: Predictability of Stock Index Returns
Abstract: We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends on three components: i) a momentum component, naturally justified in terms of agents' belief that expected returns are higher in bullish markets than in bearish ones, ii) a fundamental component proportional to the logarithmic CAPE at time zero. The initial value of the ratio determines the reference growth level, from which the actual stock price may deviate as an effect of random external disturbances, and iii) a driving component which ensures the diffusive behaviour of stock prices. Under these assumptions, we prove that for a sufficiently large horizon the expected rate of return and the expected gross return are linear in the initial logarithmic CAPE, and their variance goes to zero with a rate of convergence consistent with the diffusive behaviour. Eventually this means that the momentum component may generate bubbles and crashes in the short and medium run, nevertheless the valuation ratio remains a good reference point of future long-run returns.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/11384/39762012-01-01T00:00:00Z