Special Issue "Computational Finance"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Mathematical Finance".

Deadline for manuscript submissions: 30 August 2019

Special Issue Editor

Guest Editor
Prof. Dr. Lars Stentoft

Department of Economics, University of Western Ontario, Social Science Centre Room 4071, London, Ontario, Canada, N6A 5C2
Website | E-Mail
Interests: finance; financial econometrics; computational finance; econometrics

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the broad topic of “Computational Finance” and includes novel research on the use of computational methods and techniques for modelling financial asset prices, returns, and volatility, and in the pricing, hedging, and risk management of financial instruments.

Theoretical and empirical articles on the application of novel computational techniques in estimation, simulation, optimization, and calibration with applications to asset pricing, derivative valuation, hedging, and risk management are welcome.

Contributions focusing on multivariate or high-dimensional applications in today’s complex world, novel measures of financial risk, and other types of risks implied from derivative markets, and on the use of high-frequency data of all sorts, are encouraged.

Prof. Dr. Lars Stentoft
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • asset pricing models
  • calibration
  • derivatives
  • hedging
  • multivariate models
  • optimization
  • prediction
  • risk management
  • simulation
  • volatility

Published Papers (7 papers)

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Research

Open AccessArticle
Defined Contribution Pension Plans: Who Has Seen the Risk?
J. Risk Financial Manag. 2019, 12(2), 70; https://doi.org/10.3390/jrfm12020070
Received: 20 February 2019 / Revised: 17 April 2019 / Accepted: 18 April 2019 / Published: 24 April 2019
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Abstract
The trend towards eliminating defined benefit (DB) pension plans in favour of defined contribution (DC) plans implies that increasing numbers of pension plan participants will bear the risk that final realized portfolio values may be insufficient to fund desired retirement cash flows. We [...] Read more.
The trend towards eliminating defined benefit (DB) pension plans in favour of defined contribution (DC) plans implies that increasing numbers of pension plan participants will bear the risk that final realized portfolio values may be insufficient to fund desired retirement cash flows. We compare the outcomes of various asset allocation strategies for a typical DC plan investor. The strategies considered include constant proportion, linear glide path, and optimal dynamic (multi-period) time consistent quadratic shortfall approaches. The last of these is based on a double exponential jump diffusion model. We determine the parameters of the model using monthly US data over a 90-year sample period. We carry out tests in a synthetic market which is based on the same jump diffusion model and also using bootstrap resampling of historical data. The probability that portfolio values at retirement will be insufficient to provide adequate retirement incomes is relatively high, unless DC investors adopt optimal allocation strategies and raise typical contribution rates. This suggests there is a looming crisis in DC plans, which requires educating DC plan holders in terms of realistic expectations, required contributions, and optimal asset allocation strategies. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
The Impact of Algorithmic Trading in a Simulated Asset Market
J. Risk Financial Manag. 2019, 12(2), 68; https://doi.org/10.3390/jrfm12020068
Received: 13 March 2019 / Revised: 12 April 2019 / Accepted: 17 April 2019 / Published: 20 April 2019
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Abstract
In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly [...] Read more.
In this work we simulate algorithmic trading (AT) in asset markets to clarify its impact. Our markets consist of human and algorithmic counterparts of traders that trade based on technical and fundamental analysis, and statistical arbitrage strategies. Our specific contributions are: (1) directly analyze AT behavior to connect AT trading strategies to specific outcomes in the market; (2) measure the impact of AT on market quality; and (3) test the sensitivity of our findings to variations in market conditions and possible future events of interest. Examples of such variations and future events are the level of market uncertainty and the degree of algorithmic versus human trading. Our results show that liquidity increases initially as AT rises to about 10% share of the market; beyond this point, liquidity increases only marginally. Statistical arbitrage appears to lead to significant deviation from fundamentals. Our results can facilitate market oversight and provide hypotheses for future empirical work charting the path for developing countries where AT is still at a nascent stage. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
Efficient Numerical Pricing of American Call Options Using Symmetry Arguments
J. Risk Financial Manag. 2019, 12(2), 59; https://doi.org/10.3390/jrfm12020059
Received: 12 March 2019 / Revised: 29 March 2019 / Accepted: 4 April 2019 / Published: 9 April 2019
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Abstract
This paper demonstrates that it is possible to improve significantly on the estimated call prices obtained with the regression and simulation-based least-squares Monte Carlo method by using put-call symmetry. The results show that, for a large sample of options with characteristics of relevance [...] Read more.
This paper demonstrates that it is possible to improve significantly on the estimated call prices obtained with the regression and simulation-based least-squares Monte Carlo method by using put-call symmetry. The results show that, for a large sample of options with characteristics of relevance in real-life applications, the symmetric method performs much better on average than the regular pricing method, is the best method for most of the options, never performs poorly and, as a result, is extremely efficient compared to the optimal, but unfeasible method that picks the method with the smallest Root Mean Squared Error (RMSE). A simple classification method is proposed that, by optimally selecting among estimates from the symmetric method with a reasonably small order used in the polynomial approximation, achieves a relative efficiency of more than 98 % . The relative importance of using the symmetric method increases with option maturity and with asset volatility. Using the symmetric method to price, for example, real options, many of which are call options with long maturities on volatile assets, for example energy, could therefore improve the estimates significantly by decreasing their bias and RMSE by orders of magnitude. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
Positive Liquidity Spillovers from Sovereign Bond-Backed Securities
J. Risk Financial Manag. 2019, 12(2), 58; https://doi.org/10.3390/jrfm12020058
Received: 10 March 2019 / Revised: 2 April 2019 / Accepted: 3 April 2019 / Published: 9 April 2019
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Abstract
This paper contributes to the debate concerning the benefits and disadvantages of introducing a European Sovereign Bond-Backed Securitisation (SBBS) to address the need for a common safe asset that would break destabilising bank-sovereign linkages. The analysis focuses on assessing the effectiveness of hedges [...] Read more.
This paper contributes to the debate concerning the benefits and disadvantages of introducing a European Sovereign Bond-Backed Securitisation (SBBS) to address the need for a common safe asset that would break destabilising bank-sovereign linkages. The analysis focuses on assessing the effectiveness of hedges incurred while making markets in individual euro area sovereign bonds by taking offsetting positions in one or more of the SBBS tranches. Tranche yields are estimated using a simulation approach. This involves the generation of sovereign defaults and allocation of the combined credit risk premium of all the sovereigns, at the end of each day, to the SBBS tranches according to the seniority of claims under the proposed securitisation. Optimal hedging with SBBS is found to reduce risk exposures substantially in normal market conditions. In volatile conditions, hedging is not very effective but leaves dealers exposed to mostly idiosyncratic risks. These remaining risks largely disappear if dealers are diversified in providing liquidity across country-specific secondary markets and SBBS tranches. Hedging each of the long positions in a portfolio of individual sovereigns results in a risk exposure as low as that borne by holding the safest individual sovereign bond (the Bund). Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
Instantaneous Volatility Seasonality of High-Frequency Markets in Directional-Change Intrinsic Time
J. Risk Financial Manag. 2019, 12(2), 54; https://doi.org/10.3390/jrfm12020054
Received: 28 February 2019 / Revised: 24 March 2019 / Accepted: 26 March 2019 / Published: 1 April 2019
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Abstract
We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the [...] Read more.
We propose a novel intraday instantaneous volatility measure which utilises sequences of drawdowns and drawups non-equidistantly spaced in physical time as indicators of high-frequency activity of financial markets. The sequences are re-expressed in terms of directional-change intrinsic time which ticks only when the price curve changes the direction of its trend by a given relative value. We employ the proposed measure to uncover weekly volatility seasonality patterns of three Forex and one Bitcoin exchange rates, as well as a stock market index. We demonstrate the long memory of instantaneous volatility computed in directional-change intrinsic time. The provided volatility estimation method can be adapted as a universal multiscale risk-management tool independent of the discreteness and the type of analysed high-frequency data. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500
J. Risk Financial Manag. 2019, 12(2), 51; https://doi.org/10.3390/jrfm12020051
Received: 27 February 2019 / Revised: 24 March 2019 / Accepted: 26 March 2019 / Published: 1 April 2019
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Abstract
This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump–diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998–December 2015. In particular, the established stock selection and trading framework identifies overnight price gaps based [...] Read more.
This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump–diffusion model and applies it to high-frequency data of the S&P 500 constituents from January 1998–December 2015. In particular, the established stock selection and trading framework identifies overnight price gaps based on an advanced jump test procedure and exploits temporary market anomalies during the first minutes of a trading day. The existence of the assumed mean-reverting property is confirmed by a preliminary analysis of the S&P 500 index; this characteristic is particularly significant 120 min after market opening. In the empirical back-testing study, the strategy delivers statistically- and economically-significant returns of 51.47 percent p.a.and an annualized Sharpe ratio of 2.38 after transaction costs. We benchmarked our trading algorithm against existing quantitative strategies from the same research area and found its performance superior in a multitude of risk-return characteristics. Finally, a deep dive analysis shows that our results are consistently profitable and robust against drawdowns, even in recent years. Full article
(This article belongs to the Special Issue Computational Finance)
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Open AccessArticle
Between ℙ and ℚ: The ℙ Measure for Pricing in Asset Liability Management
J. Risk Financial Manag. 2018, 11(4), 67; https://doi.org/10.3390/jrfm11040067
Received: 17 September 2018 / Revised: 18 October 2018 / Accepted: 21 October 2018 / Published: 24 October 2018
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Abstract
Insurance companies issue guarantees that need to be valued according to the market expectations. By calibrating option pricing models to the available implied volatility surfaces, one deals with the so-called risk-neutral measure Q, which can be used to generate market consistent values [...] Read more.
Insurance companies issue guarantees that need to be valued according to the market expectations. By calibrating option pricing models to the available implied volatility surfaces, one deals with the so-called risk-neutral measure Q , which can be used to generate market consistent values for these guarantees. For asset liability management, insurers also need future values of these guarantees. Next to that, new regulations require insurance companies to value their positions on a one-year horizon. As the option prices at t = 1 are unknown, it is common practice to assume that the parameters of these option pricing models are constant, i.e., the calibrated parameters from time t = 0 are also used to value the guarantees at t = 1 . However, it is well-known that the parameters are not constant and may depend on the state of the market which evolves under the real-world measure P . In this paper, we propose improved regression models that, given a set of market variables such as the VIX index and risk-free interest rates, estimate the calibrated parameters. When the market variables are included in a real-world simulation, one is able to assess the calibrated parameters (and consequently the implied volatility surface) in line with the simulated state of the market. By performing a regression, we are able to predict out-of-sample implied volatility surfaces accurately. Moreover, the impact on the Solvency Capital Requirement has been evaluated for different points in time. The impact depends on the initial state of the market and may vary between −46% and +52%. Full article
(This article belongs to the Special Issue Computational Finance)
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