TY - JOUR
TI - Volatility Forecasting: Downside Risk, Jumps and Leverage Effect
AU - Audrino, F.
PY - 2016
N2 - We provide empirical evidence on volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. Using recently developed methodologies to detect jumps from high frequency price data, we estimate the size of positive and negative jumps and propose a methodology to estimate the size of jumps in the quadratic variation. The leverage effect is separated into continuous and discontinuous effects and past volatility is separated into ``good" and ``bad" as well as into continuous and discontinuous risks. Using a long history of the S\&P500 price index, we find that the continuous leverage effect lasts about one week while the discontinuous leverage effect disappears after one day. ``Good" and ``bad" continuous risks both characterize the volatility persistence while ``bad" jump risk is much more informative than ``good" jump risk in forecasting future volatility. The volatility forecasting model proposed is able to capture many empirical stylized facts while still remaining parsimonious in terms of the number of parameters to be estimated.
PB - MDPI AG
SN - 2225-1146
JF - Econometrics
VL - forthcoming
SP - 1
EP - 1
ER -
TY - UNPB
TI - Testing the lag structure of assets' realized volatility dynamics
AU - Audrino, F.
AU - Camponovo, L.
AU - Roth, C.
PY - 2015
N2 - A (conservative) test is constructed to investigate the optimal lag structure for forecasting realized volatility dynamics. The testing procedure relies on the recent theoretical results that show the ability of the adaptive least absolute shrinkage and selection operator (adaptive lasso) to combine efficient parameter estimation, variable selection, and valid inference for time series processes. In an application to several constituents of the S\&P 500 index it is shown that (i) the optimal significant lag structure is time-varying and subject to drastic regime shifts that seem to happen across assets simultaneously; (ii) in many cases the relevant information for prediction is included in the first 22 lags, corroborating previous results concerning the accuracy and the difficulty of outperforming out-of-sample the heterogeneous autoregressive (HAR) model; and (iii) some common features of the optimal lag structure can be identified across assets belonging to the same market segment or showing a similar beta with respect to the market index.
PB - SEPS Discussion Paper Series
CY - University of St. Gallen
ER -
TY - JOUR
TI - Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation
AU - Corsi, F.
AU - Peluso, S.
AU - Audrino, F.
PY - 2015
N2 - Motivated by the need of a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a covariance matrix estimate which is robust to both asynchronicity and microstructure noise, and positive-semidefinite by construction. We show the performance of the KEM estimator using extensive Monte Carlo simulations that mimic the liquidity and market microstructure characteristics of the S&P 500 universe as well as in an high-dimensional application on US stocks. KEM provides very accurate covariance matrix estimates and significantly outperforms alternative approaches recently introduced in the literature.
PB - Wiley-Blackwell
CY - Chichester
SN - 0883-7252
JF - Journal of Applied Econometrics
VL - 3
IS - 30
SP - 377
EP - 397
ER -
TY - JOUR
TI - Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data
AU - Audrino, F.
AU - Fengler, M.
PY - 2015
N2 - As a means of validating an option pricing model, we compare the ex-post intra-day realized variance of options with the realized variance of the associated underlying asset that would be implied using assumptions as in the Black and Scholes (BS) model, the Heston and the Bates model. Based on data for the S&P 500 index, we find that the BS model is strongly directionally biased due to the presence of stochastic volatility. The Heston model reduces the mismatch in realized variance between the two markets, but deviations are still significant. With the exception of short-dated options, we achieve best approximations after controlling for the presence of jumps in the underlying dynamics. Finally, we provide evidence that, although heavily biased, the realized variance based on the BS model contains relevant predictive information that can be exploited when option high-frequency data is not available.
PB - Elsevier
CY - Amsterdam
SN - 0378-4266
JF - Journal of Banking and Finance
VL - -
IS - 61
SP - 46
EP - 63
ER -
TY - UNPB
TI - An Empirical Analysis of the Ross Recovery Theorem
AU - Audrino, F.
AU - Huitema, R.
AU - Ludwig, M. .
PY - 2014
N2 - Building on the results of Ludwig (2012), we propose a method to construct robust time-homogeneous Markov chains that capture the risk-neutral transition of state prices from current snapshots of option prices on the S&P 500 index. Using the recovery theorem of Ross (2013), we then derive the market’s forecast of the real-world return density and investigate the predictive information content of its moments. We find that changes in the recovered moments can be used to time the index, yielding strategies that not only outperform the market, but are also significantly less volatile.
PB - SEPS Economic Working Paper Series
VL - 1411
ER -
TY - JOUR
TI - Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics
AU - Audrino, F.
AU - Knaus, S.
PY - 2014
N2 - Realized volatility computed from high-frequency data is an important measure for many applications in finance and its dynamics have been widely investigated. Recent notable advances that perform well include the heterogeneous autoregressive (HAR) model which can approximate long memory, is very parsimonious, is easy to estimate, and features good out-of-sample performance.
We prove that the least absolute shrinkage and selection operator (lasso) recovers the lags structure of the HAR model asymptotically if it is the true model, and we present Monte Carlo evidence in finite samples. The HAR model's lags structure is not fully in agreement with the one found using the lasso on real data. Moreover, we provide empirical evidence that there are two clear breaks in structure for most of the assets we consider. These results bring into question the appropriateness of the HAR model for realized volatility. Finally, in an out-of-sample analysis we show equal performance of the HAR model and the lasso approach.
PB - Taylor & Francis
CY - New York
SN - 0747-4938
JF - Econometric Reviews
VL - forthcoming
SP - 1
EP - 1
ER -
TY - JOUR
TI - Monetary policy regimes: implications for the yield curve and bond pricing
AU - Filipova, K.
AU - Audrino, F.
AU - De Giorgi, E.
PY - 2014
N2 - We develop a multivariate dynamic term structure model, which takes into account the nonlinear (time-varying) relationship between interest rates and the state of the economy. In contrast to the classical term structure literature, where nonlinearities are captured by increasing the number of latent state variables, or by latent regime shifts, in our no-?? 1/2 arbitrage framework the regimes are governed by thresholds and are directly linked to economic fundamentals. Specifically, starting from a simple monetary policy model for the short rate, we introduce a parsimonious and tractable model for the yield curve, which takes into account the possibility of regime shifts in the behavior of the Federal Reserve. In our empirical analysis, we show the merit of our approach along the following dimensions: (i) interpretable bond dynamics; (ii) accurate short end yield curve pricing; (iii) yield curve implications.
PB - Elsevier
CY - Amsterdam
SN - 0304-405X
JF - Journal of Financial Economics
VL - 113
IS - 3
SP - 427
EP - 454
ER -
TY - JOUR
TI - Bond Risk Premia Forecasting: A Simple Approach for Extracting Macroeconomic Information from a Panel of Indicators
AU - Audrino, F.
AU - Corsi, F.
AU - Filipova, K.
PY - 2014
N2 - We propose a simple but effective estimation procedure to extract the level and the volatility dynamics of a latent macroeconomic factor from a panel of observable indicators. Our approach is based on a multivariate conditionally heteroscedastic exact factor model that can take into account the heteroscedasticity feature shown by most macroeconomic variables and relies on an iterated Kalman filter procedure. In simulations we show the unbiasedness of the proposed estimator and its superiority to different approaches introduced in the literature. Simulation results are confirmed in applications to real inflation data with the goal of forecasting long-term bond risk premia. Moreover, we find that the extracted level and conditional variance of the latent factor for inflation are strongly related to NBER business cycles.
PB - Taylor & Francis
CY - Philadelphia
SN - 0747-4938
JF - Econometric Reviews
VL - online seit 08.13 - forthcoming
IS - 2014
SP - 1
EP - 43
ER -
TY - JOUR
TI - Forecasting correlations during the late-2000s financial crisis: The short-run component, the long-run component, and structural breaks
AU - Audrino, F.
PY - 2014
N2 - The predictive power of recently introduced components affecting correlations is investigated. The focus is on models allowing for a flexible specification of the short-run component of correlations as well as the long-run component. Moreover, models allowing the correlation dynamics to be subjected to regime-shift caused by threshold-based structural breaks of a different nature are also considered. The results indicate that in some cases there may be a superimposition of the long-term and short-term movements in correlations. Therefore, care is called for in interpretations when estimating the two components. Testing the forecasting accuracy of correlations during the late-2000s financial crisis yields mixed results. In general, component models allowing for a richer correlation specification possess an increased predictive accuracy. Economically speaking, no relevant gains are found by allowing for more flexibility in the correlation dynamics.
PB - Elsevier Science
CY - Amsterdam
SN - 0167-9473
JF - Computational Statistics & Data Analysis
VL - August 2014
IS - 76
SP - 43
EP - 60
ER -
TY - UNPB
TI - Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models
AU - Audrino, F.
AU - Camponovo, L.
PY - 2013
N2 - We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso model for some fixed value of the shrinkage parameter. Central in this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end we construct a simple (conservative) testing procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso combines efficient parameter estimation, variable selection, and valid finite sample inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the introduced testing procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) to the classic Taylor rule monetary policy model.
PB - SEPS Discussion paper series
ER -
TY - UNPB
TI - Monetary Policy Regimes: Implications for the Yield Curve and Bond Pricing
AU - Filipova, K.
AU - Audrino, F.
AU - De Giorgi, E.
PY - 2013
N2 - We develop a multivariate dynamic term structure model, which takes into account the nonlinear (time-varying) relationship between interest rates and the state of the economy. In contrast to the classical term structure literature, where nonlinearities are captured by increasing the number of latent state variables, or by latent regime shifts, in our no-arbitrage framework the regimes are governed by thresholds and are directly linked to different economic fundamentals. Specifically, starting from a simple monetary policy model for the short rate, we introduce a parsimonious and tractable model for the yield curve, which takes into account the possibility of regime shifts in the behavior of the Federal Reserve. In our empirical analysis, we show the merit of our approach along four dimensions: (i) interpretable bond dynamics; (ii) accurate short end yield curve pricing; (iii) yield curve implications; (iv) superior out-of-sample short rate forecasting performance.
PB - http://ssrn.com/abstract=2232742
ER -
TY - UNPB
TI - Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics
AU - Audrino, F.
AU - Knaus, S.
PY - 2012
N2 - Realized volatility computed from high-frequency data is an important measure for many applications in finance. However, its dynamics are not well understood to date. Recent notable advances that perform well include the heterogeneous autoregressive (HAR) model which is economically interpretable and but still easy to estimate. It also features good out-of-sample performance and has been extremely well received by the research community. We present a data driven approach based on the absolute shrinkage and selection operator (lasso) which should identify the aforementioned model. We prove that the lasso indeed recovers the HAR model asymptotically if it is the true model, and we present Monte Carlo evidence in finite sample. The HAR model is not recovered by the lasso on real data. This, together with an empirical out-of-sample analysis that shows equal performance of the HAR model and the lasso approach, leads to the conclusion that the HAR model may not be the true model but it captures a linear footprint of the true volatility dynamics.
T3 - Discussion paper series
PB - SEPS
CY - St. Gallen
VL - 1224
ER -
TY - JOUR
TI - Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects
AU - Audrino, F.
AU - Corsi, F.
PY - 2012
N2 - This paper presents two classes of tick-by-tick covariance estimators adapted to the case of rounding in the price time stamps to a frequency lower than the typical arrival rate of tick prices. Through Monte Carlo simulations we investigate the behavior of such estimators under realistic market microstructure conditions analogous to those of the financial data examined in this paper’s empirical section, that is, non-synchronous trading, general ARMA structure for microstructure noise, and true lead-lag cross-covariance.
Simulation results show the robustness of the proposed tick-by-tick covariance estimators to time stamp rounding, and their overall performance is superior to competing covariance estimators under empirically realistic microstructure conditions. These results are confirmed in the empirical application where the economic benefits of the proposed estimators are evaluated with volatility timing strategies applied to a bivariate portfolio of S&P 500 futures and 30-year US treasury bond futures.
PB - Oxford Journals
CY - Oxford UK
SN - 1479-8409
JF - Journal of Financial Econometrics
VL - 4
IS - 10
SP - 591
EP - 616
ER -
TY - CHAP
TI - HAR Modeling for Realized Volatility Forecasting
T2 - Handbook of Volatility Models and their Applications
AU - Audrino, F.
AU - Corsi, F.
AU - Reno, R.
PY - 2012
T3 - Wiley handbooks in financial engineering and econometrics
PB - Wiley
CY - Hoboken, N.J.
SN - 978-0-470-87251-2
SP - 363
EP - 382
ER -
TY - UNPB
TI - Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation
AU - Audrino, F.
AU - Corsi, F.
AU - Peluso, S.
PY - 2012
N2 - Motivated by the need for an unbiased and positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and Expectation Maximization (KEM) algorithm. In the expectation step, by means of the Kalman filter with missing data, we reconstruct the smoothed and synchronized series of the latent price processes. In the maximization step, we search for covariance matrices that maximize the expected likelihood obtained with the reconstructed price series. Iterating between the two EM steps, we obtain a KEM-improved covariance matrix estimate which is robust to both asynchronicity and microstructure noise, and positive-semidefinite by construction.
Extensive Monte Carlo simulations show the superior performance of the KEM estimator over several alternative covariance matrix estimates introduced in the literature. The application of the KEM estimator in practice is illustrated on a 10-dimensional US stock data set.
PB - SEPS Working Paper Series
CY - St. Gallen
ER -
TY - JOUR
TI - What drives short rate dynamics? A functional gradient descent approach
AU - Audrino, F.
PY - 2012
N2 - Functional gradient descent, a recent technique coming from computational statistics, is applied to the estimation of the conditional moments of the short rate process with the goal of finding the main drivers of the drift and volatility dynamics. Functional gradient descent can improve the accuracy of some reasonable starting estimates obtained using classical short rate models introduced in the literature. It exploits the predictive information of an enlarged set of variables, including yields at other maturities, time, and macroeconomic indicators. Fitting this methodology to the time series of monthly US 3-month Treasury bill rates, we find that the drift dynamics react mostly in a non-linear way to changes in macroeconomic variables, whereas volatility dynamics are subjected to time-dependent regime-switches. Finally we show the superior performance of the final predictions obtained by applying functional gradient descent in a forecasting exercise.
PB - Springer
CY - US
SN - 0927-7099
JF - Computational Economics
VL - 3
IS - 39
SP - 315
EP - 335
ER -
TY - JOUR
TI - Option strategies based on semi-parametric implied volatility surface prediction
AU - Audrino, F.
AU - Colangelo, D.
PY - 2011
N2 - We investigate whether a more sophisticated technique able to forecast accurately the future movements of the implied volatility surface may help in improving the performance of basic option strategies. To this goal we construct a set of strategies using predicted option returns for a forecasting period of ten trading days and form profitable hold-to-expiration, equally weighted, zero-cost portfolios with one month at-the-money options. The accurate predictions of the implied volatility surface dynamics are obtained using a statistical machine learning procedure based on regression trees. These forecasts assist in obtaining reliable option returns used as trading signals in our strategies. We test the performance of the proposed strategies on options on the S\&P100 and on its constituents between 2002 and 2006 getting positive annualized returns of up to more than 50\%. Comparing such performance to the ones obtained without using any complex model for the implied volatility surface we show that in most cases differences are small.
PB - Incisive Media
CY - London
SN - 1460-1559
JF - Journal of Investment Strategies
VL - 1
IS - 1
SP - 3
EP - 41
ER -
TY - UNPB
TI - Forecasting correlations during the late-2000s financial crisis: short-run component, long-run component, and structural breaks
AU - Audrino, F.
PY - 2011
N2 - We empirically investigate the predictive power of the various components affecting correlations that have been recently introduced in the literature. We focus on models allowing for a flexible specification of the short-run component of correlations as well as the long-run component. Moreover, we also allow the correlation dynamics to be subjected to regime-shift caused by threshold-based structural breaks of a different nature. Our results indicate that in some cases there may be a superimposition of the long- and short-term movements in correlations. Therefore, care is called for in interpretations when estimating the two components. Testing the forecasting accuracy of correlations during the late-2000s financial crisis yields mixed results. In general component models allowing for a richer correlation specification possess an increased predictive accuracy. Economically speaking, no relevant gains are found by allowing for more flexibility in the correlation dynamics.
T3 - Discussion papers in economics
PB - SEPS-UNISG
VL - 2011-12
ER -
TY - JOUR
TI - Modeling and forecasting short-term interest rates: The benefits of smooth regimes, macroeconomic variables, and bagging
AU - Audrino, F.
AU - Medeiros, M. C.
PY - 2011
N2 - In this paper we propose a smooth transition tree model for both the
conditional mean and variance of the short-term interest rate process. The estimation of such models is addressed and the asymptotic properties of the quasi-maximum likelihood estimator are derived. Model specification is also discussed. When the model is applied to the US short-term interest rate we find (1) leading indicators for inflation and real activity are the most relevant predictors in characterizing the multiple regimes' structure; (2) the optimal model has three limiting regimes. Moreover, we provide empirical evidence of the power of the model in forecasting the first two conditional moments when it is used in connection with bootstrap aggregation (bagging).
PB - Wiley
CY - Chichester UK
SN - 0883-7252
JF - Journal of Applied Econometrics
VL - 6
IS - 26
SP - 999
EP - 1022
ER -
TY - JOUR
TI - A General Multivariate Threshold GARCH Model for Dynamic Correlations
AU - Audrino, F.
AU - Trojani, F.
PY - 2011
N2 - We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step
estimation procedure that is feasible in large dimensional applications. Optimal threshold functions are estimated endogenously from the data, and the model conditional covariance matrix is ensured to be positive definite. We study the empirical performance of our model in two applications using US stock and bond market data. In both applications our model has, in terms of statistical and economic significance, higher forecasting power than several other multivariate GARCH models for conditional correlations
PB - Taylor & Francis
CY - Abingdon UK
SN - 0735-0015
JF - Journal of Business and Economic Statistics
VL - 1
IS - 29
SP - 138
EP - 149
ER -
TY - UNPB
TI - Bond Risk Premia Forecasting: A Simple Approach for Extracting Macroeconomic Information from a Panel of Indicators
AU - Audrino, F.
PY - 2010
N2 - http://ideas.repec.org/p/usg/dp2010/2010-09.html
T3 - VWA Discussion Paper Series
PB - Economic Deparment, University of St. Gallen
ER -
TY - JOUR
TI - Modeling tick-by-tick realized correlations
AU - Audrino, F.
AU - Corsi, F.
PY - 2010
N2 - A tree-structured heterogeneous autoregressive (tree-HAR) process is
proposed as a simple and parsimonious model for the estimation and prediction of tick-by-tick realized correlations. The model can account for different time and other relevant predictors' dependentregime shifts in the conditional mean dynamics of the realized correlation series. Testing the model on S&P 500 Futures and 30-year Treasury Bond Futures realized correlations, empirical evidence that the tree-HAR model reaches a good compromise between simplicity and flexibility is provided. The model yields accurate single- and multi-step out-of-sample forecasts. Such forecasts are also better than those obtained from other standard approaches, in particular when the final goal is multi-period forecasting
PB - Elsevier Science
CY - Amsterdam
SN - 0167-9473
JF - Computational Statistics and Data Analysis
VL - 11
IS - 54
SP - 2372
EP - 2382
ER -
TY - JOUR
TI - Semi-parametric forecasts of the implied volatility surface using regression trees
AU - Audrino, F.
AU - Colangelo, D.
PY - 2010
N2 - We present a new semi-parametric model for the prediction of implied volatility surfaces that can be estimated using machine learning algorithms. Given a reasonable starting model, a boosting algorithm based on regression trees sequentially minimizes generalized residuals computed as differences between observed and estimated implied volatilities. To overcome the poor predictive power of existing models, we include a grid in the region of interest, and implement a cross-validation strategy to find an optimal stopping value for the boosting procedure. Back testing the out-of-sample performance on a large data set of implied volatilities from S&P 500 options, we provide empirical evidence of the strong predictive power of our model.
PB - Springer Science
CY - Dordrecht
SN - 0960-3174
JF - Statistics and Computing
VL - 4
IS - 20
SP - 421
EP - 434
ER -
TY - UNPB
TI - Yield Curve Predictability, Regimes, and Macroeconomic Information: A Data-Driven Approach
AU - Audrino, F.
AU - Filipova, K.
PY - 2009
N2 - http://ideas.repec.org/p/usg/dp2009/2009-10.html
T3 - University of St. Gallen Department of Economics working paper series 2009
PB - University of St. Gallen
CY - St. Gallen
VL - 2009-10
ER -
TY - JOUR
TI - Splines for Financial Volatility
AU - Audrino, F.
AU - Bühlmann, P.
PY - 2009
PB - Wiley
CY - Hoboken
SN - 1369-7412
JF - Journal of the Royal Statistical Society, Series B
VL - 3
IS - 71
SP - 655
EP - 670
ER -
TY - UNPB
TI - Smooth Regimes, Macroeconomic Variables, and Bagging for the Short-Term Interest Rate Process: Discussion papers
AU - Audrino, F.
AU - Madeiros, M. C.
PY - 2008
N2 - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1233942
http://ideas.repec.org/p/usg/dp2008/2008-16.html
T3 - 2008
PB - Volkswirtschaftliche Abteilung Universität St. Gallen
CY - St. Gallen
ER -
TY - UNPB
TI - Modeling Tick-by-Tick Realized Correlations: Discussion Papers
AU - Audrino, F.
AU - Corsi, F.
PY - 2008
N2 - http://ideas.repec.org/p/usg/dp2008/2008-05.html
T3 - 2008
PB - Volkswirtschaftliche Abteilung Universität St. Gallen
CY - St. Gallen
ER -
TY - UNPB
TI - Realized Covariance Tick-by-Tick in Presence of Rounded Time Stamps and General Microstructure Effects: Discussion papers
AU - Audrino, F.
AU - Corsi, F.
PY - 2008
N2 - http://ideas.repec.org/p/usg/dp2008/2008-04.html
T3 - 2008
PB - Volkswirtschaftliche Abteilung Universität St. Gallen
CY - St. Gallen
ER -
TY - UNPB
TI - Forecasting Implied Volatility Surfaces
AU - Audrino, F.
AU - Colangelo, D.
PY - 2007
N2 - http://ideas.repec.org/p/usg/dp2007/2007-42.html
T3 - VWA Discussion Papers Series
PB - University of St. Gallen
ER -
TY - UNPB
TI - Forecasting Implied Volatility Surfaces
AU - Audrino, F.
AU - Colangelo, D.
PY - 2007
N2 - http://ideas.repec.org/p/usg/dp2007/2007-42.html
PB - 2007-42, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - UNPB
TI - A general multivariate threshold GARCH model with dynamic conditional correlations (Revised Version of Paper no. 2005-04)
UR - https://www.alexandria.unisg.ch/publications/41447
AU - Audrino, F.
AU - Trojani, F.
PY - 2007
N2 - http://ideas.repec.org/p/usg/dp2005/2005-04.html
PB - 2007-25, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - UNPB
TI - Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent
AU - Audrino, F.
AU - Trojani, F.
PY - 2007
N2 - http://ideas.repec.org/p/usg/dp2007/2007-24.html
PB - 2007-24, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - UNPB
TI - Splines for Financial Volatility
AU - Audrino, F.
AU - Bühlmann, P.
PY - 2007
N2 - http://ideas.repec.org/p/usg/dp2007/2007-11.html
PB - 2007-11, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - UNPB
TI - Realized Correlation Tick-by-Tick
AU - Audrino, F.
AU - Corsi, F.
PY - 2007
N2 - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=957997
PB - 2007-02, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - JOUR
TI - Accurate Short-Term Yield Curve Forecasting using Functional Gradient Descent
AU - Audrino, F.
AU - Trojani, F.
PY - 2007
N2 - We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for nonlinearities in the dependence of interest rates at all available maturities. Based on FGD we apply filtered historical simulation to compute reliable out-of-sample yield curve scenarios and confidence intervals. We back-test our methodology on daily USD bond data for forecasting horizons from 1 to 10 days. Based on several statistical performance measures we find significant evidence of a higher predictive power of our method when compared to scenarios generating techniques based on (i) factor analysis, (ii) a multivariate CCC-GARCH model, or (iii) an exponential smoothing covariances estimator as in the RiskMetricsTM approach.
PB - University Press
CY - Oxford
SN - 1479-8409
JF - Journal of Financial Econometrics
VL - 4
IS - 5
SP - 591
EP - 623
ER -
TY - JOUR
TI - Beta regimes for the Yield Curve
AU - Audrino, F.
AU - De Giorgi, E.
PY - 2007
N2 - We propose an affine term structure model which accommodates nonlinearities in the drift and volatility function of the short-term interest rate. Such nonlinearities are a consequence of discrete beta-distributed regime shifts constructed on multiple thresholds. We derive iterative closed-form formula for the whole yield curve dynamics that can be estimated using a linearized Kalman filter. Fitting the model on US data, we collect empirical evidence of its potential in estimating conditional volatility and correlation across yields.
PB - Oxford University Press
CY - Oxford
SN - 1479-8409
JF - Journal of Financial Econometrics
VL - 3
IS - 5
SP - 456
EP - 490
ER -
TY - JOUR
TI - A Forecasting Model for Stock Market Diversity
AU - Audrino, F.
AU - Fernholz, R.
AU - Ferretti, R.
PY - 2007
N2 - We apply the recently introduced generalized tree-structured (GTS) model to the analysis and forecast of stock market diversity. Diversity is a measure of capital concentration across a market that plays a central role in the search for arbitrage. The GTS model allows for different conditional mean and volatility regimes that are directly related to the behavior of macroeconomic fundamentals through a binary threshold construction. Testing on US market data, we collect empirical evidence of the model's strong potential in estimating and forecasting diversity accurately in comparison with other standard approaches. In addition, the GTS model allows for the construction of very simple portfolio strategies that systematically beat the standard cap-weighted S&P500 index.
PB - Springer
CY - Berlin
SN - 1614-2446
JF - Annals of Finance
VL - 2
IS - 3
SP - 213
EP - 240
ER -
TY - JOUR
TI - Average Conditional Correlation and Tree Structures for Multivariate GARCH Models
AU - Audrino, F.
AU - Barone Adesi, G.
PY - 2006
N2 - We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions without resorting to any variance reduction technique. We back-test the models on a six-dimensional exchange-rate time series using different goodness-of-fit criteria and statistical tests. We collect empirical evidence of their strong predictive power, also in comparison to alternative benchmark procedures.
PB - Wiley
CY - Chichester
SN - 0277-6693
JF - Journal of Forecasting
VL - 8
IS - 25
SP - 579
EP - 600
ER -
TY - JOUR
TI - A dynamic model of expected bond returns: A functional gradient descent approach
AU - Audrino, F.
AU - Barone Adesi, G.
PY - 2006
N2 - A multivariate methodology based on functional gradient descent to estimate and forecast time-varying expected bond returns is presented and discussed. Backtesting this procedure on US monthly data, empirical evidence of its strong forecasting potential in terms of the accuracy of the predictions is collected. The proposed methodology clearly outperforms the classical univariate analysis used in the literature.
PB - Elsevier Science
CY - Amsterdam
SN - 0167-9473
JF - Computational Statistics & Data Analysis
VL - 4
IS - 51
SP - 2267
EP - 2277
ER -
TY - JOUR
TI - Tree-structured multiple regimes in interest rates
AU - Audrino, F.
PY - 2006
N2 - This article develops a generalized tree-structured (GTS) model of the short-term interest rate that accommodates regime-dependent mean reversion and regime-dependent volatility clustering and level effects in the conditional variance. The model is constructed using the idea of multivariate tree-structured thresholds and nests the popular generalized autoregressive conditional heteroscedasticity and square root processes as simple special cases. It allows us to estimate the optimal number of regimes endogenously from the data and to exploit possible additional information in the term structure and in other macroeconomic variables. We provide empirical evidence of the strong potential of the GTS model in forecasting conditional first and second moments, also in comparison with alternative models of the short rate.
PB - Taylor & Francis
CY - Alexandria
SN - 0735-0015
JF - Journal of Business & Economic Statistics
VL - 3
IS - 24
SP - 338
EP - 353
ER -
TY - JOUR
TI - Estimating and predicting multivariate volatility thresholds in global stock markets
AU - Audrino, F.
AU - Trojani, F.
PY - 2006
PB - JSTOR
CY - Chichester
SN - 0883-7252
JF - Journal of Applied Econometrics
VL - 3
IS - 21
SP - 345
EP - 369
ER -
TY - JOUR
TI - The impact of general non-parametric volatility functions in multivariate GARCH models
AU - Audrino, F.
PY - 2006
N2 - Recent studies have revealed that financial volatilities and correlations move together over time across assets and markets. The main effort has been on improving the flexibility of conditional correlation dynamics, while maintaining computational feasibility for large estimation problems. However, since in such models conditional covariances are the product of conditional correlations and individual volatilities, it is plausible that improving the estimation of individual volatilities will lead to better covariance forecasts, too. Functional gradient descent (FGD) has already been shown to improve substantially in-sample and out-of-sample covariance accuracy in the very simple constant conditional correlation (CCC) setting. Following this direction, the impact of FGD volatility estimates is tested in several multivariate GARCH settings, both at the multivariate and at the univariate portfolio levels. In particular, improving conditional correlations and improving individual volatilities are compared, to establish which effect produces the best fits and predictions for conditional covariances.
PB - Elsevier Science
CY - Amsterdam
SN - 0167-9473
JF - Computational Statistics & Data Analysis
VL - 11
IS - 50
SP - 3032
EP - 3052
ER -
TY - UNPB
TI - A general multivariate threshold GARCH model with dynamic conditional correlations
AU - Audrino, F.
AU - Trojani, F.
PY - 2005
N2 - http://papers.ssrn.com/sol3/papers.cfm?abstract_id=487942
PB - 2005-04, VWA Discussion Papers Series, HSG St. Gallen
ER -
TY - JOUR
TI - Local Likelihood for non paramentric ARCH(1) models
AU - Audrino, F.
PY - 2005
N2 - We propose a non-parametric local likelihood estimator for the log-transformed autoregressive conditional heteroscedastic (ARCH) (1) model. Our non-parametric estimator is constructed within the likelihood framework for non-Gaussian observations: it is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and show, by a simulation experiment and some real-data examples, that the local likelihood estimator has better predictive potential than classical local regression. A possible extension of the estimation procedure to more general multiplicative ARCH(p) models with p > 1 predictor variables is also described.
PB - Blackwell
CY - Oxford
SN - 0143-9782
JF - Journal of Time Series Analysis
VL - 2
IS - 26
SP - 251
EP - 278
ER -
TY - JOUR
TI - A multivariate FGD technique to improve VaR computation in equity markets
AU - Audrino, F.
AU - Barone Adesi, G.
PY - 2005
N2 - It is difficult to compute Value-at-Risk (VaR) using multivariate models able to take into account the dependence structure between large numbers of assets and being still computationally feasible. A possible procedure is based on functional gradient descent (FGD) estimation for the volatility matrix in connection with asset historical simulation. Backtest analysis on simulated and real data provides strong empirical evidence of the better predictive ability of the proposed procedure over classical filtered historical simulation, with a resulting significant improvement in the measurement of risk.
PB - Springer
CY - Berlin
SN - 1619-697X
JF - Computational Management Science
VL - 2
IS - 2
SP - 87
EP - 106
ER -
TY - JOUR
TI - The stability of factor models of interest rates
AU - Audrino, F.
AU - Barone Adesi, G.
AU - Mira, A.
PY - 2005
N2 - The daily term structure of interest rates is filtered to reduce the influence of cross-correlations and autocorrelations on its factors. A three-factor model is fitted to the filtered data. We perform statistical tests, finding that factor loadings are unstable through time for daily data. This finding is not due to the presence of outliers nor to the selected number of factors. Such an instability problem can be solved when applying the factor analysis on multivariate scaled residuals, filtered using a nonparametric technique based on functional gradient descent.
PB - Oxford University Press
CY - Oxford
SN - 1479-8409
JF - Journal of Financial Econometrics
VL - 3
IS - 3
SP - 422
EP - 441
ER -
TY - JOUR
TI - Functional gradient descent for financial time series with an application to the measurement of market risk
AU - Audrino, F.
AU - Barone Adesi, G.
PY - 2005
N2 - The estimation and forecast of the volatility matrix are two of the main tasks of financial econometrics since they are essential ingredients in many practical applications. Unfortunately the use of classical multivariate methods in large dimensions is difficult because of the curse of dimensionality. We present a general semiparametric technique, based on functional gradient descent (FGD) and able to overcome most problems associated with a multivariate GARCH-type estimation. By testing the accuracy of the volatility estimates for the measurement of market risk on real data we provide empirical evidence of the strong predictive potential of the FGD approach, also in comparison to other standard methods.
PB - Elsevier
CY - Amsterdam
SN - 0378-4266
JF - Journal of Banking & Finance
VL - 4
IS - 29
SP - 959
EP - 977
ER -
TY - JOUR
TI - Synchronizing multivariate financial time series
AU - Audrino, F.
AU - Bühlmann, P.
PY - 2004
PB - Incisive Media Limited
CY - London
SN - 1465-1211
JF - Journal of Risk
VL - 2
IS - 6
SP - 81
EP - 106
ER -
TY - JOUR
TI - Volatility estimation with functional gradient descent for very high-dimensional financial time series
AU - Audrino, F.
AU - Bühlmann, P.
PY - 2003
PB - Incisive Media Limited
CY - London
SN - 1742–7185
JF - Journal of Computational Finance
VL - 3
IS - 6
SP - 65
EP - 89
ER -
TY - JOUR
TI - Tree-structured generalized autoregressive conditional heteroscedastic models
AU - Audrino, F.
AU - Bühlmann, P.
PY - 2001
N2 - We propose a new generalized autoregressive conditional heteroscedastic (GARCH) model with tree-structured multiple thresholds for the estimation of volatility in financial time series. The approach relies on the idea of a binary tree where every terminal node parameterizes a (local) GARCH model for a partition cell of the predictor space. The fitting of such trees is constructed within the likelihood framework for non-Gaussian observations: it is very different from the well-known regression tree procedure which is based on residual sums of squares. Our strategy includes the classical GARCH model as a special case and allows us to increase model complexity in a systematic and flexible way. We derive a consistency result and conclude from simulation and real data analysis that the new method has better predictive potential than other approaches.
PB - Royal Statistical Society
CY - London
SN - 1369-7412
JF - Journal of the Royal Statistical Society, Series B
VL - 4
IS - 63
SP - 727
EP - 744
ER -