To reconcile the mixed empirical results, we develop a theoretical model whose main implication is a concave impact of regulation on the probability of a crisis. We test this relationship by applying a Probit model of a non-linear specification to annual data from 1999 to 2011 drawn from 132 countries. The probability of a financial crisis fits an inverted U-shaped curve: it rises as regulation stringency moves from low to medium levels and falls from medium to high levels. Countries located at the intermediate level of regulatory stringency face more instability than countries that are either loosely or severely regulated. We identify the latter two groups as falling in “liberalization traps”. Institutional quality interacts significantly with the regulatory environment.
{"title":"Regulation, Financial Crises, and Liberalization Traps","authors":"F. Marchionne, B. Pisicoli, M. Fratianni","doi":"10.2139/ssrn.3421151","DOIUrl":"https://doi.org/10.2139/ssrn.3421151","url":null,"abstract":"To reconcile the mixed empirical results, we develop a theoretical model whose main implication is a concave impact of regulation on the probability of a crisis. We test this relationship by applying a Probit model of a non-linear specification to annual data from 1999 to 2011 drawn from 132 countries. The probability of a financial crisis fits an inverted U-shaped curve: it rises as regulation stringency moves from low to medium levels and falls from medium to high levels. Countries located at the intermediate level of regulatory stringency face more instability than countries that are either loosely or severely regulated. We identify the latter two groups as falling in “liberalization traps”. Institutional quality interacts significantly with the regulatory environment.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127123080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology.
{"title":"Understanding Jumps in High Frequency Digital Asset Markets","authors":"Danial Saef, Odett Nagy, Sergej Sizov, W. Härdle","doi":"10.2139/ssrn.3944865","DOIUrl":"https://doi.org/10.2139/ssrn.3944865","url":null,"abstract":"While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"27 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116393940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We find that liquidity volatility negatively predicts stock returns in global markets. This relationship holds for different liquidity measures and cannot be explained by the idiosyncratic volatility effect. This puzzle can be explained by the asymmetric impact of liquidity increase and decrease on expected returns. Since the price decline following liquidity decrease outweighs the price appreciation after liquidity increase, high-liquidity-volatility stocks, which are more likely to experience large liquidity changes in either direction, tend to have negative returns on average. We find that including liquidity decrease explains the negative premium of liquidity volatility, while including liquidity increase does not.
{"title":"Liquidity Shocks and the Negative Premium of Liquidity Volatility Around the World","authors":"Frank Y. Feng, W. Kang, Huiping Zhang","doi":"10.2139/ssrn.3930591","DOIUrl":"https://doi.org/10.2139/ssrn.3930591","url":null,"abstract":"We find that liquidity volatility negatively predicts stock returns in global markets. This relationship holds for different liquidity measures and cannot be explained by the idiosyncratic volatility effect. This puzzle can be explained by the asymmetric impact of liquidity increase and decrease on expected returns. Since the price decline following liquidity decrease outweighs the price appreciation after liquidity increase, high-liquidity-volatility stocks, which are more likely to experience large liquidity changes in either direction, tend to have negative returns on average. We find that including liquidity decrease explains the negative premium of liquidity volatility, while including liquidity increase does not.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider the problem of estimating volatility based on high-frequency data when the observed price process is a continuous Itô semimartingale contaminated by microstructure noise. Assuming that the noise process is compatible across different sampling frequencies, we argue that it typically has a similar local behavior to fractional Brownian motion. For the resulting class of processes, which we call mixed semimartingales, we derive consistent estimators and asymptotic confidence intervals for the roughness parameter of the noise and the integrated price and noise volatilities, in all cases where these quantities are identifiable. Our model can explain key features of recent stock price data, most notably divergence rates in volatility signature plots that vary considerably over time and between assets.
{"title":"Mixed Semimartingales: Volatility Estimation in the Presence of Rough Noise","authors":"Carsten Chong, T. Delerue, Guoying Li","doi":"10.2139/ssrn.3878809","DOIUrl":"https://doi.org/10.2139/ssrn.3878809","url":null,"abstract":"We consider the problem of estimating volatility based on high-frequency data when the observed price process is a continuous Itô semimartingale contaminated by microstructure noise. Assuming that the noise process is compatible across different sampling frequencies, we argue that it typically has a similar local behavior to fractional Brownian motion. For the resulting class of processes, which we call mixed semimartingales, we derive consistent estimators and asymptotic confidence intervals for the roughness parameter of the noise and the integrated price and noise volatilities, in all cases where these quantities are identifiable. Our model can explain key features of recent stock price data, most notably divergence rates in volatility signature plots that vary considerably over time and between assets.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123749031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Institute of Social and Economic Research, C. Leung
This paper provides some background for the book, Handbook of Real Estate and Macroeconomics. It gives an overview of different chapters and how various themes and ideas can be connected. Directions for future research are also discussed.
{"title":"Handbook of Real Estate and Macroeconomics: An Introduction","authors":"Institute of Social and Economic Research, C. Leung","doi":"10.2139/ssrn.3902313","DOIUrl":"https://doi.org/10.2139/ssrn.3902313","url":null,"abstract":"This paper provides some background for the book, Handbook of Real Estate and Macroeconomics. It gives an overview of different chapters and how various themes and ideas can be connected. Directions for future research are also discussed.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130270080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce a data driven and model free approach for computing conditional expectations. The new method combines Gaussian Mean Mixture models with classic analytic techniques based on the properties of the Gaussian distribution. We also incorporate a proxy hedge that leads to analytic expressions for the hedge with respect to the chosen proxy. This essentially makes use of the representation of the hedge sensitivity measuring the part of the variance that is attributed to the proxy. If we take the underlying, this corresponds to a time discrete minimal variance delta hedge. We apply our method to the calibration of pricing and hedging of (multi-dimensional) exotic Bermudan options, the calibration of stochastic local volatility models and applications to xVA/exposure calculation. For illustration we have chosen the rough Bergomi model and high-dimensional Heston models. Finally, we discuss issues when increasing the dimensionality and propose solutions using established statistical learning methods.
{"title":"GMM DCKE - Semi-Analytic Conditional Expectations","authors":"Joerg Kienitz","doi":"10.2139/ssrn.3902490","DOIUrl":"https://doi.org/10.2139/ssrn.3902490","url":null,"abstract":"We introduce a data driven and model free approach for computing conditional expectations. The new method combines Gaussian Mean Mixture models with classic analytic techniques based on the properties of the Gaussian distribution. We also incorporate a proxy hedge that leads to analytic expressions for the hedge with respect to the chosen proxy. This essentially makes use of the representation of the hedge sensitivity measuring the part of the variance that is attributed to the proxy. If we take the underlying, this corresponds to a time discrete minimal variance delta hedge. We apply our method to the calibration of pricing and hedging of (multi-dimensional) exotic Bermudan options, the calibration of stochastic local volatility models and applications to xVA/exposure calculation. For illustration we have chosen the rough Bergomi model and high-dimensional Heston models. Finally, we discuss issues when increasing the dimensionality and propose solutions using established statistical learning methods.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124683234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper compares different GARCH models in terms of their out-of-sample predictive ability of leveraged loan market volatility. The study investigates whether the asymmetric effects of good and bad news on volatility is present and how distributional assumptions affect the selection of GARCH models. Compared to two widely used historical volatility models, the simple moving average and the exponentially weighted moving average, the results suggest that asymmetric GARCH models have marginally better out-of-sample predictive ability. In addition, this study finds that fixed income market volatilities improve the forecasts of loan market volatility. The model comparison involves a regression-based approach, loss functions and statistical tests.
{"title":"Forecasting leveraged loan market volatility using GARCH models","authors":"Andreas Keßler","doi":"10.2139/ssrn.3874947","DOIUrl":"https://doi.org/10.2139/ssrn.3874947","url":null,"abstract":"This paper compares different GARCH models in terms of their out-of-sample predictive ability of leveraged loan market volatility. The study investigates whether the asymmetric effects of good and bad news on volatility is present and how distributional assumptions affect the selection of GARCH models. Compared to two widely used historical volatility models, the simple moving average and the exponentially weighted moving average, the results suggest that asymmetric GARCH models have marginally better out-of-sample predictive ability. In addition, this study finds that fixed income market volatilities improve the forecasts of loan market volatility. The model comparison involves a regression-based approach, loss functions and statistical tests.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124215630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of the different measures concerns the ease with which they may be calculated empirically, merely involving cross-products of signed, or thresholded, high-frequency returns. I begin by considering univariate semivariation measures, followed by multivariate semicovariation and semibeta measures, before briefly discussing even richer partial (co)variation measures. I focus my discussion on practical uses of the measures emphasizing what I consider to be the most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing
{"title":"Realized Semi(Co)Variation: Signs that All Volatilities are Not Created Equal","authors":"T. Bollerslev","doi":"10.2139/ssrn.3872858","DOIUrl":"https://doi.org/10.2139/ssrn.3872858","url":null,"abstract":"I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of the different measures concerns the ease with which they may be calculated empirically, merely involving cross-products of signed, or thresholded, high-frequency returns. I begin by considering univariate semivariation measures, followed by multivariate semicovariation and semibeta measures, before briefly discussing even richer partial (co)variation measures. I focus my discussion on practical uses of the measures emphasizing what I consider to be the most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116528127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
La crisis en 2008 escaló al resto del mundo y la falta de liquidez se esparció como pólvora. Algunos bancos centrales tuvieron que intervenir en los mercados monetarios y en instituciones financieras para rescatarlas: se generó un efecto dominó que provocó una crisis alimentaria mundial y un aumento de la pobreza. Además, “el cambio en las condiciones del mercado reveló la rapidez con que la liquidez puede agotarse y puso de manifiesto que la falta de liquidez puede prolongarse durante bastante tiempo”, señala un documento del Banco de Pagos Internacionales (BPI, por sus siglas en inglés), conocido como el banco de los bancos centrales. Este episodio confirmó la importancia de la liquidez en el funcionamiento de los mercados financieros y el sector bancario. Fue así como el comité de Basilea III (iniciativas para fortalecer el sistema financiero mundial tras la crisis de 2008 y 2009) acordó medidas para estar prevenidos ante otra recesión. Entre ellas, a partir de 2019, las instituciones financieras deben cumplir con agregar el factor de riesgo de liquidez en la medición del riesgo de mercado.
{"title":"El ‘modelo Al Janabi’, la herramienta que podría evitar las crisis","authors":"Mazin A. M. Al Janabi","doi":"10.2139/ssrn.3838031","DOIUrl":"https://doi.org/10.2139/ssrn.3838031","url":null,"abstract":"La crisis en 2008 escaló al resto del mundo y la falta de liquidez se esparció como pólvora. Algunos bancos centrales tuvieron que intervenir en los mercados monetarios y en instituciones financieras para rescatarlas: se generó un efecto dominó que provocó una crisis alimentaria mundial y un aumento de la pobreza. Además, “el cambio en las condiciones del mercado reveló la rapidez con que la liquidez puede agotarse y puso de manifiesto que la falta de liquidez puede prolongarse durante bastante tiempo”, señala un documento del Banco de Pagos Internacionales (BPI, por sus siglas en inglés), conocido como el banco de los bancos centrales. Este episodio confirmó la importancia de la liquidez en el funcionamiento de los mercados financieros y el sector bancario. Fue así como el comité de Basilea III (iniciativas para fortalecer el sistema financiero mundial tras la crisis de 2008 y 2009) acordó medidas para estar prevenidos ante otra recesión. Entre ellas, a partir de 2019, las instituciones financieras deben cumplir con agregar el factor de riesgo de liquidez en la medición del riesgo de mercado.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122221626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Benhamou, D. Saltiel, S. Tabachnik, Sui Kai Wong, François Chareyron
Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional "walk-forward analysis", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent.
Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.
{"title":"Adaptive Learning for Financial Markets Mixing Model-Based and Model-Free RL for Volatility Targeting","authors":"E. Benhamou, D. Saltiel, S. Tabachnik, Sui Kai Wong, François Chareyron","doi":"10.2139/ssrn.3830012","DOIUrl":"https://doi.org/10.2139/ssrn.3830012","url":null,"abstract":"Model-Free Reinforcement Learning has achieved meaningful results in stable environments but, to this day, it remains problematic in regime changing environments like financial markets. In contrast, model-based RL is able to capture some fundamental and dynamical concepts of the environment but suffer from cognitive bias. In this work, we propose to combine the best of the two techniques by selecting various model-based approaches thanks to Model-Free Deep Reinforcement Learning. Using not only past performance and volatility, we include additional contextual information such as macro and risk appetite signals to account for implicit regime changes. We also adapt traditional RL methods to real-life situations by considering only past data for the training sets. Hence, we cannot use future information in our training data set as implied by K-fold cross validation. Building on traditional statistical methods, we use the traditional \"walk-forward analysis\", which is defined by successive training and testing based on expanding periods, to assert the robustness of the resulting agent. <br><br>Finally, we present the concept of statistical difference's significance based on a two-tailed T-test, to highlight the ways in which our models differ from more traditional ones. Our experimental results show that our approach outperforms traditional financial baseline portfolio models such as the Markowitz model in almost all evaluation metrics commonly used in financial mathematics, namely net performance, Sharpe and Sortino ratios, maximum drawdown, maximum drawdown over volatility.","PeriodicalId":321181,"journal":{"name":"Econometric Modeling: International Financial Markets - Volatility & Financial Crises eJournal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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