Digital financePub Date : 2021-01-01Epub Date: 2021-09-02DOI: 10.1007/s42521-021-00040-8
Apostolos Chalkis, Emmanouil Christoforou, Ioannis Z Emiris, Theodore Dalamagas
{"title":"Modeling asset allocations and a new portfolio performance score.","authors":"Apostolos Chalkis, Emmanouil Christoforou, Ioannis Z Emiris, Theodore Dalamagas","doi":"10.1007/s42521-021-00040-8","DOIUrl":"https://doi.org/10.1007/s42521-021-00040-8","url":null,"abstract":"<p><p>We discuss and extend a powerful, geometric framework to represent the set of portfolios, which identifies the space of asset allocations with the points lying in a convex polytope. Based on this viewpoint, we survey certain state-of-the-art tools from geometric and statistical computing to handle important and difficult problems in digital finance. Although our tools are quite general, in this paper, we focus on two specific questions. The first concerns crisis detection, which is of prime interest for the public in general and for policy makers in particular because of the significant impact that crises have on the economy. Certain features in stock markets lead to this type of anomaly detection: Given the assets' returns, we describe the relationship between portfolios' return and volatility by means of a copula, without making any assumption on investors' strategies. We examine a recent method relying on copulae to construct an appropriate indicator that allows us to automate crisis detection. On real data the indicator detects all past crashes in the cryptocurrency market and from the DJ600-Europe index, from 1990 to 2008, the indicator identifies correctly 4 crises and issues one false positive for which we offer an explanation. Our second contribution is to introduce an original computational framework to model asset allocation strategies, which is of independent interest for digital finance and its applications. Our approach addresses the crucial question of evaluating portfolio management, and is relevant the individual managers as well as financial institutions. To evaluate portfolio performance, we provide a new portfolio score, based on the aforementioned framework and concepts. In particular, it relies on statistical properties of portfolios, and we show how they can be computed efficiently.</p>","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 3-4","pages":"333-371"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8412890/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39393567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital financePub Date : 2020-10-29DOI: 10.2139/ssrn.3721478
M. Dai, Hanqing Jin, S. Kou, Yuhong Xu
{"title":"Robo-advising: a dynamic mean-variance approach","authors":"M. Dai, Hanqing Jin, S. Kou, Yuhong Xu","doi":"10.2139/ssrn.3721478","DOIUrl":"https://doi.org/10.2139/ssrn.3721478","url":null,"abstract":"In contrast to traditional financial advising, robo-advising needs to elicit investors’ risk profile via several simple online questions and provide advice consistent with conventional investment wisdom, e.g., rich and young people should invest more in risky assets. To meet the two challenges, we propose to do the asset allocation part of robo-advising using a dynamic mean-variance criterion over the portfolio’s log returns. We obtain analytical and time-consistent optimal portfolio policies under jump-diffusion models and regime-switching models.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"81 - 97"},"PeriodicalIF":0.0,"publicationDate":"2020-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49226149","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}
Digital financePub Date : 2020-10-23DOI: 10.1007/s42521-023-00086-w
Jurgen E. Schatzmann, Bernhard Haslhofer
{"title":"Exploring investor behavior in Bitcoin: a study of the disposition effect","authors":"Jurgen E. Schatzmann, Bernhard Haslhofer","doi":"10.1007/s42521-023-00086-w","DOIUrl":"https://doi.org/10.1007/s42521-023-00086-w","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45494405","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}
Digital financePub Date : 2020-08-18DOI: 10.1007/s42521-021-00035-5
Lamprini Zarpala, Fran Casino
{"title":"A blockchain-based forensic model for financial crime investigation: the embezzlement scenario","authors":"Lamprini Zarpala, Fran Casino","doi":"10.1007/s42521-021-00035-5","DOIUrl":"https://doi.org/10.1007/s42521-021-00035-5","url":null,"abstract":"","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"301 - 332"},"PeriodicalIF":0.0,"publicationDate":"2020-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42521-021-00035-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46338719","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}
Digital financePub Date : 2020-07-30DOI: 10.2139/ssrn.3664219
Alla Petukhina, Erin Sprünken
{"title":"Evaluation of multi-asset investment strategies with digital assets","authors":"Alla Petukhina, Erin Sprünken","doi":"10.2139/ssrn.3664219","DOIUrl":"https://doi.org/10.2139/ssrn.3664219","url":null,"abstract":"The drastic growth of the cryptocurrencies market capitalization boosts investigation of their diversification benefits in portfolio construction. In this paper with a set of classical and modern measurement tools, we assess the out-of-sample performance of eight portfolio allocation strategies relative to the naive 1/ N rule applied to traditional and crypto-assets investment universe. Evaluated strategies include a range from classical Markowitz rule to the recently introduced LIBRO approach (Trimborn et al. in Journal of Financial Econometrics 1–27, 2019). Furthermore, we also compare three extensions for strategies with respect to input estimators applied. The results show that in the presence of alternative assets, such as cryptocurrencies, mean–variance strategies underperform the benchmark portfolio. In contrast, CVaR optimization tends to outperform the benchmark as well as geometric optimization, although we find a strong dependence of the former’s success on trading costs. Furthermore, we find evidence that liquidity-bounded strategies tend to perform very well. Thus, our findings underscore the non-normal distribution of returns and the necessity to control for liquidity constraints at alternative asset markets.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"45-79"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44051398","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}
Digital financePub Date : 2020-07-01DOI: 10.2139/ssrn.3639098
F. Woebbeking
{"title":"Cryptocurrency volatility markets","authors":"F. Woebbeking","doi":"10.2139/ssrn.3639098","DOIUrl":"https://doi.org/10.2139/ssrn.3639098","url":null,"abstract":"By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"3 1","pages":"273 - 298"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41566110","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}
Digital financePub Date : 2020-06-26DOI: 10.2139/ssrn.3636278
Georg Keilbar, Yanfen Zhang
{"title":"On cointegration and cryptocurrency dynamics","authors":"Georg Keilbar, Yanfen Zhang","doi":"10.2139/ssrn.3636278","DOIUrl":"https://doi.org/10.2139/ssrn.3636278","url":null,"abstract":"This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.","PeriodicalId":72817,"journal":{"name":"Digital finance","volume":"1 1","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45182536","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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