ERN: Time-Series Models (Single) (Topic)最新文献

{"title":"Dynamic Multi-Factor Bid-Offer Adjustment Model - Visual Version","authors":"R. Kashyap","doi":"10.2139/ssrn.2431577","DOIUrl":"https://doi.org/10.2139/ssrn.2431577","url":null,"abstract":"The objective is to come up with a model that alters the Bid-Offer, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as the rest of social sciences, where changes can be observed and decisions can be taken by participants to influence the system, means that our model has to be adaptive and include a feedback loop that alters the bid offer adjustment based on the modifications we are seeing in the market and trading conditions, without a significant time delay. We will build a sample model that incorporates such a feedback mechanism and also makes it possible to check the efficacy of the changes to the quotes being made, by gauging the impact on the Profits.The market conditions here refer to factors that are beyond the direct control of the market maker and this information is usually available publicly to other participants. Trading conditions refer to factors that can be influenced by the market maker and are dependent on the trading book being managed and will be privy only to the market maker and will be mostly confidential to others. The factors we use to adjust the spread are the price volatility, which is publicly observable; and trade count and volume, which are generally only known to the market maker, in various instruments over different historical durations in time. The contributions of each of the factors to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The ensuing discussion considers the calculations for each factor separately and the consolidation in detail. Any model that automatically updates the quotes is more suited for instruments that have a high number of transactions within short intervals, making it hard for traders to manually monitor and adjust the spread; though this is by no means a stringent requirement. We can use similar models for illiquid instruments as well and use the quotations provided by the model as a baseline for further human refinement. We have chosen the currency markets to build the sample model since they are extremely liquid, Over the Counter (OTC), and hence trading in them is not as transparent as other financial instruments like equities. The nature of currency trading implies that we do not have any idea on the actual volumes traded and the number of trades. We simulate the number of trades and the average size of trades from a log normal distribution. The parameters of the log normal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can be easily extended to other financial instruments and possibly to any product with an ability to make electronic price quotations or even be used to periodically perform manual price updates on products that are traded non-electronically.Thankfully, we are not at a stage where Starbucks will sell c","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130782685","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}

{"title":"Statistical Inference of Conditional Quantiles in Nonlinear Time Series Models","authors":"Mike K. P. So, Ray S. W. Chung","doi":"10.2139/ssrn.2557953","DOIUrl":"https://doi.org/10.2139/ssrn.2557953","url":null,"abstract":"This paper studies the statistical properties of a two-step conditional quantile estimator in nonlinear time series models with unspecified error distribution. The asymptotic distribution of the quasi-maximum likelihood estimators and the filtered empirical percentiles is derived. Three applications of the asymptotic result are considered. First, we construct an interval estimator of the conditional quantile without any distributional assumptions. Second, we develop a specification test for the error distribution. Finally, using the specification test, we propose methods for estimating the tail index of the error distribution that supports the construction of a new estimator for the conditional quantile at the extreme tail. The asymptotic results and their applications are illustrated by simulations and real data analyses in which our methods for analyzing daily and intraday financial return series have been adopted.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128897138","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}

{"title":"Distributional Effects of the Australian Renewable Energy Target (RET) through Wholesale and Retail Electricity Price Impacts","authors":"Johanna Cludius, S. Forrest, I. MacGill","doi":"10.2139/ssrn.2359205","DOIUrl":"https://doi.org/10.2139/ssrn.2359205","url":null,"abstract":"The Australian Renewable Energy Target (RET) has spurred significant investment in renewable electricity generation, notably wind power, over the past decade. This paper considers distributional implications of the RET for different energy users. Using time-series regression, we show that the increasing amount of wind energy has placed considerable downward pressure on wholesale electricity prices through the so-called merit order effect. On the other hand, RET costs are passed on to consumers in the form of retail electricity price premiums. Our findings highlight likely significant redistributive transfers between different energy user classes under current RET arrangements. In particular, some energy-intensive industries are benefiting from lower wholesale electricity prices whilst being largely exempted from contributing to the costs of the scheme. By contrast, many households are paying significant RET pass through costs whilst not necessarily benefiting from lower wholesale prices. A more equitable distribution of RET costs and benefits could be achieved by reviewing the scope and extent of industry exemptions and ensuring that methodologies to estimate wholesale price components in regulated electricity tariffs reflect more closely actual market conditions. More generally, these findings support the growing international appreciation that policy makers need to integrate distributional assessments into policy design and implementation.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127944205","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}

{"title":"Regulated Fractional Variance Ratio Unit Root Tests","authors":"Mirza Trokić","doi":"10.2139/ssrn.2042794","DOIUrl":"https://doi.org/10.2139/ssrn.2042794","url":null,"abstract":"This article addresses unit root testing on regulated series through the variance ratio (VR) statistic of Nielsen (2009). The asymptotic distribution of the regulated VR statistic is developed with and without OLS detrending. Results of Cavaliere and Xu (2011) are extended by also developing the asymptotic distribution of regulated series with a linear trend. Asymptotic local power is analyzed for various choices of the fractional integration parameter d. It is shown that power performance depends crucially on the length of the regulating interval. When the interval is sufficiently wide the results in Nielsen (2009) are recovered.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114788045","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}

{"title":"Stochastic Nonlinear Time Series Forecasting Using Time-Delay Reservoir Computers: Performance and Universality","authors":"Lyudmila Grigoryeva, J. Henriques, L. Larger, J. Ortega","doi":"10.2139/ssrn.2350331","DOIUrl":"https://doi.org/10.2139/ssrn.2350331","url":null,"abstract":"Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963853","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}

{"title":"State Space Disaggregation Model with Information Loss Function","authors":"D. Jun, Jihwan Moon","doi":"10.2139/ssrn.2360450","DOIUrl":"https://doi.org/10.2139/ssrn.2360450","url":null,"abstract":"Different data frequency is a common problem in many research fields; therefore, it should be handled before a particular study is well under way. Many novel ideas including disaggregation techniques, which are the major interest of this study, have been suggested to mitigate the nuisances of mixed-frequency data. In this study, we suggest a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement a Monte Carlo simulation and provide an empirical study.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125033346","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}

{"title":"Macroeconomic Forecasting and Structural Analysis Through Regularized Reduced-Rank Regression","authors":"E. Bernardini, G. Cubadda","doi":"10.2139/ssrn.2334862","DOIUrl":"https://doi.org/10.2139/ssrn.2334862","url":null,"abstract":"This paper proposes a strategy to detect and impose reduced-rank restrictions in medium vector autoregressive models. In this framework, it is known that Canonical Correlation Analysis (CCA) does not perform well because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naive univariate forecasting methods. In particular, we suggest to use a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, thus obtaining a method that is asymptotically equivalent to CCA, but it is numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach both in forecasting and in structural analysis.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123515465","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}

{"title":"Realized Range Volatility Forecasting: Dynamic Features and Predictive Variables","authors":"M. Caporin, Gabriel G. Velo","doi":"10.2139/ssrn.2322637","DOIUrl":"https://doi.org/10.2139/ssrn.2322637","url":null,"abstract":"In this paper, we estimate, model and forecast realized range volatility, a realized measure and estimator of the quadratic variation of financial prices. This quantity was introduced early in the literature and it is based on the high–low range observed at high frequency during the day. We consider the impact of the microstructure noise in high frequency data and correct our estimations, following a known procedure. Then, we model the realized range accounting for the well-known stylized effects present in financial data and we investigate the role that macroeconomic and financial variables play when forecasting daily stocks volatility. We consider an HAR model with asymmetric effects with respect to the volatility and the return, and GARCH and GJR specifications for the variance equation. Moreover, we consider a non-Gaussian distribution for the innovations. Finally, we extend the model including macroeconomic and financial variables that capture the present and the future state of the economy. We find that these variables are significantly correlated with the first common component of the volatility series and they have a highly in-sample explanatory power. The analysis of the forecast performance in 16 NYSE stocks suggests that the introduction of asymmetric effects with respect to the returns and the volatility in the HAR model result in a significant improvement in the point forecasting accuracy as well and the variables related with the U.S. stock market performance and proxies for the credit risk.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133356376","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}

{"title":"Wavelet Power: Wavelet Energy Ratio Unit Root Tests","authors":"Mirza Trokić","doi":"10.2139/ssrn.2131529","DOIUrl":"https://doi.org/10.2139/ssrn.2131529","url":null,"abstract":"This paper uses wavelet theory to propose a frequency domain nonparametric and tuning parameter free family of unit root tests indexed by the fractional parameter d. The proposed test exploits the wavelet power spectrum of the observed series and its fractional partial sum to construct a test of the unit root based on the ratio of the resulting scaling energies. The construction takes its inspiration from the variance ratio (VR) unit root test of Nielsen (2009) and Fan and Gen cay (2010) (FG). The result is a statistic whose power properties virtually mimic that of the VR statistics but which drastically reduces the severe size distortions suffered by both the VR and FG test in the presence of serially correlated MA(1) errors when the MA parameter is close to negative unity. Any remaining size distortions are virtually eliminated using a modern wavelet resampling technique called wavestrapping. Finally, the test is visibly more robust to size distortions arising from lowering d than its VR counterpart and unlike the FG test, requires no estimation for construction.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130315267","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}

{"title":"European Sovereign CDS Premia during the Crisis – A Cointegration Analysis","authors":"Alexander Schmidt","doi":"10.2139/ssrn.2295399","DOIUrl":"https://doi.org/10.2139/ssrn.2295399","url":null,"abstract":"In this paper, I use multivariate time series models in order to analyze the evolution of European Sovereign CDS spreads during the recent crisis. I find evidence that sovereigns’ credit risk premia are non-stationary but cointegrated with simple measures of the countries’ indebtedness and the overall default risk in the economy. Vector error correction models are estimated on the individual country and aggregated European level, using monthly averages of sovereign and iTraxx CDS as well as debt-over-GDP ratios. Rising public debt levels are found to explain about half of the structural CDS level increases since the outbreak of the financial crisis. Yet, the largest part of sovereign CDS variance cannot be attributed to macroeconomic fundamentals and originates from the interaction with the overall CDS markets. A structural break analysis suggests two different regimes with a significant repricing of sovereign risk. With the onset of the European debt crisis there is evidence for an increased interaction with the financial sector as measured by the iTraxx Fin.","PeriodicalId":418701,"journal":{"name":"ERN: Time-Series Models (Single) (Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114717954","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}