530 research outputs found
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Special Issue āMachine Learning in Insuranceā
Learning in Insuranceā, which represents a compilation of ten high-quality articles discussing avant-garde developments or introducing new theoretical or practical advances in this field
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Self-selection and risk sharing in a modern world of lifelong annuities - Abstract of the London Discussion
This abstract relates to the following paper: Gerrard, R., Hiabu, M., Kyriakou, I. and Nielsen, J. P. (2018) Self-selection and risk sharing in a modern world of lifelong annuities ā Abstract of the London Discussion. British Actuarial Journal. Cambridge University Press, 23. doi: 10.1017/S135732171800020X
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Self-selection and risk sharing in a modern world of life-long annuities
Communicating a pension product well is as important as optimising the financial value. In a recent study, we showed that up to 80% of the value of a pension lump sum could be lost if customer communication failed. In this paper, we extend the simple customer interaction of the earlier contribution to the more challenging lifetime annuity case. Using a simple mobile phone device, the pension customer can select the life-long optimal investment strategy within minutes. The financial risk trade-off is presented as a trade-off between the pension paid and the number of years the life-long annuity is guaranteed. The pension payment decreases when investment security increases. The necessary underlying mathematical financial hedging theory is included in the stud
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Forecasting benchmarks of long-term stock returns via machine learning
Recent advances in pension product development seem to favour alternatives to the risk free asset often used in the financial theory as a performance standard for measuring the value generated by an investment or a reference point for determining the value of a financial instrument. To this end, in this paper, we apply the simplest machine learning technique, namely, a fully nonparametric smoother with the covariates and the smoothing parameter chosen by cross-validation to forecast stock returns in excess of different benchmarks, including the short-term interest rate, long-term interest rate, earnings-by-price ratio, and the inflation. We find that, net-of-inflation, the combined earnings-by-price and long-short rate spread form our best-performing two-dimensional set of predictors for future annual stock returns. This is a crucial conclusion for actuarial applications that aim to provide real-income forecasts for pensioners
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Longer-Term Forecasting of Excess Stock ReturnsāThe Five-Year Case
Long-term return expectations or predictions play an important role in planning purposes and guidance of long-term investors. Five-year stock returns are less volatile around their geometric mean than returns of higher frequency, such as one-year returns. One would, therefore, expect models using the latter to better reduce the noise and beat the simple historical mean than models based on the former. However, this paper shows that the general tendency is surprisingly the opposite: long-term forecasts over five years have a similar or even better predictive power when compared to the one-year case. We consider a long list of economic predictors and benchmarks relevant for the long-term investor. Our predictive approach consists of adopting and implementing a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. We consistently find that long-term forecasting performs well and recommend drawing more attention to it when designing investment strategies for long-term investors. Furthermore, our preferred predictive model did stand the test of Covid-19 providing a relatively optimistic outlook in March 2020 when uncertainty was all around us with lockdown and facing an unknown new pandemic
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Herd behavior in the drybulk market: An empirical analysis of the decision to invest in new and retire existing fleet capacity
We examine whether investors herd in their decision to order or scrap vessels in the drybulk market. We decompose herding into unintentional and intentional, and test for herd behavior under asymmetric effects with respect to freight market states, cycle phases, risk-return and valuation profiles, and ownership of the vessel. We detect unintentional herd behavior during down freight markets and contractions. Furthermore, we find evidence of spill-over unintentional herding effects from the newbuilding to the scrap market. Finally, asymmetric herd effects are evident between traditional and liberal philosophy towards the ownership of the vessel, and during extreme risk-return and valuation periods
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Freight Derivatives Pricing for Decoupled Mean-Reverting Diffusion and Jumps
We develop an accurate valuation setup for freight options, featuring an exponential meanreverting model for the freight rate with distinct reversion scales for its jump and diffusion components. We calibrate to Baltic option prices and analyze the freight rate dynamics. More specifically, we observe that jumps dissipate faster than the diffusive deviations about the equilibrium level. We benchmark against practitionersā model of choice, i.e., the lognormal model and variants, and find that our approach reduces the pricing error while preserving analytical tractability and computational competence. We also find that neglecting fast mean-reverting jumps leads to nontrivial option mispricings
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Shipping equity risk behavior and portfolio management
This paper investigates the dynamics of stock price volatility for different vessel-type segments of the U. S, water transportation industry . We measure market exposure by a portfolio of tanker, dry bulk, container, and gas stocks to examine tail behavior and tail risk dependence. The role of mixture distributions in predicting future volatility is studied from both statistical and economic perspectives. We further test for predictability in co-movements in the tails of sectors returns . Findings indicate that large losses are strongly correlated, supporting asymmetric transmission processes for financial contagion. Finally, using a non-parametric approach, we extend the model to the multivariate case and assess the value of volatility and correlation timing in optimal portfolio selection. The results can help to improve the understanding of time-varying volatility, correlation and tail systemic risk of shipping stock markets, and consequently, have implications for risk management and asset allocation practices, as well as regulatory policies
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Freight options: Price modelling and empirical analysis
This paper discusses an extension of the traditional lognormal representation for the risk neutral spot freight rate dynamics to a diffusion model overlaid with jumps of random magnitude and arrival. Then, we develop a valuation framework for options on the average spot freight rate, which are commonly traded in the freight derivatives market. By exploiting the computational efficiency of the proposed pricing scheme, we calibrate the jump diffusion model using market quotes of options on the trip-charter route average Baltic Capesize, Panamax and Supramax Indices. We show that the jump-extended setting yields important model improvements over the basic lognormal setting
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Affine-Structure Models and the Pricing of Energy Commodity Derivatives
We consider a seasonal mean-reverting model for energy commodity prices with jumps and Heston-type stochastic volatility, and three nested models for comparison. By exploiting the affine form of the log-spot models, we develop a general valuation framework for futures and discrete arithmetic Asian options. We investigate five major petroleum commodities from Europe (Brent crude oil, gasoil) and US (light sweet crude oil, gasoline, heating oil) and analyse the effects of the competing fitted spot models in futures pricing, Asian options pricing and hedging. We find evidence that price jumps and stochastic volatility are important features of the petroleum price dynamics
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