Exploring option pricing and hedging via volatility asymmetry

This paper evaluates the application of two well-known asymmetric stochastic volatility (ASV) models to option price forecasting and dynamic delta hedging. They are specied in discrete time in contrast to the classical stochastic volatility (SV) models used in option pricing. There is some related literature, but little is known about the empirical implications of volatility asymmetry on option pricing. The objectives of this paper are to estimate ASV option pricing models using a Bayesian approach unknown in this type of literature, and to investigate the e ect of volatility asymmetry on option pricing for di erent size equity sectors and periods of volatility. Using the S&P MidCap 400 and S&P 500 European call option quotes, results show that volatility asymmetry benets the accuracy of option price forecasting and hedging cost e ectiveness in the large-cap equity sector. However, asymmetric SV models do not improve the option price forecasting and dynamic hedging in the mid-cap equity sector.The second author acknowledges nancial support from Spanish Ministry of Economy and Competitiveness, research projects ECO2015-70331-C2-2-R and ECO2015-65701-P, and FCT grant UID/GES/00315/2013

Data cloning for a threshold asymmetric stochastic volatility model

In this paper, we propose a new asymmetric stochastic volatility model whose asymmetry parameter can change depending on the intensity of the shock and is modeled as a threshold function whose threshold depends on past returns. We study the model in terms of leverage and propagation using a new concept that has recently appeared in the literature. We find that the new model can generate more leverage and propagation than a well-known asymmetric volatility model. We also propose to estimate the parameters of the model by cloning data. We compare the estimates in finite samples of data cloning and a Bayesian approach and find that data cloning is often more accurate. Data cloning is a general technique for computing maximum likelihood estimators and their asymptotic variances using a Markov chain Monte Carlo (MCMC) method. The empirical application shows that the new model often improves the fit compared to the benchmark model. Finally, the new proposal together with data cloning estimation often leads to more accurate 1-day and 10-day volatility forecasts, especially for return series with high volatility

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

Efficiency evaluation of hotel chains: a Spanish case study

The tourism industry, in particular the hotel sector, is a highly competitive market. In this context, it is important that an hotel chain operates efficiently if it wants to improve or maintain its market position. The objective of this work is to compare the relative efficiency of hotel chains operating in Spain. To do this, we have designed a stochastic frontier model to measure revenue efficiency as a function of various different inputs such as total staff or number of rooms. Given that chains vary considerably in size, both inputs and outputs are normalized by an appropriate size measure. In contrast to most previous work, we account for heterogeneity in hotel chains by introducing relevant variables, such as the proportion of hotels in the chain with three stars or fewer, into the efficiency term of the stochastic frontier model. Our results suggest that in the Spanish case, in the period of the economic crisis, hotel chains increase overall revenue by investing in fewer, big hotels rather than more, small hotels. Furthermore, in terms of revenue efficiency, it appears better for hotel chains to invest in hotels of three or fewer stars than in higher star rated hotels. Finally, there is no clear evidence of a relationship between the size of a hotel chain and its efficiency.The authors acknowledge financial support from the Spanish Ministry of Economy and Competitiveness, research projects ECO2015-66593-P, ECO2015-70331-C2-2-R, ECO2015-65701-P and Fundação para a Ciência e Tecnologia grant UID/GES/00315/2013

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

Detecting outliers in multivariate volatility models: a wavelet procedure

It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers.The authors acknowledge financial support from BRU-IUL, FEDER funds, Spanish Ministry of Economy and Competitiveness (MTM2014-56535-R, MTM2012-36163-C06-03, ECO2015-70331-C2-2-R) Spanish Ministry of Science, Innovation and Universities (PGC2018-096977-B-l00), FCT grant UID/GES/00315/2019 and Junta de Andalucía (FQM-329)

Bayesian analysis of dynamic effects in inefficiency : evidence from the Colombian banking sector

Firms face a continuous process of technological and environmental changes that implies making managerial decisions in a dynamic context. However, costs and other constraints prevent firms from making instant adjustments towards optimal conditions and may cause inefficiency to be persistent in time. In this work, we propose a flexible dynamic model that makes possible to distinguish persistent effects in the inefficiency from firm inefficiency heterogeneity and to capture differences in the adjustment costs between firms. The new model is fitted to a ten year sample of Colombian banks. Our findings suggest that firm characteristics associated to size and foreign ownership have negative effects on inefficiency and separating these heterogeneity factors from the dynamics of inefficiency improves model fit. On the other hand, acquisitions are found to have positive and persistent effects on inefficiency. Colombian banks are found to present high inefficiency persistence but there exist important differences between institutions. In particular, merged banks present low costs of adjustment that allow them to recover rapidly the efficiency losses derived from merging processesFinancial support from the Spanish Ministry of Education and Science, research projects ECO2012- 3401, MTM2010-17323, ECO2009-08100 and SEJ2007-64500 is also gratefully acknowledge

Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

Estimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, it is not clear in general in which component of the error distribution the covariates should be included. In the classical context, some studies include covariates in the scale parameter of the inefficiency with the property of preserving the shape of its distribution. We extend this idea to Bayesian inference for stochastic frontier models capturing both observed and unobserved heterogeneity under half normal, truncated and exponential distributed inefficiencies. We use the WinBugs package to implement our approach throughout. Our findings using two real data sets, illustrate the relevant effects on shrinking and separating individual posterior efficiencies when heterogeneity affects the scale of the inefficiency. We also see that the inclusion of unobserved heterogeneity is still relevant when no observable covariates are available.Financial support from the Spanish Ministry of Education and Science, research projects ECO2009-08100, MTM2010-17323 and SEJ2007-64500 is also gratefully acknowledged

Dynamic effects in inefficiency: evidence from the Colombian banking sector

Financial support from the Spanish Ministry of Education and Science, research Projects ECO2012-3401, MTM2010-17323, SEJ2007-64500 and ECO2012-38442 is also gratefully acknowledged.Firms face a continuous process of technological and environmental changes that requires them to make managerial decisions in a dynamic context. However, costs and constraints prevent firms from making instant adjustments towards optimal conditions and may cause inefficiency to persist in time. We propose a dynamic inefficiency specification that captures differences in the adjustment costs among firms and non-persistent effects of inefficiency heterogeneity. The model is fitted to a ten year sample of Colombian banks. The new specification improves model fit and have effects on efficiency estimations. Overall, Colombian banks present high inefficiency persistence but important differences between institutions are found. In particular, merged banks present low adjustment costs that allow them to recover rapidly efficiency losses derived from merging processes