Asymmetric stochastic volatility models

Mención Internacional en el título de doctorThis dissertation focuses on the analysis of Stochastic Volatility (SV) models with leverage effect. We propose a general family of asymmetric SV (GASV) models and consider in detail two particular specifications within this family. The first one is the Threshold GASV (T-GASV) model which nests some of the most famous asymmetric SV models available in the literature with the errors being either Normal or GED.We also propose score driven GASV models with different assumptions about the error distribution, namely the Normal, Student-t or GED distributions, where the volatility is driven by the score of the lagged return distribution conditional on the volatility. Closed-form expressions of some statistical moments of interest of these two GASV models are derived and analyzed. We show that some of the parameters of these models cannot be properly identified by the moments usually considered when describing the stylized facts of financial returns, namely, excess kurtosis, autocorrelations of squares and cross-correlations between returns and future squared returns. As a byproduct, we obtain the statistical properties of those nested popular asymmetric SV models, some of which were previously unknown in the literature. By comparing the properties of these models, we are able to establish the advantages and limitations of each of them and give some guidelines about which model to implement in practice. We also propose the Stochastic News Impact Surface (SNIS) to represent the asymmetric response of volatility to positive and negative shocks in the context of SV models. The SNIS is useful to show the added flexibility of SV models over GARCH models when representing conditionally heteroscedastic time series with leverage effect. Analyzing the SNIS, we find that the asymmetric impact of the level disturbance on the volatility can be different depending on the volatility disturbance. Finally, we analyze the finite sample properties of a MCMC estimator of the parameters and volatilities of some restricted GASV models. Furthermore, estimating the restricted T-GASV model using this MCMC estimator, we show that one can correctly identify the true nested specifications which are popularly implemented in empirical applications. All the results are illustrated by Monte Carlo experiments and by fitting the models to both daily and weekly financial returns.Programa Oficial de Doctorado en Economía de la Empresa y Métodos CuantitativosPresidente: Román Liesenfeld; Secretario: David Veredas; Vocal: Antonios Ntmo

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW

One for all : nesting asymmetric stochastic volatility models

This paper proposes a new stochastic volatility model to represent the dynamic evolution of conditionally heteroscedastic time series with leverage effect. Although there are already several models proposed in the literature with the same purpose, our main justification for a further new model is that it nests some of the most popular stochastic volatility specifications usually implemented to real time series of financial returns. We derive closed-form expressions of its statistical properties and, consequently, of those of the nested specifications. Some of these properties were previously unknown in the literature although the restricted models are often fitted by empirical researchers. By comparing the properties of the restricted models, we are able to establish the advantages and limitations of each of them. Finally, we analyze the performance of a MCMC estimator of the parameters and volatilities of the new proposed model and show that, if the error distribution is known, it has appropriate finite sample properties. Furthermore, estimating the new model using the MCMC estimator, one can correctly identify the restricted true specifications. All the results are illustrated by estimating the parameters and volatilities of simulated time series and of a series of daily S&P500 returnsFinancial support from the Spanish Ministry of Education and Science, research projects ECO2009-08100 and ECO2012-32401, is acknowledged. The third author is also grateful for project MTM2010-1732

Score driven asymmetric stochastic volatility models

In this paper we propose a new class of asymmetric stochastic volatility (SV) models, which specifies the volatility as a function of the score of the distribution of returns conditional on volatilities based on the Generalized Autoregressive Score (GAS) model. Different specifications of the log-volatility are obtained by assuming different return error distributions. In particular, we consider three of the most popular distributions, namely, the Normal, Student-t and Generalized Error Distribution and derive the statistical properties of each of the corresponding score driven SV models. We show that some of the parameters cannot be property identified by the moments usually considered as to describe the stylized facts of financial returns, namely, excess kurtosis, autocorrelations of squares and cross-correlations between returns and future squared returns. The parameters of some restricted score driven SV models can be estimated adequately using a MCMC procedure. Finally, the new proposed models are fitted to financial returns and evaluated in terms of their in-sample and out-of-sample performanceFinancial support from the Spanish Ministry of Education and Science, research project ECO2012-32401, is acknowledged. The third author is also grateful for project MTM2010-1732

Threshold stochastic volatility: properties and forecasting

We analyze the ability of Threshold Stochastic Volatility (TSV) models to represent and forecast asymmetric volatilities. First, we derive the statistical properties of TSV models. Second, we demonstrate the good finite sample properties of a MCMC estimator, implemented in the software package WinBUGS, when estimating the parameters of a general specification, denoted CTSV, that nests the TSV and asymmetric autoregressive stochastic volatility (A-ARSV) models. The MCMC estimator also discriminates between the two specifications and allows us to obtain volatility forecasts. Third, we analyze daily S&P 500 and FTSE 100 returns and show that the estimated CTSV model implies plug-in moments that are slightly closer to the observed sample moments than those implied by other nested specifications. Furthermore, different asymmetric specifications generate rather different European options prices. Finally, although none of the models clearly emerge as best outof- sample, it seems that including both threshold variables and correlated errors may be a good compromise.We acknowledge financial support from the Spanish Ministry of Economy and Competitiveness, research projects ECO2015-70331-C2-2-R and ECO2015-65701-P, as well as FCT grant UID/GES/00315/2013

Asymmetric stochastic volatility models: properties and particle filter-based simulated maximum likelihood estimation

The statistical properties of a general family of asymmetric stochastic volatility (A-SV)models which capture the leverage effect in financial returns are derived providing analyt- ical expressions of moments and autocorrelations of power-transformed absolute returns.The parameters of the A-SV model are estimated by a particle filter-based simulated max- imum likelihood estimator and Monte Carlo simulations are carried out to validate it. Itis shown empirically that standard SV models may significantly underestimate the value- at-risk of weekly S&P 500 returns at dates following negative returns and overestimate itafter positive returns. By contrast, the general specification proposed provide reliable fore- casts at all dates. Furthermore, based on daily S&P 500 returns, it is shown that the mostadequate specification of the asymmetry can change over time.We gratefully acknowledge the financial support from the Spanish Government, contract grants ECO2015-70331-C2-2-R and ECO2015-65701-P (MINECO/FEDER), the computer support from EUROFIDAI, and the FCT grant UID/GES/00315/2013

SIK2 inhibition enhances PARP inhibitor activity synergistically in ovarian and triple-negative breast cancers

Poly(ADP-ribose) polymerase inhibitors (PARP inhibitors) have had an increasing role in the treatment of ovarian and breast cancers. PARP inhibitors are selectively active in cells with homologous recombination DNA repair deficiency caused by mutations in BRCA1/2 and other DNA repair pathway genes. Cancers with homologous recombination DNA repair proficiency respond poorly to PARP inhibitors. Cancers that initially respond to PARP inhibitors eventually develop drug resistance. We have identified salt-inducible kinase 2 (SIK2) inhibitors, ARN3236 and ARN3261, which decreased DNA double-strand break (DSB) repair functions and produced synthetic lethality with multiple PARP inhibitors in both homologous recombination DNA repair deficiency and proficiency cancer cells. SIK2 is required for centrosome splitting and PI3K activation and regulates cancer cell proliferation, metastasis, and sensitivity to chemotherapy. Here, we showed that SIK2 inhibitors sensitized ovarian and triple-negative breast cancer (TNBC) cells and xenografts to PARP inhibitors. SIK2 inhibitors decreased PARP enzyme activity and phosphorylation of class-IIa histone deacetylases (HDAC4/5/7). Furthermore, SIK2 inhibitors abolished class-IIa HDAC4/5/7–associated transcriptional activity of myocyte enhancer factor-2D (MEF2D), decreasing MEF2D binding to regulatory regions with high chromatin accessibility in FANCD2, EXO1, and XRCC4 genes, resulting in repression of their functions in the DNA DSB repair pathway. The combination of PARP inhibitors and SIK2 inhibitors provides a therapeutic strategy to enhance PARP inhibitor sensitivity for ovarian cancer and TNBC

Adaptative predictability of stock market returns

We revisit the stock market return predictability using the variance risk premium and conditional variance as predictors of classical predictive regressions and time-varying coefficient predictive regressions. Also, we propose three new models to forecast the conditional variance and estimate the variance risk premium. Our empirical results show, first, that the flexibility provided by time-varying coefficient regressions often improve the ability of the variance risk premium, the conditional variance, and other control variables to predict stock market returns. Second, the conditional variance and variance risk premium obtained from varying coefficient models perform consistently well at predicting stock market returns. Finally, the time-varying coefficient predictive regressions show that the variance risk premium is a predictor of stock market excess returns before the global financial crisis of 2007, but its predictability decreases in the post global financial crisis period at the 3-month horizon. At the 12-month horizon, both the variance risk premium and conditional variance are predictors of stock excess returns during most of 2000-2015

The Oral Delivery System of Modified GLP-1 by Probiotics for T2DM

The glucagon-like peptide-1 (GLP-1) is a peptide with incretin activity and plays an important role in glycemic control as well as the improvement of insulin resistance in type 2 diabetes mellitus (T2DM). However, the short half-life of the native GLP-1 in circulation poses difficulties for clinical practice. To improve the proteolytic stability and delivery properties of GLP-1, a protease-resistant modified GLP-1 (mGLP-1) was constructed with added arginine to ensure the structural integrity of the released mGLP-1 in vivo. The model probiotic Lactobacillus plantarum WCFS1 was chosen as the oral delivery vehicle with controllable endogenous genetic tools driven for mGLP-1 secretory constitutive expression. The feasibility of our design was explored in db/db mice which showed an improvement in diabetic symptoms related to decreased pancreatic glucagon, elevated pancreatic β-cell proportion, and increased insulin sensitivity. In conclusion, this study provides a novel strategy for the oral delivery of mGLP-1 and further probiotic transformation